From acef95f4e4c18fc38ff7f14303d61c83f2ae96dc Mon Sep 17 00:00:00 2001 From: github-actions Date: Wed, 29 May 2024 18:43:38 +0000 Subject: [PATCH] update catalog --- .../Daily_Bloom_binary/collection.json | 40 ++-- .../models/asl.auto.arima.json | 13 +- .../Daily_Bloom_binary/models/asl.ets.json | 13 +- .../Daily_Bloom_binary/models/asl.met.lm.json | 11 +- .../models/asl.met.lm.step.json | 11 +- .../Daily_Bloom_binary/models/asl.tbats.json | 13 +- .../models/asl.temp.lm.json | 11 +- .../models/climatology.json | 13 +- .../Daily_Bloom_binary/models/fableARIMA.json | 13 +- .../Daily_Bloom_binary/models/fableETS.json | 13 +- .../models/fableNNETAR.json | 13 +- .../Daily_Bloom_binary/models/glm_aed_v1.json | 13 +- .../models/historic_mean.json | 13 +- .../models/monthly_mean.json | 13 +- .../models/persistenceRW.json | 13 +- .../Daily_Chlorophyll-a/collection.json | 44 ++-- .../models/asl.auto.arima.json | 13 +- .../Daily_Chlorophyll-a/models/asl.ets.json | 13 +- .../models/asl.met.lm.json | 11 +- .../models/asl.met.lm.step.json | 11 +- .../Daily_Chlorophyll-a/models/asl.tbats.json | 13 +- .../models/asl.temp.lm.json | 11 +- .../models/climatology.json | 13 +- .../models/climatology2.json | 11 +- .../models/fableARIMA.json | 13 +- .../Daily_Chlorophyll-a/models/fableETS.json | 13 +- .../models/fableNNETAR.json | 13 +- .../models/gfs_seamless.json | 11 +- .../models/glm_aed_v1.json | 13 +- .../models/historic_mean.json | 13 +- .../models/monthly_mean.json | 13 +- .../models/persistenceRW.json | 13 +- .../collection.json | 12 +- .../models/asl.auto.arima.json | 168 +++++++++++++++ .../models/asl.ets.json | 168 +++++++++++++++ .../models/asl.met.lm.json | 168 +++++++++++++++ .../models/asl.met.lm.step.json | 168 +++++++++++++++ .../models/asl.tbats.json | 168 +++++++++++++++ .../models/asl.temp.lm.json | 168 +++++++++++++++ .../models/climatology.json | 168 +++++++++++++++ .../models/fableNNETAR.json | 168 +++++++++++++++ .../models/fableNNETAR_focal.json | 168 +++++++++++++++ .../models/gfs_seamless.json | 168 +++++++++++++++ .../models/glm_aed_v1.json | 167 +++++++++++++++ .../models/historic_mean.json | 168 +++++++++++++++ .../models/monthly_mean.json | 168 +++++++++++++++ .../models/persistenceRW.json | 168 +++++++++++++++ .../Physical/Daily_Secchi/collection.json | 12 +- .../Daily_Secchi/models/asl.auto.arima.json | 168 +++++++++++++++ .../Physical/Daily_Secchi/models/asl.ets.json | 168 +++++++++++++++ .../Daily_Secchi/models/asl.met.lm.json | 168 +++++++++++++++ .../Daily_Secchi/models/asl.met.lm.step.json | 168 +++++++++++++++ .../Daily_Secchi/models/asl.tbats.json | 168 +++++++++++++++ .../Daily_Secchi/models/asl.temp.lm.json | 168 +++++++++++++++ .../Daily_Secchi/models/climatology.json | 168 +++++++++++++++ .../Daily_Secchi/models/fableNNETAR.json | 168 +++++++++++++++ .../models/fableNNETAR_focal.json | 168 +++++++++++++++ .../Daily_Secchi/models/gfs_seamless.json | 168 +++++++++++++++ .../Daily_Secchi/models/glm_aed_v1.json | 167 +++++++++++++++ .../Daily_Secchi/models/historic_mean.json | 168 +++++++++++++++ .../Daily_Secchi/models/monthly_mean.json | 168 +++++++++++++++ .../Daily_Secchi/models/persistenceRW.json | 168 +++++++++++++++ .../models/secchi_example_forecast.json | 167 +++++++++++++++ .../models/secchi_last3obs_mean.json | 167 +++++++++++++++ .../Daily_Water_temperature/collection.json | 12 +- .../models/TESTclimatology.json | 168 +++++++++++++++ .../models/TempC_mean_example_forecast.json | 167 +++++++++++++++ .../models/Temp_C_mean.json | 167 +++++++++++++++ .../models/asl.auto.arima.json | 168 +++++++++++++++ .../models/asl.ets.json | 168 +++++++++++++++ .../models/asl.met.lm.json | 168 +++++++++++++++ .../models/asl.met.lm.step.json | 168 +++++++++++++++ .../models/asl.tbats.json | 168 +++++++++++++++ .../models/asl.temp.lm.json | 168 +++++++++++++++ .../models/climatology.json | 169 +++++++++++++++ .../models/climatology2.json | 168 +++++++++++++++ .../models/example_ID.json | 167 +++++++++++++++ .../models/example_forecast.json | 167 +++++++++++++++ .../models/fableNNETAR.json | 168 +++++++++++++++ .../models/fableNNETAR_focal.json | 168 +++++++++++++++ .../models/flareGOTM.json | 167 +++++++++++++++ .../models/flareSimstrat.json | 167 +++++++++++++++ .../models/gfs_seamless.json | 168 +++++++++++++++ .../models/glm_aed_v1.json | 167 +++++++++++++++ .../models/historic_mean.json | 169 +++++++++++++++ .../models/inflow_gefsClimAED.json | 167 +++++++++++++++ .../models/monthly_mean.json | 169 +++++++++++++++ .../models/persistenceFO.json | 167 +++++++++++++++ .../models/persistenceRW.json | 169 +++++++++++++++ .../Hourly_Water_temperature/collection.json | 10 +- catalog/forecasts/collection.json | 12 +- catalog/inventory/collection.json | 12 +- catalog/noaa_forecasts/Pseudo/collection.json | 12 +- .../Stage1-stats/collection.json | 12 +- catalog/noaa_forecasts/Stage1/collection.json | 12 +- catalog/noaa_forecasts/Stage2/collection.json | 12 +- catalog/noaa_forecasts/Stage3/collection.json | 12 +- catalog/noaa_forecasts/collection.json | 12 +- .../Daily_Bloom_binary/collection.json | 40 ++-- .../Daily_Chlorophyll-a/collection.json | 44 ++-- catalog/scores/Biological/collection.json | 12 +- .../collection.json | 40 ++-- catalog/scores/Chemical/collection.json | 12 +- .../Physical/Daily_Secchi/collection.json | 42 ++-- .../Daily_Water_temperature/collection.json | 60 +++--- .../Hourly_Water_temperature/collection.json | 10 +- catalog/scores/Physical/collection.json | 12 +- catalog/scores/collection.json | 12 +- .../models/model_items/Flow_cms_mean.json | 38 ++-- .../models/model_items/TESTclimatology.json | 39 ++-- .../TempC_mean_example_forecast.json | 38 ++-- .../models/model_items/Temp_C_mean.json | 38 ++-- .../models/model_items/asl.auto.arima.json | 68 +------ .../scores/models/model_items/asl.ets.json | 68 +------ .../scores/models/model_items/asl.met.lm.json | 68 +------ .../models/model_items/asl.met.lm.step.json | 68 +------ .../scores/models/model_items/asl.tbats.json | 68 +------ .../models/model_items/asl.temp.lm.json | 68 +------ catalog/scores/models/model_items/cfs.json | 62 ++---- .../models/model_items/climatology.json | 80 ++------ .../models/model_items/climatology2.json | 42 ++-- .../models/model_items/ecmwf_ifs04.json | 62 ++---- .../scores/models/model_items/example_ID.json | 38 ++-- .../models/model_items/example_forecast.json | 38 ++-- .../scores/models/model_items/fableARIMA.json | 42 ++-- .../scores/models/model_items/fableETS.json | 42 ++-- .../models/model_items/fableNNETAR.json | 56 +---- .../models/model_items/fableNNETAR_focal.json | 40 +--- .../scores/models/model_items/flareGOTM.json | 40 ++-- .../models/model_items/flareSimstrat.json | 40 ++-- .../scores/models/model_items/gem_global.json | 69 ++----- .../models/model_items/gfs_seamless.json | 109 ++-------- .../scores/models/model_items/glm_aed_v1.json | 82 +------- .../models/model_items/historic_mean.json | 78 +------ .../models/model_items/icon_seamless.json | 69 ++----- .../model_items/inflow_gefsClimAED.json | 97 ++------- .../models/model_items/monthly_mean.json | 78 +------ .../models/model_items/persistenceFO.json | 40 ++-- .../models/model_items/persistenceRW.json | 94 ++------- .../model_items/secchi_example_forecast.json | 38 ++-- .../model_items/secchi_last3obs_mean.json | 40 ++-- .../Daily_Bloom_binary/collection.json | 40 ++-- .../Daily_Chlorophyll-a/collection.json | 44 ++-- catalog/summaries/Biological/collection.json | 12 +- .../collection.json | 40 ++-- catalog/summaries/Chemical/collection.json | 12 +- .../Physical/Daily_Secchi/collection.json | 44 ++-- .../Daily_Water_temperature/collection.json | 60 +++--- .../Hourly_Water_temperature/collection.json | 10 +- catalog/summaries/Physical/collection.json | 12 +- catalog/summaries/collection.json | 12 +- .../models/model_items/Flow_cms_mean.json | 38 ++-- .../models/model_items/TESTclimatology.json | 39 ++-- .../TempC_mean_example_forecast.json | 38 ++-- .../models/model_items/Temp_C_mean.json | 38 ++-- .../models/model_items/asl.auto.arima.json | 75 +------ .../summaries/models/model_items/asl.ets.json | 75 +------ .../models/model_items/asl.met.lm.json | 75 +------ .../models/model_items/asl.met.lm.step.json | 73 +------ .../models/model_items/asl.tbats.json | 75 +------ .../models/model_items/asl.temp.lm.json | 75 +------ catalog/summaries/models/model_items/cfs.json | 62 ++---- .../models/model_items/climatology.json | 87 ++------ .../models/model_items/climatology2.json | 42 ++-- .../models/model_items/ecmwf_ifs04.json | 62 ++---- .../models/model_items/example_ID.json | 38 ++-- .../models/model_items/example_forecast.json | 38 ++-- .../models/model_items/fableARIMA.json | 42 ++-- .../models/model_items/fableETS.json | 42 ++-- .../models/model_items/fableNNETAR.json | 56 +---- .../models/model_items/fableNNETAR_focal.json | 40 +--- .../models/model_items/flareGOTM.json | 41 ++-- .../models/model_items/flareSimstrat.json | 41 ++-- .../models/model_items/gem_global.json | 69 ++----- .../models/model_items/gfs_seamless.json | 109 ++-------- .../models/model_items/glm_aed_v1.json | 89 +------- .../models/model_items/historic_mean.json | 83 +------- .../models/model_items/icon_seamless.json | 69 ++----- .../model_items/inflow_gefsClimAED.json | 104 ++-------- .../models/model_items/monthly_mean.json | 83 +------- .../models/model_items/persistenceFO.json | 40 ++-- .../models/model_items/persistenceRW.json | 192 ++---------------- .../model_items/secchi_example_forecast.json | 38 ++-- .../model_items/secchi_last3obs_mean.json | 40 ++-- catalog/targets/collection.json | 2 +- 185 files changed, 10729 insertions(+), 3551 deletions(-) create mode 100644 catalog/forecasts/Chemical/Daily_oxygen_concentration/models/asl.auto.arima.json create mode 100644 catalog/forecasts/Chemical/Daily_oxygen_concentration/models/asl.ets.json create mode 100644 catalog/forecasts/Chemical/Daily_oxygen_concentration/models/asl.met.lm.json create mode 100644 catalog/forecasts/Chemical/Daily_oxygen_concentration/models/asl.met.lm.step.json create mode 100644 catalog/forecasts/Chemical/Daily_oxygen_concentration/models/asl.tbats.json create mode 100644 catalog/forecasts/Chemical/Daily_oxygen_concentration/models/asl.temp.lm.json create mode 100644 catalog/forecasts/Chemical/Daily_oxygen_concentration/models/climatology.json create mode 100644 catalog/forecasts/Chemical/Daily_oxygen_concentration/models/fableNNETAR.json create mode 100644 catalog/forecasts/Chemical/Daily_oxygen_concentration/models/fableNNETAR_focal.json create mode 100644 catalog/forecasts/Chemical/Daily_oxygen_concentration/models/gfs_seamless.json create mode 100644 catalog/forecasts/Chemical/Daily_oxygen_concentration/models/glm_aed_v1.json create mode 100644 catalog/forecasts/Chemical/Daily_oxygen_concentration/models/historic_mean.json create mode 100644 catalog/forecasts/Chemical/Daily_oxygen_concentration/models/monthly_mean.json create mode 100644 catalog/forecasts/Chemical/Daily_oxygen_concentration/models/persistenceRW.json create mode 100644 catalog/forecasts/Physical/Daily_Secchi/models/asl.auto.arima.json create mode 100644 catalog/forecasts/Physical/Daily_Secchi/models/asl.ets.json create mode 100644 catalog/forecasts/Physical/Daily_Secchi/models/asl.met.lm.json create mode 100644 catalog/forecasts/Physical/Daily_Secchi/models/asl.met.lm.step.json create mode 100644 catalog/forecasts/Physical/Daily_Secchi/models/asl.tbats.json create mode 100644 catalog/forecasts/Physical/Daily_Secchi/models/asl.temp.lm.json create mode 100644 catalog/forecasts/Physical/Daily_Secchi/models/climatology.json create mode 100644 catalog/forecasts/Physical/Daily_Secchi/models/fableNNETAR.json create mode 100644 catalog/forecasts/Physical/Daily_Secchi/models/fableNNETAR_focal.json create mode 100644 catalog/forecasts/Physical/Daily_Secchi/models/gfs_seamless.json create mode 100644 catalog/forecasts/Physical/Daily_Secchi/models/glm_aed_v1.json create mode 100644 catalog/forecasts/Physical/Daily_Secchi/models/historic_mean.json create mode 100644 catalog/forecasts/Physical/Daily_Secchi/models/monthly_mean.json create mode 100644 catalog/forecasts/Physical/Daily_Secchi/models/persistenceRW.json create mode 100644 catalog/forecasts/Physical/Daily_Secchi/models/secchi_example_forecast.json create mode 100644 catalog/forecasts/Physical/Daily_Secchi/models/secchi_last3obs_mean.json create mode 100644 catalog/forecasts/Physical/Daily_Water_temperature/models/TESTclimatology.json create mode 100644 catalog/forecasts/Physical/Daily_Water_temperature/models/TempC_mean_example_forecast.json create mode 100644 catalog/forecasts/Physical/Daily_Water_temperature/models/Temp_C_mean.json create mode 100644 catalog/forecasts/Physical/Daily_Water_temperature/models/asl.auto.arima.json create mode 100644 catalog/forecasts/Physical/Daily_Water_temperature/models/asl.ets.json create mode 100644 catalog/forecasts/Physical/Daily_Water_temperature/models/asl.met.lm.json create mode 100644 catalog/forecasts/Physical/Daily_Water_temperature/models/asl.met.lm.step.json create mode 100644 catalog/forecasts/Physical/Daily_Water_temperature/models/asl.tbats.json create mode 100644 catalog/forecasts/Physical/Daily_Water_temperature/models/asl.temp.lm.json create mode 100644 catalog/forecasts/Physical/Daily_Water_temperature/models/climatology.json create mode 100644 catalog/forecasts/Physical/Daily_Water_temperature/models/climatology2.json create mode 100644 catalog/forecasts/Physical/Daily_Water_temperature/models/example_ID.json create mode 100644 catalog/forecasts/Physical/Daily_Water_temperature/models/example_forecast.json create mode 100644 catalog/forecasts/Physical/Daily_Water_temperature/models/fableNNETAR.json create mode 100644 catalog/forecasts/Physical/Daily_Water_temperature/models/fableNNETAR_focal.json create mode 100644 catalog/forecasts/Physical/Daily_Water_temperature/models/flareGOTM.json create mode 100644 catalog/forecasts/Physical/Daily_Water_temperature/models/flareSimstrat.json create mode 100644 catalog/forecasts/Physical/Daily_Water_temperature/models/gfs_seamless.json create mode 100644 catalog/forecasts/Physical/Daily_Water_temperature/models/glm_aed_v1.json create mode 100644 catalog/forecasts/Physical/Daily_Water_temperature/models/historic_mean.json create mode 100644 catalog/forecasts/Physical/Daily_Water_temperature/models/inflow_gefsClimAED.json create mode 100644 catalog/forecasts/Physical/Daily_Water_temperature/models/monthly_mean.json create mode 100644 catalog/forecasts/Physical/Daily_Water_temperature/models/persistenceFO.json create mode 100644 catalog/forecasts/Physical/Daily_Water_temperature/models/persistenceRW.json diff --git a/catalog/forecasts/Biological/Daily_Bloom_binary/collection.json b/catalog/forecasts/Biological/Daily_Bloom_binary/collection.json index f7e79472cf..a6e927d28a 100644 --- a/catalog/forecasts/Biological/Daily_Bloom_binary/collection.json +++ b/catalog/forecasts/Biological/Daily_Bloom_binary/collection.json @@ -15,72 +15,72 @@ { "rel": "item", "type": "application/json", - "href": "./models/fableARIMA.json" + "href": "../../models/model_items/fableARIMA.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableETS.json" + "href": "../../models/model_items/fableETS.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR.json" + "href": "../../models/model_items/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "./models/glm_aed_v1.json" + "href": "../../models/model_items/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "../../models/model_items/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/historic_mean.json" + "href": "../../models/model_items/historic_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/monthly_mean.json" + "href": "../../models/model_items/monthly_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "../../models/model_items/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.auto.arima.json" + "href": "../../models/model_items/asl.auto.arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.ets.json" + "href": "../../models/model_items/asl.ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.tbats.json" + "href": "../../models/model_items/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.json" + "href": "../../models/model_items/asl.met.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.step.json" + "href": "../../models/model_items/asl.met.lm.step.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.temp.lm.json" + "href": "../../models/model_items/asl.temp.lm.json" }, { "rel": "parent", @@ -125,12 +125,17 @@ "interval": [ [ "2023-10-01T00:00:00Z", - "2024-07-02T00:00:00Z" + "2024-07-03T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -190,11 +195,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ], "assets": { diff --git a/catalog/forecasts/Biological/Daily_Bloom_binary/models/asl.auto.arima.json b/catalog/forecasts/Biological/Daily_Bloom_binary/models/asl.auto.arima.json index e733fe6239..ec28162662 100644 --- a/catalog/forecasts/Biological/Daily_Bloom_binary/models/asl.auto.arima.json +++ b/catalog/forecasts/Biological/Daily_Bloom_binary/models/asl.auto.arima.json @@ -16,9 +16,10 @@ ] }, "properties": { + "title": "asl.auto.arima", "description": "\nmodel info: forecast::auto.arima() function in R, fit individually at each site/depth\n\nSites: bvre, fcre\n\nVariables: Daily Bloom_binary", "start_datetime": "2024-04-29", - "end_datetime": "2024-07-01", + "end_datetime": "2024-07-02", "providers": [ { "url": "pending", @@ -44,6 +45,11 @@ "Daily Bloom_binary" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -103,11 +109,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Bloom_binary/models/asl.ets.json b/catalog/forecasts/Biological/Daily_Bloom_binary/models/asl.ets.json index 9f780762af..b90f0d275a 100644 --- a/catalog/forecasts/Biological/Daily_Bloom_binary/models/asl.ets.json +++ b/catalog/forecasts/Biological/Daily_Bloom_binary/models/asl.ets.json @@ -16,9 +16,10 @@ ] }, "properties": { + "title": "asl.ets", "description": "\nmodel info: forecast::ets() function in R, fit individually at each site/depth\n\nSites: bvre, fcre\n\nVariables: Daily Bloom_binary", "start_datetime": "2024-04-29", - "end_datetime": "2024-07-01", + "end_datetime": "2024-07-02", "providers": [ { "url": "pending", @@ -44,6 +45,11 @@ "Daily Bloom_binary" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -103,11 +109,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Bloom_binary/models/asl.met.lm.json b/catalog/forecasts/Biological/Daily_Bloom_binary/models/asl.met.lm.json index 735e6040b3..5bce1f65aa 100644 --- a/catalog/forecasts/Biological/Daily_Bloom_binary/models/asl.met.lm.json +++ b/catalog/forecasts/Biological/Daily_Bloom_binary/models/asl.met.lm.json @@ -16,6 +16,7 @@ ] }, "properties": { + "title": "asl.met.lm", "description": "\nmodel info: Linear regression with air temp, humidity, precip, and wind speed\n\nSites: bvre, fcre\n\nVariables: Daily Bloom_binary", "start_datetime": "2024-05-14", "end_datetime": "2024-06-27", @@ -44,6 +45,11 @@ "Daily Bloom_binary" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -103,11 +109,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Bloom_binary/models/asl.met.lm.step.json b/catalog/forecasts/Biological/Daily_Bloom_binary/models/asl.met.lm.step.json index 3663d9d577..b140bf17a2 100644 --- a/catalog/forecasts/Biological/Daily_Bloom_binary/models/asl.met.lm.step.json +++ b/catalog/forecasts/Biological/Daily_Bloom_binary/models/asl.met.lm.step.json @@ -16,6 +16,7 @@ ] }, "properties": { + "title": "asl.met.lm.step", "description": "\nmodel info: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC\n\nSites: bvre, fcre\n\nVariables: Daily Bloom_binary", "start_datetime": "2024-05-14", "end_datetime": "2024-06-19", @@ -44,6 +45,11 @@ "Daily Bloom_binary" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -103,11 +109,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Bloom_binary/models/asl.tbats.json b/catalog/forecasts/Biological/Daily_Bloom_binary/models/asl.tbats.json index 845e28af66..fcb4394fed 100644 --- a/catalog/forecasts/Biological/Daily_Bloom_binary/models/asl.tbats.json +++ b/catalog/forecasts/Biological/Daily_Bloom_binary/models/asl.tbats.json @@ -16,9 +16,10 @@ ] }, "properties": { + "title": "asl.tbats", "description": "\nmodel info: forecast::tbats() function in R, fit individually at each site/depth\n\nSites: bvre, fcre\n\nVariables: Daily Bloom_binary", "start_datetime": "2024-04-29", - "end_datetime": "2024-07-01", + "end_datetime": "2024-07-02", "providers": [ { "url": "pending", @@ -44,6 +45,11 @@ "Daily Bloom_binary" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -103,11 +109,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Bloom_binary/models/asl.temp.lm.json b/catalog/forecasts/Biological/Daily_Bloom_binary/models/asl.temp.lm.json index 3ef889a749..5cada64503 100644 --- a/catalog/forecasts/Biological/Daily_Bloom_binary/models/asl.temp.lm.json +++ b/catalog/forecasts/Biological/Daily_Bloom_binary/models/asl.temp.lm.json @@ -16,6 +16,7 @@ ] }, "properties": { + "title": "asl.temp.lm", "description": "\nmodel info: Linear regression with air temperature\n\nSites: bvre, fcre\n\nVariables: Daily Bloom_binary", "start_datetime": "2024-05-14", "end_datetime": "2024-06-27", @@ -44,6 +45,11 @@ "Daily Bloom_binary" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -103,11 +109,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Bloom_binary/models/climatology.json b/catalog/forecasts/Biological/Daily_Bloom_binary/models/climatology.json index 095bbdb6e1..17783cc68a 100644 --- a/catalog/forecasts/Biological/Daily_Bloom_binary/models/climatology.json +++ b/catalog/forecasts/Biological/Daily_Bloom_binary/models/climatology.json @@ -16,9 +16,10 @@ ] }, "properties": { + "title": "climatology", "description": "\nmodel info: Historical DOY mean and sd. Assumes normal distribution\n\nSites: fcre, bvre\n\nVariables: Daily Bloom_binary", "start_datetime": "2024-02-09", - "end_datetime": "2024-07-02", + "end_datetime": "2024-07-03", "providers": [ { "url": "pending", @@ -44,6 +45,11 @@ "Daily Bloom_binary" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -103,11 +109,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Bloom_binary/models/fableARIMA.json b/catalog/forecasts/Biological/Daily_Bloom_binary/models/fableARIMA.json index 24147d30ea..daaf22a905 100644 --- a/catalog/forecasts/Biological/Daily_Bloom_binary/models/fableARIMA.json +++ b/catalog/forecasts/Biological/Daily_Bloom_binary/models/fableARIMA.json @@ -16,9 +16,10 @@ ] }, "properties": { + "title": "fableARIMA", "description": "\nmodel info: ARIMA fit using the ARIMA() function in the fable R package\n\nSites: bvre, fcre\n\nVariables: Daily Bloom_binary", "start_datetime": "2023-10-01", - "end_datetime": "2024-07-01", + "end_datetime": "2024-07-02", "providers": [ { "url": "pending", @@ -44,6 +45,11 @@ "Daily Bloom_binary" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -103,11 +109,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Bloom_binary/models/fableETS.json b/catalog/forecasts/Biological/Daily_Bloom_binary/models/fableETS.json index a07b70beef..82ababd9bb 100644 --- a/catalog/forecasts/Biological/Daily_Bloom_binary/models/fableETS.json +++ b/catalog/forecasts/Biological/Daily_Bloom_binary/models/fableETS.json @@ -16,9 +16,10 @@ ] }, "properties": { + "title": "fableETS", "description": "\nmodel info: fable package exponential smoothing model fable::ETS()\n\nSites: bvre, fcre\n\nVariables: Daily Bloom_binary", "start_datetime": "2023-10-01", - "end_datetime": "2024-07-01", + "end_datetime": "2024-07-02", "providers": [ { "url": "pending", @@ -44,6 +45,11 @@ "Daily Bloom_binary" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -103,11 +109,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Bloom_binary/models/fableNNETAR.json b/catalog/forecasts/Biological/Daily_Bloom_binary/models/fableNNETAR.json index d044f39536..ea34592690 100644 --- a/catalog/forecasts/Biological/Daily_Bloom_binary/models/fableNNETAR.json +++ b/catalog/forecasts/Biological/Daily_Bloom_binary/models/fableNNETAR.json @@ -16,9 +16,10 @@ ] }, "properties": { + "title": "fableNNETAR", "description": "\nmodel info: autoregressive neural net fit using the NNETAR() function in the fable R package\n\nSites: bvre, fcre\n\nVariables: Daily Bloom_binary", "start_datetime": "2023-10-01", - "end_datetime": "2024-07-01", + "end_datetime": "2024-07-02", "providers": [ { "url": "pending", @@ -44,6 +45,11 @@ "Daily Bloom_binary" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -103,11 +109,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Bloom_binary/models/glm_aed_v1.json b/catalog/forecasts/Biological/Daily_Bloom_binary/models/glm_aed_v1.json index 8cac05323b..e13fa3d16d 100644 --- a/catalog/forecasts/Biological/Daily_Bloom_binary/models/glm_aed_v1.json +++ b/catalog/forecasts/Biological/Daily_Bloom_binary/models/glm_aed_v1.json @@ -15,9 +15,10 @@ ] }, "properties": { + "title": "glm_aed_v1", "description": "\nmodel info: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters.\n\nSites: fcre\n\nVariables: Daily Bloom_binary", "start_datetime": "2023-10-20", - "end_datetime": "2024-06-29", + "end_datetime": "2024-06-30", "providers": [ { "url": "pending", @@ -43,6 +44,11 @@ "Daily Bloom_binary" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -102,11 +108,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Bloom_binary/models/historic_mean.json b/catalog/forecasts/Biological/Daily_Bloom_binary/models/historic_mean.json index 5c7c560e3b..e6bbad1f60 100644 --- a/catalog/forecasts/Biological/Daily_Bloom_binary/models/historic_mean.json +++ b/catalog/forecasts/Biological/Daily_Bloom_binary/models/historic_mean.json @@ -16,9 +16,10 @@ ] }, "properties": { + "title": "historic_mean", "description": "\nmodel info: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model.\n\nSites: fcre, bvre\n\nVariables: Daily Bloom_binary", "start_datetime": "2024-02-09", - "end_datetime": "2024-06-30", + "end_datetime": "2024-07-01", "providers": [ { "url": "pending", @@ -44,6 +45,11 @@ "Daily Bloom_binary" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -103,11 +109,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Bloom_binary/models/monthly_mean.json b/catalog/forecasts/Biological/Daily_Bloom_binary/models/monthly_mean.json index 81653b815d..bf757f9e1f 100644 --- a/catalog/forecasts/Biological/Daily_Bloom_binary/models/monthly_mean.json +++ b/catalog/forecasts/Biological/Daily_Bloom_binary/models/monthly_mean.json @@ -16,9 +16,10 @@ ] }, "properties": { + "title": "monthly_mean", "description": "\nmodel info: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast.\n\nSites: fcre, bvre\n\nVariables: Daily Bloom_binary", "start_datetime": "2024-02-09", - "end_datetime": "2024-07-02", + "end_datetime": "2024-07-03", "providers": [ { "url": "pending", @@ -44,6 +45,11 @@ "Daily Bloom_binary" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -103,11 +109,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Bloom_binary/models/persistenceRW.json b/catalog/forecasts/Biological/Daily_Bloom_binary/models/persistenceRW.json index 69b38acccd..dadff6de12 100644 --- a/catalog/forecasts/Biological/Daily_Bloom_binary/models/persistenceRW.json +++ b/catalog/forecasts/Biological/Daily_Bloom_binary/models/persistenceRW.json @@ -16,9 +16,10 @@ ] }, "properties": { + "title": "persistenceRW", "description": "\nmodel info: Random walk from the fable package with ensembles used to represent uncertainty\n\nSites: fcre, bvre\n\nVariables: Daily Bloom_binary", "start_datetime": "2024-02-09", - "end_datetime": "2024-06-30", + "end_datetime": "2024-07-01", "providers": [ { "url": "pending", @@ -44,6 +45,11 @@ "Daily Bloom_binary" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -103,11 +109,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Chlorophyll-a/collection.json b/catalog/forecasts/Biological/Daily_Chlorophyll-a/collection.json index e16689d1bf..d7e6bb126a 100644 --- a/catalog/forecasts/Biological/Daily_Chlorophyll-a/collection.json +++ b/catalog/forecasts/Biological/Daily_Chlorophyll-a/collection.json @@ -15,82 +15,82 @@ { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "../../models/model_items/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableARIMA.json" + "href": "../../models/model_items/fableARIMA.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableETS.json" + "href": "../../models/model_items/fableETS.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR.json" + "href": "../../models/model_items/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "./models/glm_aed_v1.json" + "href": "../../models/model_items/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "../../models/model_items/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology2.json" + "href": "../../models/model_items/climatology2.json" }, { "rel": "item", "type": "application/json", - "href": "./models/historic_mean.json" + "href": "../../models/model_items/historic_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/monthly_mean.json" + "href": "../../models/model_items/monthly_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.auto.arima.json" + "href": "../../models/model_items/asl.auto.arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.ets.json" + "href": "../../models/model_items/asl.ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.tbats.json" + "href": "../../models/model_items/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/gfs_seamless.json" + "href": "../../models/model_items/gfs_seamless.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.json" + "href": "../../models/model_items/asl.met.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.step.json" + "href": "../../models/model_items/asl.met.lm.step.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.temp.lm.json" + "href": "../../models/model_items/asl.temp.lm.json" }, { "rel": "parent", @@ -135,12 +135,17 @@ "interval": [ [ "2023-10-01T00:00:00Z", - "2024-07-02T00:00:00Z" + "2024-07-03T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -200,11 +205,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ], "assets": { diff --git a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/asl.auto.arima.json b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/asl.auto.arima.json index bd3b4c01eb..fac3b0e8f5 100644 --- a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/asl.auto.arima.json +++ b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/asl.auto.arima.json @@ -16,9 +16,10 @@ ] }, "properties": { + "title": "asl.auto.arima", "description": "\nmodel info: forecast::auto.arima() function in R, fit individually at each site/depth\n\nSites: bvre, fcre\n\nVariables: Daily Chlorophyll-a", "start_datetime": "2024-03-12", - "end_datetime": "2024-07-01", + "end_datetime": "2024-07-02", "providers": [ { "url": "pending", @@ -44,6 +45,11 @@ "Daily Chlorophyll-a" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -103,11 +109,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/asl.ets.json b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/asl.ets.json index 74b725d64e..ed6b1bf318 100644 --- a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/asl.ets.json +++ b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/asl.ets.json @@ -16,9 +16,10 @@ ] }, "properties": { + "title": "asl.ets", "description": "\nmodel info: forecast::ets() function in R, fit individually at each site/depth\n\nSites: bvre, fcre\n\nVariables: Daily Chlorophyll-a", "start_datetime": "2024-03-12", - "end_datetime": "2024-07-01", + "end_datetime": "2024-07-02", "providers": [ { "url": "pending", @@ -44,6 +45,11 @@ "Daily Chlorophyll-a" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -103,11 +109,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/asl.met.lm.json b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/asl.met.lm.json index 0b94a34c00..b028aff4c3 100644 --- a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/asl.met.lm.json +++ b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/asl.met.lm.json @@ -16,6 +16,7 @@ ] }, "properties": { + "title": "asl.met.lm", "description": "\nmodel info: Linear regression with air temp, humidity, precip, and wind speed\n\nSites: bvre, fcre\n\nVariables: Daily Chlorophyll-a", "start_datetime": "2024-05-06", "end_datetime": "2024-06-27", @@ -44,6 +45,11 @@ "Daily Chlorophyll-a" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -103,11 +109,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/asl.met.lm.step.json b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/asl.met.lm.step.json index bd23daa14a..5660c3d547 100644 --- a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/asl.met.lm.step.json +++ b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/asl.met.lm.step.json @@ -16,6 +16,7 @@ ] }, "properties": { + "title": "asl.met.lm.step", "description": "\nmodel info: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC\n\nSites: bvre, fcre\n\nVariables: Daily Chlorophyll-a", "start_datetime": "2024-05-06", "end_datetime": "2024-06-19", @@ -44,6 +45,11 @@ "Daily Chlorophyll-a" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -103,11 +109,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/asl.tbats.json b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/asl.tbats.json index dc5859a095..199546f3f8 100644 --- a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/asl.tbats.json +++ b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/asl.tbats.json @@ -16,9 +16,10 @@ ] }, "properties": { + "title": "asl.tbats", "description": "\nmodel info: forecast::tbats() function in R, fit individually at each site/depth\n\nSites: bvre, fcre\n\nVariables: Daily Chlorophyll-a", "start_datetime": "2024-03-20", - "end_datetime": "2024-07-01", + "end_datetime": "2024-07-02", "providers": [ { "url": "pending", @@ -44,6 +45,11 @@ "Daily Chlorophyll-a" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -103,11 +109,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/asl.temp.lm.json b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/asl.temp.lm.json index 74825f7da9..4dc7e886d2 100644 --- a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/asl.temp.lm.json +++ b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/asl.temp.lm.json @@ -16,6 +16,7 @@ ] }, "properties": { + "title": "asl.temp.lm", "description": "\nmodel info: Linear regression with air temperature\n\nSites: bvre, fcre\n\nVariables: Daily Chlorophyll-a", "start_datetime": "2024-05-06", "end_datetime": "2024-06-27", @@ -44,6 +45,11 @@ "Daily Chlorophyll-a" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -103,11 +109,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/climatology.json b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/climatology.json index 9a8e52fab0..628be3bd73 100644 --- a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/climatology.json +++ b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/climatology.json @@ -16,9 +16,10 @@ ] }, "properties": { + "title": "climatology", "description": "\nmodel info: Historical DOY mean and sd. Assumes normal distribution\n\nSites: bvre, fcre\n\nVariables: Daily Chlorophyll-a", "start_datetime": "2023-10-02", - "end_datetime": "2024-07-02", + "end_datetime": "2024-07-03", "providers": [ { "url": "pending", @@ -44,6 +45,11 @@ "Daily Chlorophyll-a" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -103,11 +109,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/climatology2.json b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/climatology2.json index 2422bbf906..91b4334a48 100644 --- a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/climatology2.json +++ b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/climatology2.json @@ -16,6 +16,7 @@ ] }, "properties": { + "title": "climatology2", "description": "\nmodel info: Same is the other climatology\n\nSites: bvre, fcre\n\nVariables: Daily Chlorophyll-a", "start_datetime": "2023-10-07", "end_datetime": "2023-11-10", @@ -44,6 +45,11 @@ "Daily Chlorophyll-a" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -103,11 +109,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/fableARIMA.json b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/fableARIMA.json index 8dcd940554..829a939f81 100644 --- a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/fableARIMA.json +++ b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/fableARIMA.json @@ -16,9 +16,10 @@ ] }, "properties": { + "title": "fableARIMA", "description": "\nmodel info: ARIMA fit using the ARIMA() function in the fable R package\n\nSites: bvre, fcre\n\nVariables: Daily Chlorophyll-a", "start_datetime": "2023-10-01", - "end_datetime": "2024-07-01", + "end_datetime": "2024-07-02", "providers": [ { "url": "pending", @@ -44,6 +45,11 @@ "Daily Chlorophyll-a" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -103,11 +109,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/fableETS.json b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/fableETS.json index e16b3af368..a03a407da3 100644 --- a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/fableETS.json +++ b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/fableETS.json @@ -16,9 +16,10 @@ ] }, "properties": { + "title": "fableETS", "description": "\nmodel info: fable package exponential smoothing model fable::ETS()\n\nSites: fcre, bvre\n\nVariables: Daily Chlorophyll-a", "start_datetime": "2023-10-01", - "end_datetime": "2024-07-01", + "end_datetime": "2024-07-02", "providers": [ { "url": "pending", @@ -44,6 +45,11 @@ "Daily Chlorophyll-a" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -103,11 +109,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/fableNNETAR.json b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/fableNNETAR.json index 4fbf02aa32..1c0233dc4a 100644 --- a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/fableNNETAR.json +++ b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/fableNNETAR.json @@ -16,9 +16,10 @@ ] }, "properties": { + "title": "fableNNETAR", "description": "\nmodel info: autoregressive neural net fit using the NNETAR() function in the fable R package\n\nSites: fcre, bvre\n\nVariables: Daily Chlorophyll-a", "start_datetime": "2023-10-01", - "end_datetime": "2024-07-01", + "end_datetime": "2024-07-02", "providers": [ { "url": "pending", @@ -44,6 +45,11 @@ "Daily Chlorophyll-a" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -103,11 +109,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/gfs_seamless.json b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/gfs_seamless.json index 685031d0ce..d7bc36fc23 100644 --- a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/gfs_seamless.json +++ b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/gfs_seamless.json @@ -16,6 +16,7 @@ ] }, "properties": { + "title": "gfs_seamless", "description": "\nmodel info: NOAA Global Ensemble Forecasting Model downloaded using the https://open-meteo.com. The seamless model combines the 0.25 and 0.5 degree resolution products to get a full 35-day ahead forecast\n\nSites: bvre, fcre\n\nVariables: Daily Chlorophyll-a", "start_datetime": "2024-04-09", "end_datetime": "2024-06-08", @@ -44,6 +45,11 @@ "Daily Chlorophyll-a" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -103,11 +109,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/glm_aed_v1.json b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/glm_aed_v1.json index 1f4556df31..4f627526f2 100644 --- a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/glm_aed_v1.json +++ b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/glm_aed_v1.json @@ -15,9 +15,10 @@ ] }, "properties": { + "title": "glm_aed_v1", "description": "\nmodel info: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters.\n\nSites: fcre\n\nVariables: Daily Chlorophyll-a", "start_datetime": "2023-10-14", - "end_datetime": "2024-06-29", + "end_datetime": "2024-06-30", "providers": [ { "url": "pending", @@ -43,6 +44,11 @@ "Daily Chlorophyll-a" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -102,11 +108,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/historic_mean.json b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/historic_mean.json index e008b1ea4a..b7985ba245 100644 --- a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/historic_mean.json +++ b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/historic_mean.json @@ -16,9 +16,10 @@ ] }, "properties": { + "title": "historic_mean", "description": "\nmodel info: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model.\n\nSites: fcre, bvre\n\nVariables: Daily Chlorophyll-a", "start_datetime": "2024-02-06", - "end_datetime": "2024-06-30", + "end_datetime": "2024-07-01", "providers": [ { "url": "pending", @@ -44,6 +45,11 @@ "Daily Chlorophyll-a" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -103,11 +109,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/monthly_mean.json b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/monthly_mean.json index 1b5161b7bd..1dea8a2bfc 100644 --- a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/monthly_mean.json +++ b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/monthly_mean.json @@ -16,9 +16,10 @@ ] }, "properties": { + "title": "monthly_mean", "description": "\nmodel info: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast.\n\nSites: fcre, bvre\n\nVariables: Daily Chlorophyll-a", "start_datetime": "2024-02-06", - "end_datetime": "2024-07-02", + "end_datetime": "2024-07-03", "providers": [ { "url": "pending", @@ -44,6 +45,11 @@ "Daily Chlorophyll-a" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -103,11 +109,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/persistenceRW.json b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/persistenceRW.json index efe31a168a..8d0cc56f3f 100644 --- a/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/persistenceRW.json +++ b/catalog/forecasts/Biological/Daily_Chlorophyll-a/models/persistenceRW.json @@ -16,9 +16,10 @@ ] }, "properties": { + "title": "persistenceRW", "description": "\nmodel info: Random walk from the fable package with ensembles used to represent uncertainty\n\nSites: bvre, fcre\n\nVariables: Daily Chlorophyll-a", "start_datetime": "2023-10-01", - "end_datetime": "2024-06-30", + "end_datetime": "2024-07-01", "providers": [ { "url": "pending", @@ -44,6 +45,11 @@ "Daily Chlorophyll-a" ], "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -103,11 +109,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, diff --git a/catalog/forecasts/Chemical/Daily_oxygen_concentration/collection.json b/catalog/forecasts/Chemical/Daily_oxygen_concentration/collection.json index 5b62ec0980..1fd9a4532d 100644 --- a/catalog/forecasts/Chemical/Daily_oxygen_concentration/collection.json +++ b/catalog/forecasts/Chemical/Daily_oxygen_concentration/collection.json @@ -134,12 +134,17 @@ "interval": [ [ "2023-10-14T00:00:00Z", - "2024-06-27T00:00:00Z" + "2024-07-03T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -199,11 +204,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ], "assets": { diff --git a/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/asl.auto.arima.json b/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/asl.auto.arima.json new file mode 100644 index 0000000000..fe617e8c59 --- /dev/null +++ b/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/asl.auto.arima.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "asl.auto.arima_DO_mgL_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "asl.auto.arima", + "description": "\nmodel info: forecast::auto.arima() function in R, fit individually at each site/depth\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", + "start_datetime": "2024-03-12", + "end_datetime": "2024-07-02", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily oxygen_concentration" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "asl.auto.arima" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "asl.auto.arima" + }, + { + "rel": "self", + "href": "asl.auto.arima.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.auto.arima.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.auto.arima.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily oxygen_concentration", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/asl.ets.json b/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/asl.ets.json new file mode 100644 index 0000000000..89fa34372d --- /dev/null +++ b/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/asl.ets.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "asl.ets_DO_mgL_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "asl.ets", + "description": "\nmodel info: forecast::ets() function in R, fit individually at each site/depth\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", + "start_datetime": "2024-03-12", + "end_datetime": "2024-07-02", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily oxygen_concentration" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "asl.ets" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "asl.ets" + }, + { + "rel": "self", + "href": "asl.ets.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.ets.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.ets.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily oxygen_concentration", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/asl.met.lm.json b/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/asl.met.lm.json new file mode 100644 index 0000000000..d1f8955af7 --- /dev/null +++ b/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/asl.met.lm.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "asl.met.lm_DO_mgL_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "asl.met.lm", + "description": "\nmodel info: Linear regression with air temp, humidity, precip, and wind speed\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", + "start_datetime": "2024-05-06", + "end_datetime": "2024-06-27", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily oxygen_concentration" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "asl.met.lm" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "asl.met.lm" + }, + { + "rel": "self", + "href": "asl.met.lm.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.met.lm.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.met.lm.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily oxygen_concentration", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/asl.met.lm.step.json b/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/asl.met.lm.step.json new file mode 100644 index 0000000000..2c9c9b9757 --- /dev/null +++ b/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/asl.met.lm.step.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "asl.met.lm.step_DO_mgL_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "asl.met.lm.step", + "description": "\nmodel info: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", + "start_datetime": "2024-05-06", + "end_datetime": "2024-06-19", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily oxygen_concentration" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "asl.met.lm.step" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "asl.met.lm.step" + }, + { + "rel": "self", + "href": "asl.met.lm.step.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.met.lm.step.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.met.lm.step.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily oxygen_concentration", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/asl.tbats.json b/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/asl.tbats.json new file mode 100644 index 0000000000..3d073b9f49 --- /dev/null +++ b/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/asl.tbats.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "asl.tbats_DO_mgL_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "asl.tbats", + "description": "\nmodel info: forecast::tbats() function in R, fit individually at each site/depth\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", + "start_datetime": "2024-03-20", + "end_datetime": "2024-07-02", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily oxygen_concentration" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "asl.tbats" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "asl.tbats" + }, + { + "rel": "self", + "href": "asl.tbats.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.tbats.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.tbats.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily oxygen_concentration", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/asl.temp.lm.json b/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/asl.temp.lm.json new file mode 100644 index 0000000000..630c08182e --- /dev/null +++ b/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/asl.temp.lm.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "asl.temp.lm_DO_mgL_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "asl.temp.lm", + "description": "\nmodel info: Linear regression with air temperature\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", + "start_datetime": "2024-05-06", + "end_datetime": "2024-06-27", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily oxygen_concentration" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "asl.temp.lm" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "asl.temp.lm" + }, + { + "rel": "self", + "href": "asl.temp.lm.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.temp.lm.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.temp.lm.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily oxygen_concentration", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/climatology.json b/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/climatology.json new file mode 100644 index 0000000000..cda74a3bc7 --- /dev/null +++ b/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/climatology.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "climatology_DO_mgL_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8372, 37.3032], + [-79.8159, 37.3129] + ] + }, + "properties": { + "title": "climatology", + "description": "\nmodel info: Historical DOY mean and sd. Assumes normal distribution\n\nSites: fcre, bvre\n\nVariables: Daily oxygen_concentration", + "start_datetime": "2024-02-06", + "end_datetime": "2024-07-03", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily oxygen_concentration" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "climatology" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "climatology" + }, + { + "rel": "self", + "href": "climatology.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/climatology.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/climatology.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily oxygen_concentration", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/fableNNETAR.json b/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/fableNNETAR.json new file mode 100644 index 0000000000..59c3bab41c --- /dev/null +++ b/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/fableNNETAR.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "fableNNETAR_DO_mgL_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "fableNNETAR", + "description": "\nmodel info: autoregressive neural net fit using the NNETAR() function in the fable R package\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", + "start_datetime": "2024-04-26", + "end_datetime": "2024-06-03", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily oxygen_concentration" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "fableNNETAR" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "fableNNETAR" + }, + { + "rel": "self", + "href": "fableNNETAR.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableNNETAR.R", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/fableNNETAR.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/fableNNETAR.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableNNETAR.R", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily oxygen_concentration", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/fableNNETAR_focal.json b/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/fableNNETAR_focal.json new file mode 100644 index 0000000000..7194a80ef1 --- /dev/null +++ b/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/fableNNETAR_focal.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "fableNNETAR_focal_DO_mgL_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "fableNNETAR_focal", + "description": "\nmodel info: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", + "start_datetime": "2024-04-26", + "end_datetime": "2024-07-01", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily oxygen_concentration" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "fableNNETAR_focal" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "fableNNETAR_focal" + }, + { + "rel": "self", + "href": "fableNNETAR_focal.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/addelany/vera4casts/blob/main/code/combined_workflow/nnetar_workflow.R", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/fableNNETAR_focal.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/fableNNETAR_focal.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/addelany/vera4casts/blob/main/code/combined_workflow/nnetar_workflow.R", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily oxygen_concentration", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=fableNNETAR_focal?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=fableNNETAR_focal?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/gfs_seamless.json b/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/gfs_seamless.json new file mode 100644 index 0000000000..6055554f76 --- /dev/null +++ b/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/gfs_seamless.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "gfs_seamless_DO_mgL_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "gfs_seamless", + "description": "\nmodel info: NOAA Global Ensemble Forecasting Model downloaded using the https://open-meteo.com. The seamless model combines the 0.25 and 0.5 degree resolution products to get a full 35-day ahead forecast\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", + "start_datetime": "2024-04-09", + "end_datetime": "2024-06-08", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily oxygen_concentration" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "gfs_seamless" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "gfs_seamless" + }, + { + "rel": "self", + "href": "gfs_seamless.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/addelany/vera4cast/blob/main/drivers/gfs_seamless.R", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/gfs_seamless.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/gfs_seamless.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/addelany/vera4cast/blob/main/drivers/gfs_seamless.R", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily oxygen_concentration", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/glm_aed_v1.json b/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/glm_aed_v1.json new file mode 100644 index 0000000000..bb5ebbdedb --- /dev/null +++ b/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/glm_aed_v1.json @@ -0,0 +1,167 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "glm_aed_v1_DO_mgL_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8372, 37.3032] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "glm_aed_v1", + "description": "\nmodel info: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters.\n\nSites: fcre\n\nVariables: Daily oxygen_concentration", + "start_datetime": "2023-10-14", + "end_datetime": "2024-06-30", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily oxygen_concentration" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "glm_aed_v1" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "glm_aed_v1" + }, + { + "rel": "self", + "href": "glm_aed_v1.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/FLARE-forecast/FCRE-forecast-code/blob/main/workflows/glm_aed/combined_run_aed.R", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/glm_aed_v1.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/glm_aed_v1.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/FLARE-forecast/FCRE-forecast-code/blob/main/workflows/glm_aed/combined_run_aed.R", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily oxygen_concentration", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/historic_mean.json b/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/historic_mean.json new file mode 100644 index 0000000000..6db8c192e4 --- /dev/null +++ b/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/historic_mean.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "historic_mean_DO_mgL_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8372, 37.3032], + [-79.8159, 37.3129] + ] + }, + "properties": { + "title": "historic_mean", + "description": "\nmodel info: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model.\n\nSites: fcre, bvre\n\nVariables: Daily oxygen_concentration", + "start_datetime": "2024-02-06", + "end_datetime": "2024-07-01", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily oxygen_concentration" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "historic_mean" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "historic_mean" + }, + { + "rel": "self", + "href": "historic_mean.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/historic_mean.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/historic_mean.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily oxygen_concentration", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/monthly_mean.json b/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/monthly_mean.json new file mode 100644 index 0000000000..3bbe11cd04 --- /dev/null +++ b/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/monthly_mean.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "monthly_mean_DO_mgL_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8372, 37.3032], + [-79.8159, 37.3129] + ] + }, + "properties": { + "title": "monthly_mean", + "description": "\nmodel info: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast.\n\nSites: fcre, bvre\n\nVariables: Daily oxygen_concentration", + "start_datetime": "2024-02-06", + "end_datetime": "2024-07-03", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily oxygen_concentration" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "monthly_mean" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "monthly_mean" + }, + { + "rel": "self", + "href": "monthly_mean.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/monthly_mean.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/monthly_mean.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily oxygen_concentration", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/persistenceRW.json b/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/persistenceRW.json new file mode 100644 index 0000000000..0058c4358e --- /dev/null +++ b/catalog/forecasts/Chemical/Daily_oxygen_concentration/models/persistenceRW.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "persistenceRW_DO_mgL_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8372, 37.3032], + [-79.8159, 37.3129] + ] + }, + "properties": { + "title": "persistenceRW", + "description": "\nmodel info: Random walk from the fable package with ensembles used to represent uncertainty\n\nSites: fcre, bvre\n\nVariables: Daily oxygen_concentration", + "start_datetime": "2024-02-06", + "end_datetime": "2024-07-01", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily oxygen_concentration" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "persistenceRW" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "persistenceRW" + }, + { + "rel": "self", + "href": "persistenceRW.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/persistenceRW.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/persistenceRW.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily oxygen_concentration", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Secchi/collection.json b/catalog/forecasts/Physical/Daily_Secchi/collection.json index e469cec8a4..a43b6e12d0 100644 --- a/catalog/forecasts/Physical/Daily_Secchi/collection.json +++ b/catalog/forecasts/Physical/Daily_Secchi/collection.json @@ -135,12 +135,17 @@ "interval": [ [ "2023-10-14T00:00:00Z", - "2024-06-27T00:00:00Z" + "2024-07-03T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -200,11 +205,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ], "assets": { diff --git a/catalog/forecasts/Physical/Daily_Secchi/models/asl.auto.arima.json b/catalog/forecasts/Physical/Daily_Secchi/models/asl.auto.arima.json new file mode 100644 index 0000000000..80c8cf66ad --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Secchi/models/asl.auto.arima.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "asl.auto.arima_Secchi_m_sample_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "asl.auto.arima", + "description": "\nmodel info: forecast::auto.arima() function in R, fit individually at each site/depth\n\nSites: bvre, fcre\n\nVariables: Daily Secchi", + "start_datetime": "2024-03-12", + "end_datetime": "2024-07-02", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Secchi" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "asl.auto.arima" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "asl.auto.arima" + }, + { + "rel": "self", + "href": "asl.auto.arima.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.auto.arima.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.auto.arima.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Secchi", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Secchi/models/asl.ets.json b/catalog/forecasts/Physical/Daily_Secchi/models/asl.ets.json new file mode 100644 index 0000000000..8177deaed0 --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Secchi/models/asl.ets.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "asl.ets_Secchi_m_sample_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "asl.ets", + "description": "\nmodel info: forecast::ets() function in R, fit individually at each site/depth\n\nSites: bvre, fcre\n\nVariables: Daily Secchi", + "start_datetime": "2024-03-12", + "end_datetime": "2024-07-02", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Secchi" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "asl.ets" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "asl.ets" + }, + { + "rel": "self", + "href": "asl.ets.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.ets.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.ets.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Secchi", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.ets?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.ets?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Secchi/models/asl.met.lm.json b/catalog/forecasts/Physical/Daily_Secchi/models/asl.met.lm.json new file mode 100644 index 0000000000..94382da0c9 --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Secchi/models/asl.met.lm.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "asl.met.lm_Secchi_m_sample_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "asl.met.lm", + "description": "\nmodel info: Linear regression with air temp, humidity, precip, and wind speed\n\nSites: bvre, fcre\n\nVariables: Daily Secchi", + "start_datetime": "2024-05-06", + "end_datetime": "2024-06-27", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Secchi" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "asl.met.lm" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "asl.met.lm" + }, + { + "rel": "self", + "href": "asl.met.lm.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.met.lm.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.met.lm.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Secchi", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Secchi/models/asl.met.lm.step.json b/catalog/forecasts/Physical/Daily_Secchi/models/asl.met.lm.step.json new file mode 100644 index 0000000000..7f63da8410 --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Secchi/models/asl.met.lm.step.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "asl.met.lm.step_Secchi_m_sample_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "asl.met.lm.step", + "description": "\nmodel info: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC\n\nSites: bvre, fcre\n\nVariables: Daily Secchi", + "start_datetime": "2024-05-06", + "end_datetime": "2024-06-19", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Secchi" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "asl.met.lm.step" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "asl.met.lm.step" + }, + { + "rel": "self", + "href": "asl.met.lm.step.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.met.lm.step.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.met.lm.step.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Secchi", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Secchi/models/asl.tbats.json b/catalog/forecasts/Physical/Daily_Secchi/models/asl.tbats.json new file mode 100644 index 0000000000..9405bb76d9 --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Secchi/models/asl.tbats.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "asl.tbats_Secchi_m_sample_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "asl.tbats", + "description": "\nmodel info: forecast::tbats() function in R, fit individually at each site/depth\n\nSites: bvre, fcre\n\nVariables: Daily Secchi", + "start_datetime": "2024-03-20", + "end_datetime": "2024-07-02", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Secchi" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "asl.tbats" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "asl.tbats" + }, + { + "rel": "self", + "href": "asl.tbats.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.tbats.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.tbats.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Secchi", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Secchi/models/asl.temp.lm.json b/catalog/forecasts/Physical/Daily_Secchi/models/asl.temp.lm.json new file mode 100644 index 0000000000..0d9b775e1e --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Secchi/models/asl.temp.lm.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "asl.temp.lm_Secchi_m_sample_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "asl.temp.lm", + "description": "\nmodel info: Linear regression with air temperature\n\nSites: bvre, fcre\n\nVariables: Daily Secchi", + "start_datetime": "2024-05-06", + "end_datetime": "2024-06-27", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Secchi" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "asl.temp.lm" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "asl.temp.lm" + }, + { + "rel": "self", + "href": "asl.temp.lm.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.temp.lm.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.temp.lm.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Secchi", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Secchi/models/climatology.json b/catalog/forecasts/Physical/Daily_Secchi/models/climatology.json new file mode 100644 index 0000000000..501ea803af --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Secchi/models/climatology.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "climatology_Secchi_m_sample_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8372, 37.3032], + [-79.8159, 37.3129] + ] + }, + "properties": { + "title": "climatology", + "description": "\nmodel info: Historical DOY mean and sd. Assumes normal distribution\n\nSites: fcre, bvre\n\nVariables: Daily Secchi", + "start_datetime": "2024-02-06", + "end_datetime": "2024-07-03", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Secchi" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "climatology" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "climatology" + }, + { + "rel": "self", + "href": "climatology.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/climatology.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/climatology.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Secchi", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=climatology?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=climatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Secchi/models/fableNNETAR.json b/catalog/forecasts/Physical/Daily_Secchi/models/fableNNETAR.json new file mode 100644 index 0000000000..28ec8f9ca3 --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Secchi/models/fableNNETAR.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "fableNNETAR_Secchi_m_sample_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "fableNNETAR", + "description": "\nmodel info: autoregressive neural net fit using the NNETAR() function in the fable R package\n\nSites: bvre, fcre\n\nVariables: Daily Secchi", + "start_datetime": "2024-04-26", + "end_datetime": "2024-06-03", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Secchi" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "fableNNETAR" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "fableNNETAR" + }, + { + "rel": "self", + "href": "fableNNETAR.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableNNETAR.R", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/fableNNETAR.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/fableNNETAR.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableNNETAR.R", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Secchi", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Secchi/models/fableNNETAR_focal.json b/catalog/forecasts/Physical/Daily_Secchi/models/fableNNETAR_focal.json new file mode 100644 index 0000000000..8135376aee --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Secchi/models/fableNNETAR_focal.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "fableNNETAR_focal_Secchi_m_sample_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "fableNNETAR_focal", + "description": "\nmodel info: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables\n\nSites: bvre, fcre\n\nVariables: Daily Secchi", + "start_datetime": "2024-04-26", + "end_datetime": "2024-07-01", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Secchi" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "fableNNETAR_focal" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "fableNNETAR_focal" + }, + { + "rel": "self", + "href": "fableNNETAR_focal.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/addelany/vera4casts/blob/main/code/combined_workflow/nnetar_workflow.R", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/fableNNETAR_focal.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/fableNNETAR_focal.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/addelany/vera4casts/blob/main/code/combined_workflow/nnetar_workflow.R", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Secchi", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=fableNNETAR_focal?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=fableNNETAR_focal?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Secchi/models/gfs_seamless.json b/catalog/forecasts/Physical/Daily_Secchi/models/gfs_seamless.json new file mode 100644 index 0000000000..27fe32a1bd --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Secchi/models/gfs_seamless.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "gfs_seamless_Secchi_m_sample_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "gfs_seamless", + "description": "\nmodel info: NOAA Global Ensemble Forecasting Model downloaded using the https://open-meteo.com. The seamless model combines the 0.25 and 0.5 degree resolution products to get a full 35-day ahead forecast\n\nSites: bvre, fcre\n\nVariables: Daily Secchi", + "start_datetime": "2024-04-09", + "end_datetime": "2024-06-08", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Secchi" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "gfs_seamless" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "gfs_seamless" + }, + { + "rel": "self", + "href": "gfs_seamless.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/addelany/vera4cast/blob/main/drivers/gfs_seamless.R", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/gfs_seamless.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/gfs_seamless.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/addelany/vera4cast/blob/main/drivers/gfs_seamless.R", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Secchi", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Secchi/models/glm_aed_v1.json b/catalog/forecasts/Physical/Daily_Secchi/models/glm_aed_v1.json new file mode 100644 index 0000000000..627ca98b5d --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Secchi/models/glm_aed_v1.json @@ -0,0 +1,167 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "glm_aed_v1_Secchi_m_sample_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8372, 37.3032] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "glm_aed_v1", + "description": "\nmodel info: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters.\n\nSites: fcre\n\nVariables: Daily Secchi", + "start_datetime": "2023-10-14", + "end_datetime": "2024-06-30", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Secchi" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "glm_aed_v1" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "glm_aed_v1" + }, + { + "rel": "self", + "href": "glm_aed_v1.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/FLARE-forecast/FCRE-forecast-code/blob/main/workflows/glm_aed/combined_run_aed.R", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/glm_aed_v1.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/glm_aed_v1.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/FLARE-forecast/FCRE-forecast-code/blob/main/workflows/glm_aed/combined_run_aed.R", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Secchi", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Secchi/models/historic_mean.json b/catalog/forecasts/Physical/Daily_Secchi/models/historic_mean.json new file mode 100644 index 0000000000..992ca42c1a --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Secchi/models/historic_mean.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "historic_mean_Secchi_m_sample_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8372, 37.3032], + [-79.8159, 37.3129] + ] + }, + "properties": { + "title": "historic_mean", + "description": "\nmodel info: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model.\n\nSites: fcre, bvre\n\nVariables: Daily Secchi", + "start_datetime": "2024-02-06", + "end_datetime": "2024-07-01", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Secchi" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "historic_mean" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "historic_mean" + }, + { + "rel": "self", + "href": "historic_mean.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/historic_mean.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/historic_mean.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Secchi", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=historic_mean?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=historic_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Secchi/models/monthly_mean.json b/catalog/forecasts/Physical/Daily_Secchi/models/monthly_mean.json new file mode 100644 index 0000000000..f1ce0b0349 --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Secchi/models/monthly_mean.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "monthly_mean_Secchi_m_sample_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8372, 37.3032], + [-79.8159, 37.3129] + ] + }, + "properties": { + "title": "monthly_mean", + "description": "\nmodel info: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast.\n\nSites: fcre, bvre\n\nVariables: Daily Secchi", + "start_datetime": "2024-02-06", + "end_datetime": "2024-07-03", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Secchi" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "monthly_mean" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "monthly_mean" + }, + { + "rel": "self", + "href": "monthly_mean.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/monthly_mean.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/monthly_mean.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Secchi", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Secchi/models/persistenceRW.json b/catalog/forecasts/Physical/Daily_Secchi/models/persistenceRW.json new file mode 100644 index 0000000000..75d32c5b62 --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Secchi/models/persistenceRW.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "persistenceRW_Secchi_m_sample_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8372, 37.3032], + [-79.8159, 37.3129] + ] + }, + "properties": { + "title": "persistenceRW", + "description": "\nmodel info: Random walk from the fable package with ensembles used to represent uncertainty\n\nSites: fcre, bvre\n\nVariables: Daily Secchi", + "start_datetime": "2024-02-06", + "end_datetime": "2024-07-01", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Secchi" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "persistenceRW" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "persistenceRW" + }, + { + "rel": "self", + "href": "persistenceRW.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/persistenceRW.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/persistenceRW.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Secchi", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Secchi/models/secchi_example_forecast.json b/catalog/forecasts/Physical/Daily_Secchi/models/secchi_example_forecast.json new file mode 100644 index 0000000000..448b2d7b0e --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Secchi/models/secchi_example_forecast.json @@ -0,0 +1,167 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "secchi_example_forecast_Secchi_m_sample_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8372, 37.3032] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "secchi_example_forecast", + "description": [], + "start_datetime": "2024-04-11", + "end_datetime": "2024-05-11", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Secchi" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "secchi_example_forecast" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "secchi_example_forecast" + }, + { + "rel": "self", + "href": "secchi_example_forecast.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": [], + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/secchi_example_forecast.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/secchi_example_forecast.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": [], + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Secchi", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=secchi_example_forecast?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=secchi_example_forecast?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Secchi/models/secchi_last3obs_mean.json b/catalog/forecasts/Physical/Daily_Secchi/models/secchi_last3obs_mean.json new file mode 100644 index 0000000000..400e97a008 --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Secchi/models/secchi_last3obs_mean.json @@ -0,0 +1,167 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "secchi_last3obs_mean_Secchi_m_sample_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8372, 37.3032] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "secchi_last3obs_mean", + "description": "\nmodel info: This forecast simply takes the mean of the last three secchi observations and uses the standard deviation of that mean for the uncertainty around the forecast.\n\nSites: fcre\n\nVariables: Daily Secchi", + "start_datetime": "2024-05-02", + "end_datetime": "2024-06-27", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Secchi" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "secchi_last3obs_mean" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "secchi_last3obs_mean" + }, + { + "rel": "self", + "href": "secchi_last3obs_mean.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/kjkhoffman/vera4casts/blob/main/forecast_code/run_secchi_forecast.R", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/secchi_last3obs_mean.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/secchi_last3obs_mean.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/kjkhoffman/vera4casts/blob/main/forecast_code/run_secchi_forecast.R", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Secchi", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=secchi_last3obs_mean?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=secchi_last3obs_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Water_temperature/collection.json b/catalog/forecasts/Physical/Daily_Water_temperature/collection.json index 815522a8b1..9312a157e5 100644 --- a/catalog/forecasts/Physical/Daily_Water_temperature/collection.json +++ b/catalog/forecasts/Physical/Daily_Water_temperature/collection.json @@ -175,12 +175,17 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2024-06-27T00:00:00Z" + "2024-07-03T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -240,11 +245,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ], "assets": { diff --git a/catalog/forecasts/Physical/Daily_Water_temperature/models/TESTclimatology.json b/catalog/forecasts/Physical/Daily_Water_temperature/models/TESTclimatology.json new file mode 100644 index 0000000000..e91e40bae7 --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Water_temperature/models/TESTclimatology.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "TESTclimatology_Temp_C_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "TESTclimatology", + "description": [], + "start_datetime": "2023-09-22", + "end_datetime": "2023-10-27", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Water_temperature" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "TESTclimatology" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "TESTclimatology" + }, + { + "rel": "self", + "href": "TESTclimatology.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": [], + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/TESTclimatology.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/TESTclimatology.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": [], + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Water_temperature", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=TESTclimatology?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=TESTclimatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Water_temperature/models/TempC_mean_example_forecast.json b/catalog/forecasts/Physical/Daily_Water_temperature/models/TempC_mean_example_forecast.json new file mode 100644 index 0000000000..ebb0dd2a45 --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Water_temperature/models/TempC_mean_example_forecast.json @@ -0,0 +1,167 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "TempC_mean_example_forecast_Temp_C_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8372, 37.3032] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "TempC_mean_example_forecast", + "description": [], + "start_datetime": "2023-11-07", + "end_datetime": "2024-07-01", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Water_temperature" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "TempC_mean_example_forecast" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "TempC_mean_example_forecast" + }, + { + "rel": "self", + "href": "TempC_mean_example_forecast.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": [], + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/TempC_mean_example_forecast.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/TempC_mean_example_forecast.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": [], + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Water_temperature", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=TempC_mean_example_forecast?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=TempC_mean_example_forecast?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Water_temperature/models/Temp_C_mean.json b/catalog/forecasts/Physical/Daily_Water_temperature/models/Temp_C_mean.json new file mode 100644 index 0000000000..7296c76042 --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Water_temperature/models/Temp_C_mean.json @@ -0,0 +1,167 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "Temp_C_mean_Temp_C_mean_P1D_forecast", + "bbox": [ + [-79.8357, 37.3078, -79.8357, 37.3078] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8357, 37.3078] + ] + }, + "properties": { + "title": "Temp_C_mean", + "description": [], + "start_datetime": "2024-02-06", + "end_datetime": "2024-03-15", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Water_temperature" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "Temp_C_mean" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "Temp_C_mean" + }, + { + "rel": "self", + "href": "Temp_C_mean.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": [], + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/Temp_C_mean.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/Temp_C_mean.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": [], + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Water_temperature", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=Temp_C_mean?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=Temp_C_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Water_temperature/models/asl.auto.arima.json b/catalog/forecasts/Physical/Daily_Water_temperature/models/asl.auto.arima.json new file mode 100644 index 0000000000..6ef8602130 --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Water_temperature/models/asl.auto.arima.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "asl.auto.arima_Temp_C_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "asl.auto.arima", + "description": "\nmodel info: forecast::auto.arima() function in R, fit individually at each site/depth\n\nSites: bvre, fcre\n\nVariables: Daily Water_temperature", + "start_datetime": "2024-03-12", + "end_datetime": "2024-07-02", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Water_temperature" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "asl.auto.arima" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "asl.auto.arima" + }, + { + "rel": "self", + "href": "asl.auto.arima.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.auto.arima.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.auto.arima.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Water_temperature", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Water_temperature/models/asl.ets.json b/catalog/forecasts/Physical/Daily_Water_temperature/models/asl.ets.json new file mode 100644 index 0000000000..5f9150dbec --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Water_temperature/models/asl.ets.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "asl.ets_Temp_C_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "asl.ets", + "description": "\nmodel info: forecast::ets() function in R, fit individually at each site/depth\n\nSites: bvre, fcre\n\nVariables: Daily Water_temperature", + "start_datetime": "2024-03-12", + "end_datetime": "2024-07-02", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Water_temperature" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "asl.ets" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "asl.ets" + }, + { + "rel": "self", + "href": "asl.ets.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.ets.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.ets.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Water_temperature", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Water_temperature/models/asl.met.lm.json b/catalog/forecasts/Physical/Daily_Water_temperature/models/asl.met.lm.json new file mode 100644 index 0000000000..1125bdb134 --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Water_temperature/models/asl.met.lm.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "asl.met.lm_Temp_C_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "asl.met.lm", + "description": "\nmodel info: Linear regression with air temp, humidity, precip, and wind speed\n\nSites: bvre, fcre\n\nVariables: Daily Water_temperature", + "start_datetime": "2024-05-06", + "end_datetime": "2024-06-27", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Water_temperature" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "asl.met.lm" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "asl.met.lm" + }, + { + "rel": "self", + "href": "asl.met.lm.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.met.lm.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.met.lm.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Water_temperature", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Water_temperature/models/asl.met.lm.step.json b/catalog/forecasts/Physical/Daily_Water_temperature/models/asl.met.lm.step.json new file mode 100644 index 0000000000..ca42d83fcf --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Water_temperature/models/asl.met.lm.step.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "asl.met.lm.step_Temp_C_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "asl.met.lm.step", + "description": "\nmodel info: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC\n\nSites: bvre, fcre\n\nVariables: Daily Water_temperature", + "start_datetime": "2024-05-06", + "end_datetime": "2024-06-19", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Water_temperature" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "asl.met.lm.step" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "asl.met.lm.step" + }, + { + "rel": "self", + "href": "asl.met.lm.step.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.met.lm.step.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.met.lm.step.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Water_temperature", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Water_temperature/models/asl.tbats.json b/catalog/forecasts/Physical/Daily_Water_temperature/models/asl.tbats.json new file mode 100644 index 0000000000..238b6c2561 --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Water_temperature/models/asl.tbats.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "asl.tbats_Temp_C_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "asl.tbats", + "description": "\nmodel info: forecast::tbats() function in R, fit individually at each site/depth\n\nSites: bvre, fcre\n\nVariables: Daily Water_temperature", + "start_datetime": "2024-03-20", + "end_datetime": "2024-07-02", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Water_temperature" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "asl.tbats" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "asl.tbats" + }, + { + "rel": "self", + "href": "asl.tbats.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.tbats.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.tbats.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Water_temperature", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Water_temperature/models/asl.temp.lm.json b/catalog/forecasts/Physical/Daily_Water_temperature/models/asl.temp.lm.json new file mode 100644 index 0000000000..6bcff34482 --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Water_temperature/models/asl.temp.lm.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "asl.temp.lm_Temp_C_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "asl.temp.lm", + "description": "\nmodel info: Linear regression with air temperature\n\nSites: bvre, fcre\n\nVariables: Daily Water_temperature", + "start_datetime": "2024-05-06", + "end_datetime": "2024-06-27", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Water_temperature" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "asl.temp.lm" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "asl.temp.lm" + }, + { + "rel": "self", + "href": "asl.temp.lm.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.temp.lm.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.temp.lm.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Water_temperature", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Water_temperature/models/climatology.json b/catalog/forecasts/Physical/Daily_Water_temperature/models/climatology.json new file mode 100644 index 0000000000..c71b7ad888 --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Water_temperature/models/climatology.json @@ -0,0 +1,169 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "climatology_Temp_C_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032], + [-79.8357, 37.3078] + ] + }, + "properties": { + "title": "climatology", + "description": "\nmodel info: Historical DOY mean and sd. Assumes normal distribution\n\nSites: bvre, fcre, tubr\n\nVariables: Daily Water_temperature", + "start_datetime": "2023-09-21", + "end_datetime": "2024-07-03", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Water_temperature" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "climatology" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "climatology" + }, + { + "rel": "self", + "href": "climatology.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/climatology.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/climatology.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Water_temperature", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Water_temperature/models/climatology2.json b/catalog/forecasts/Physical/Daily_Water_temperature/models/climatology2.json new file mode 100644 index 0000000000..d4f5421918 --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Water_temperature/models/climatology2.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "climatology2_Temp_C_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "climatology2", + "description": "\nmodel info: Same is the other climatology\n\nSites: bvre, fcre\n\nVariables: Daily Water_temperature", + "start_datetime": "2023-10-07", + "end_datetime": "2023-11-10", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Water_temperature" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "climatology2" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "climatology2" + }, + { + "rel": "self", + "href": "climatology2.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/baseline_models/run_exo_baselines.R", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/climatology2.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/climatology2.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/baseline_models/run_exo_baselines.R", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Water_temperature", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=climatology2?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=climatology2?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Water_temperature/models/example_ID.json b/catalog/forecasts/Physical/Daily_Water_temperature/models/example_ID.json new file mode 100644 index 0000000000..b9090c259c --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Water_temperature/models/example_ID.json @@ -0,0 +1,167 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "example_ID_Temp_C_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8372, 37.3032] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "example_ID", + "description": [], + "start_datetime": "2024-04-03", + "end_datetime": "2024-06-26", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Water_temperature" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "example_ID" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "example_ID" + }, + { + "rel": "self", + "href": "example_ID.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": [], + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/example_ID.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/example_ID.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": [], + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Water_temperature", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=example_ID?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=example_ID?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Water_temperature/models/example_forecast.json b/catalog/forecasts/Physical/Daily_Water_temperature/models/example_forecast.json new file mode 100644 index 0000000000..443af1e0a8 --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Water_temperature/models/example_forecast.json @@ -0,0 +1,167 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "example_forecast_Temp_C_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8372, 37.3032] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "example_forecast", + "description": [], + "start_datetime": "2023-11-07", + "end_datetime": "2023-12-06", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Water_temperature" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "example_forecast" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "example_forecast" + }, + { + "rel": "self", + "href": "example_forecast.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": [], + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/example_forecast.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/example_forecast.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": [], + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Water_temperature", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=example_forecast?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=example_forecast?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Water_temperature/models/fableNNETAR.json b/catalog/forecasts/Physical/Daily_Water_temperature/models/fableNNETAR.json new file mode 100644 index 0000000000..8838d4927c --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Water_temperature/models/fableNNETAR.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "fableNNETAR_Temp_C_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "fableNNETAR", + "description": "\nmodel info: autoregressive neural net fit using the NNETAR() function in the fable R package\n\nSites: bvre, fcre\n\nVariables: Daily Water_temperature", + "start_datetime": "2024-04-26", + "end_datetime": "2024-06-03", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Water_temperature" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "fableNNETAR" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "fableNNETAR" + }, + { + "rel": "self", + "href": "fableNNETAR.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableNNETAR.R", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/fableNNETAR.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/fableNNETAR.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableNNETAR.R", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Water_temperature", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Water_temperature/models/fableNNETAR_focal.json b/catalog/forecasts/Physical/Daily_Water_temperature/models/fableNNETAR_focal.json new file mode 100644 index 0000000000..ad8f2b593e --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Water_temperature/models/fableNNETAR_focal.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "fableNNETAR_focal_Temp_C_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "fableNNETAR_focal", + "description": "\nmodel info: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables\n\nSites: bvre, fcre\n\nVariables: Daily Water_temperature", + "start_datetime": "2024-04-26", + "end_datetime": "2024-07-01", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Water_temperature" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "fableNNETAR_focal" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "fableNNETAR_focal" + }, + { + "rel": "self", + "href": "fableNNETAR_focal.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/addelany/vera4casts/blob/main/code/combined_workflow/nnetar_workflow.R", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/fableNNETAR_focal.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/fableNNETAR_focal.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/addelany/vera4casts/blob/main/code/combined_workflow/nnetar_workflow.R", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Water_temperature", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=fableNNETAR_focal?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=fableNNETAR_focal?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Water_temperature/models/flareGOTM.json b/catalog/forecasts/Physical/Daily_Water_temperature/models/flareGOTM.json new file mode 100644 index 0000000000..7dc4c415ef --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Water_temperature/models/flareGOTM.json @@ -0,0 +1,167 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "flareGOTM_Temp_C_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8372, 37.3032] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "flareGOTM", + "description": "\nmodel info: FLARE-GOTM combines the 1D hydrodynamic process-based model GOTM, a data assimilation algorithm, and NOAA weather data to forecast water column temperatures.\n\nSites: fcre\n\nVariables: Daily Water_temperature", + "start_datetime": "2024-01-21", + "end_datetime": "2024-03-19", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Water_temperature" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "flareGOTM" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "flareGOTM" + }, + { + "rel": "self", + "href": "flareGOTM.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": null, + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/flareGOTM.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/flareGOTM.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": null, + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Water_temperature", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=flareGOTM?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=flareGOTM?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Water_temperature/models/flareSimstrat.json b/catalog/forecasts/Physical/Daily_Water_temperature/models/flareSimstrat.json new file mode 100644 index 0000000000..15506f552c --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Water_temperature/models/flareSimstrat.json @@ -0,0 +1,167 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "flareSimstrat_Temp_C_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8372, 37.3032] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "flareSimstrat", + "description": "\nmodel info: FLARE-Simstrat combines the 1D process-based model Simstrat, a data assimilation algorithm (EnKF) and NOAA driver weather data to make predictions of water column temperatures.\n\nSites: fcre\n\nVariables: Daily Water_temperature", + "start_datetime": "2024-01-21", + "end_datetime": "2024-03-19", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Water_temperature" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "flareSimstrat" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "flareSimstrat" + }, + { + "rel": "self", + "href": "flareSimstrat.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/FLARE-forecast/FCRE-forecast-code/blob/main/workflows/ler/combined_workflow_Simstrat.R", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/flareSimstrat.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/flareSimstrat.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/FLARE-forecast/FCRE-forecast-code/blob/main/workflows/ler/combined_workflow_Simstrat.R", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Water_temperature", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=flareSimstrat?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=flareSimstrat?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Water_temperature/models/gfs_seamless.json b/catalog/forecasts/Physical/Daily_Water_temperature/models/gfs_seamless.json new file mode 100644 index 0000000000..e89b52fabc --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Water_temperature/models/gfs_seamless.json @@ -0,0 +1,168 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "gfs_seamless_Temp_C_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "gfs_seamless", + "description": "\nmodel info: NOAA Global Ensemble Forecasting Model downloaded using the https://open-meteo.com. The seamless model combines the 0.25 and 0.5 degree resolution products to get a full 35-day ahead forecast\n\nSites: bvre, fcre\n\nVariables: Daily Water_temperature", + "start_datetime": "2024-04-09", + "end_datetime": "2024-06-08", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Water_temperature" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "gfs_seamless" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "gfs_seamless" + }, + { + "rel": "self", + "href": "gfs_seamless.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/addelany/vera4cast/blob/main/drivers/gfs_seamless.R", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/gfs_seamless.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/gfs_seamless.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/addelany/vera4cast/blob/main/drivers/gfs_seamless.R", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Water_temperature", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Water_temperature/models/glm_aed_v1.json b/catalog/forecasts/Physical/Daily_Water_temperature/models/glm_aed_v1.json new file mode 100644 index 0000000000..b0aedef2bc --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Water_temperature/models/glm_aed_v1.json @@ -0,0 +1,167 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "glm_aed_v1_Temp_C_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8372, 37.3032] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8372, 37.3032] + ] + }, + "properties": { + "title": "glm_aed_v1", + "description": "\nmodel info: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters.\n\nSites: fcre\n\nVariables: Daily Water_temperature", + "start_datetime": "2023-10-14", + "end_datetime": "2024-06-30", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Water_temperature" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "glm_aed_v1" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "glm_aed_v1" + }, + { + "rel": "self", + "href": "glm_aed_v1.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/FLARE-forecast/FCRE-forecast-code/blob/main/workflows/glm_aed/combined_run_aed.R", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/glm_aed_v1.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/glm_aed_v1.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/FLARE-forecast/FCRE-forecast-code/blob/main/workflows/glm_aed/combined_run_aed.R", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Water_temperature", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Water_temperature/models/historic_mean.json b/catalog/forecasts/Physical/Daily_Water_temperature/models/historic_mean.json new file mode 100644 index 0000000000..61504d19a6 --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Water_temperature/models/historic_mean.json @@ -0,0 +1,169 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "historic_mean_Temp_C_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8357, 37.3078], + [-79.8372, 37.3032], + [-79.8159, 37.3129] + ] + }, + "properties": { + "title": "historic_mean", + "description": "\nmodel info: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model.\n\nSites: tubr, fcre, bvre\n\nVariables: Daily Water_temperature", + "start_datetime": "2024-02-06", + "end_datetime": "2024-07-01", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Water_temperature" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "historic_mean" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "historic_mean" + }, + { + "rel": "self", + "href": "historic_mean.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/historic_mean.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/historic_mean.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Water_temperature", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Water_temperature/models/inflow_gefsClimAED.json b/catalog/forecasts/Physical/Daily_Water_temperature/models/inflow_gefsClimAED.json new file mode 100644 index 0000000000..4453efb335 --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Water_temperature/models/inflow_gefsClimAED.json @@ -0,0 +1,167 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "inflow_gefsClimAED_Temp_C_mean_P1D_forecast", + "bbox": [ + [-79.8357, 37.3078, -79.8357, 37.3078] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8357, 37.3078] + ] + }, + "properties": { + "title": "inflow_gefsClimAED", + "description": "\nmodel info: flow is forecasted as using a linear relationship between historical flow, month, and 5-day sum of precipitation. Temperature is forecasted using a linear relationship between historical water temperature, month, and 5-day mean air temperature. NOAA GEFS is then used to get the future values of 5-day sum precipitation and mean temperature. Nutrients are forecasting using the DOY climatology. The DOY climatology was developed using a linear interpolation of the historical samples.\n\nSites: tubr\n\nVariables: Daily Water_temperature", + "start_datetime": "2023-10-13", + "end_datetime": "2024-06-30", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Water_temperature" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "inflow_gefsClimAED" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "inflow_gefsClimAED" + }, + { + "rel": "self", + "href": "inflow_gefsClimAED.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/LTREB-reservoirs/vera4cast_models/blob/main/inflow_aed.R", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/inflow_gefsClimAED.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/inflow_gefsClimAED.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/LTREB-reservoirs/vera4cast_models/blob/main/inflow_aed.R", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Water_temperature", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Water_temperature/models/monthly_mean.json b/catalog/forecasts/Physical/Daily_Water_temperature/models/monthly_mean.json new file mode 100644 index 0000000000..3d2191849d --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Water_temperature/models/monthly_mean.json @@ -0,0 +1,169 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "monthly_mean_Temp_C_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8357, 37.3078], + [-79.8372, 37.3032], + [-79.8159, 37.3129] + ] + }, + "properties": { + "title": "monthly_mean", + "description": "\nmodel info: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast.\n\nSites: tubr, fcre, bvre\n\nVariables: Daily Water_temperature", + "start_datetime": "2024-02-06", + "end_datetime": "2024-07-03", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Water_temperature" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "monthly_mean" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "monthly_mean" + }, + { + "rel": "self", + "href": "monthly_mean.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/monthly_mean.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/monthly_mean.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Water_temperature", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Water_temperature/models/persistenceFO.json b/catalog/forecasts/Physical/Daily_Water_temperature/models/persistenceFO.json new file mode 100644 index 0000000000..b76b67b64a --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Water_temperature/models/persistenceFO.json @@ -0,0 +1,167 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "persistenceFO_Temp_C_mean_P1D_forecast", + "bbox": [ + [-79.8159, 37.3129, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129] + ] + }, + "properties": { + "title": "persistenceFO", + "description": "\nmodel info: another persistence forecast\n\nSites: bvre\n\nVariables: Daily Water_temperature", + "start_datetime": "2023-09-27", + "end_datetime": "2023-10-30", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Water_temperature" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "persistenceFO" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "persistenceFO" + }, + { + "rel": "self", + "href": "persistenceFO.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": null, + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/persistenceFO.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/persistenceFO.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": null, + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Water_temperature", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=persistenceFO?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=persistenceFO?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Daily_Water_temperature/models/persistenceRW.json b/catalog/forecasts/Physical/Daily_Water_temperature/models/persistenceRW.json new file mode 100644 index 0000000000..91926b1a7a --- /dev/null +++ b/catalog/forecasts/Physical/Daily_Water_temperature/models/persistenceRW.json @@ -0,0 +1,169 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "persistenceRW_Temp_C_mean_P1D_forecast", + "bbox": [ + [-79.8372, 37.3032, -79.8159, 37.3129] + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [-79.8159, 37.3129], + [-79.8372, 37.3032], + [-79.8357, 37.3078] + ] + }, + "properties": { + "title": "persistenceRW", + "description": "\nmodel info: Random walk from the fable package with ensembles used to represent uncertainty\n\nSites: bvre, fcre, tubr\n\nVariables: Daily Water_temperature", + "start_datetime": "2023-09-21", + "end_datetime": "2024-07-01", + "providers": [ + { + "url": "pending", + "name": "pending", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "https://www.ecoforecastprojectvt.org", + "name": "Ecoforecast Challenge", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Forecasting", + "vera4cast", + "Daily Water_temperature" + ], + "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, + { + "name": "datetime", + "type": "date32[day]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" + }, + { + "name": "parameter", + "type": "string", + "description": "ensemble member or distribution parameter" + }, + { + "name": "prediction", + "type": "double", + "description": "predicted value for variable" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "project_id", + "type": "string", + "description": "unique identifier for the forecast project" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + } + ] + }, + "collection": "forecasts", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "persistenceRW" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "persistenceRW" + }, + { + "rel": "self", + "href": "persistenceRW.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", + "type": "text/html", + "title": "Link for Model Code" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/persistenceRW.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/persistenceRW.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Water_temperature", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/forecasts/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} diff --git a/catalog/forecasts/Physical/Hourly_Water_temperature/collection.json b/catalog/forecasts/Physical/Hourly_Water_temperature/collection.json index 7e9d8f8286..c8b8654022 100644 --- a/catalog/forecasts/Physical/Hourly_Water_temperature/collection.json +++ b/catalog/forecasts/Physical/Hourly_Water_temperature/collection.json @@ -61,6 +61,11 @@ } }, "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -120,11 +125,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ], "assets": { diff --git a/catalog/forecasts/collection.json b/catalog/forecasts/collection.json index 87d3026403..1975399a87 100644 --- a/catalog/forecasts/collection.json +++ b/catalog/forecasts/collection.json @@ -71,12 +71,17 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2449-11-11T00:00:00Z" + "2451-11-28T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "13 columns", + "type": null, + "description": {} + }, { "name": "datetime", "type": "date32[day]", @@ -136,11 +141,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ], "assets": { diff --git a/catalog/inventory/collection.json b/catalog/inventory/collection.json index 22e4c9c15a..8e6275e7e7 100644 --- a/catalog/inventory/collection.json +++ b/catalog/inventory/collection.json @@ -58,12 +58,17 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2439-08-21T00:00:00Z" + "2451-11-28T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "14 columns", + "type": null, + "description": {} + }, { "name": "duration", "type": "string", @@ -128,11 +133,6 @@ "name": "latitude", "type": "double", "description": {} - }, - { - "name": "longitude", - "type": "double", - "description": {} } ], "assets": { diff --git a/catalog/noaa_forecasts/Pseudo/collection.json b/catalog/noaa_forecasts/Pseudo/collection.json index 07151ac86f..7d1c27f829 100644 --- a/catalog/noaa_forecasts/Pseudo/collection.json +++ b/catalog/noaa_forecasts/Pseudo/collection.json @@ -53,12 +53,17 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-05-23T00:00:00Z" + "2024-05-29T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "5 columns", + "type": null, + "description": {} + }, { "name": "parameter", "type": "double", @@ -78,11 +83,6 @@ "name": "prediction", "type": "double", "description": "predicted value for variable" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" } ], "assets": { diff --git a/catalog/noaa_forecasts/Stage1-stats/collection.json b/catalog/noaa_forecasts/Stage1-stats/collection.json index af6264d0db..06947e1873 100644 --- a/catalog/noaa_forecasts/Stage1-stats/collection.json +++ b/catalog/noaa_forecasts/Stage1-stats/collection.json @@ -53,12 +53,17 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-05-23T00:00:00Z" + "2024-05-29T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "5 columns", + "type": null, + "description": {} + }, { "name": "parameter", "type": "double", @@ -78,11 +83,6 @@ "name": "prediction", "type": "double", "description": "predicted value for variable" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" } ], "assets": { diff --git a/catalog/noaa_forecasts/Stage1/collection.json b/catalog/noaa_forecasts/Stage1/collection.json index 126adb1414..183c92ac5d 100644 --- a/catalog/noaa_forecasts/Stage1/collection.json +++ b/catalog/noaa_forecasts/Stage1/collection.json @@ -53,12 +53,17 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-05-23T00:00:00Z" + "2024-05-29T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "5 columns", + "type": null, + "description": {} + }, { "name": "parameter", "type": "double", @@ -78,11 +83,6 @@ "name": "prediction", "type": "double", "description": "predicted value for variable" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" } ], "assets": { diff --git a/catalog/noaa_forecasts/Stage2/collection.json b/catalog/noaa_forecasts/Stage2/collection.json index d49172ea72..ed804b2b2c 100644 --- a/catalog/noaa_forecasts/Stage2/collection.json +++ b/catalog/noaa_forecasts/Stage2/collection.json @@ -53,12 +53,17 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-05-23T00:00:00Z" + "2024-05-29T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "5 columns", + "type": null, + "description": {} + }, { "name": "parameter", "type": "double", @@ -78,11 +83,6 @@ "name": "prediction", "type": "double", "description": "predicted value for variable" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" } ], "assets": { diff --git a/catalog/noaa_forecasts/Stage3/collection.json b/catalog/noaa_forecasts/Stage3/collection.json index db3fc70b91..f519f5dcf7 100644 --- a/catalog/noaa_forecasts/Stage3/collection.json +++ b/catalog/noaa_forecasts/Stage3/collection.json @@ -53,12 +53,17 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-05-23T00:00:00Z" + "2024-05-29T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "5 columns", + "type": null, + "description": {} + }, { "name": "parameter", "type": "double", @@ -78,11 +83,6 @@ "name": "prediction", "type": "double", "description": "predicted value for variable" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" } ], "assets": { diff --git a/catalog/noaa_forecasts/collection.json b/catalog/noaa_forecasts/collection.json index 8a26e52804..5956aca114 100644 --- a/catalog/noaa_forecasts/collection.json +++ b/catalog/noaa_forecasts/collection.json @@ -83,12 +83,17 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-05-23T00:00:00Z" + "2024-05-29T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "5 columns", + "type": null, + "description": {} + }, { "name": "parameter", "type": "double", @@ -108,11 +113,6 @@ "name": "prediction", "type": "double", "description": "predicted value for variable" - }, - { - "name": "family", - "type": "string", - "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”" } ], "assets": { diff --git a/catalog/scores/Biological/Daily_Bloom_binary/collection.json b/catalog/scores/Biological/Daily_Bloom_binary/collection.json index fc95837bb3..6caa5e59cb 100644 --- a/catalog/scores/Biological/Daily_Bloom_binary/collection.json +++ b/catalog/scores/Biological/Daily_Bloom_binary/collection.json @@ -15,72 +15,72 @@ { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.auto.arima.json" + "href": "./models/asl.auto.arima.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.ets.json" + "href": "./models/asl.ets.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.met.lm.json" + "href": "./models/asl.met.lm.step.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.tbats.json" + "href": "./models/asl.met.lm.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.temp.lm.json" + "href": "./models/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/climatology.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/fableARIMA.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/fableETS.json" + "href": "./models/fableARIMA.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/fableNNETAR.json" + "href": "./models/fableETS.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/glm_aed_v1.json" + "href": "./models/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/historic_mean.json" + "href": "./models/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/monthly_mean.json" + "href": "./models/historic_mean.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/persistenceRW.json" + "href": "./models/monthly_mean.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.met.lm.step.json" + "href": "./models/persistenceRW.json" }, { "rel": "parent", @@ -125,12 +125,17 @@ "interval": [ [ "2023-10-01T00:00:00Z", - "2024-05-22T00:00:00Z" + "2024-05-28T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -230,11 +235,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ], "assets": { diff --git a/catalog/scores/Biological/Daily_Chlorophyll-a/collection.json b/catalog/scores/Biological/Daily_Chlorophyll-a/collection.json index 7b3e587fd2..389b63fac2 100644 --- a/catalog/scores/Biological/Daily_Chlorophyll-a/collection.json +++ b/catalog/scores/Biological/Daily_Chlorophyll-a/collection.json @@ -15,82 +15,82 @@ { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.auto.arima.json" + "href": "./models/asl.auto.arima.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.ets.json" + "href": "./models/asl.ets.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.met.lm.step.json" + "href": "./models/asl.met.lm.step.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.met.lm.json" + "href": "./models/asl.met.lm.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.tbats.json" + "href": "./models/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.temp.lm.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/climatology.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/climatology2.json" + "href": "./models/climatology2.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/fableARIMA.json" + "href": "./models/fableARIMA.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/fableETS.json" + "href": "./models/fableETS.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/fableNNETAR.json" + "href": "./models/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/gfs_seamless.json" + "href": "./models/gfs_seamless.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/glm_aed_v1.json" + "href": "./models/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/historic_mean.json" + "href": "./models/historic_mean.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/monthly_mean.json" + "href": "./models/monthly_mean.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/persistenceRW.json" + "href": "./models/persistenceRW.json" }, { "rel": "parent", @@ -135,12 +135,17 @@ "interval": [ [ "2023-10-01T00:00:00Z", - "2024-05-22T00:00:00Z" + "2024-05-28T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -240,11 +245,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ], "assets": { diff --git a/catalog/scores/Biological/collection.json b/catalog/scores/Biological/collection.json index 330982c130..f6a74f3140 100644 --- a/catalog/scores/Biological/collection.json +++ b/catalog/scores/Biological/collection.json @@ -65,12 +65,17 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2024-05-23T00:00:00Z" + "2024-05-28T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -170,11 +175,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ], "assets": { diff --git a/catalog/scores/Chemical/Daily_oxygen_concentration/collection.json b/catalog/scores/Chemical/Daily_oxygen_concentration/collection.json index 6779a17bfd..48b4b4e860 100644 --- a/catalog/scores/Chemical/Daily_oxygen_concentration/collection.json +++ b/catalog/scores/Chemical/Daily_oxygen_concentration/collection.json @@ -24,72 +24,72 @@ { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.auto.arima.json" + "href": "./models/asl.auto.arima.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.ets.json" + "href": "./models/asl.ets.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.met.lm.step.json" + "href": "./models/asl.met.lm.step.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.met.lm.json" + "href": "./models/asl.met.lm.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.tbats.json" + "href": "./models/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.temp.lm.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/climatology.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/fableNNETAR.json" + "href": "./models/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/fableNNETAR_focal.json" + "href": "./models/fableNNETAR_focal.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/gfs_seamless.json" + "href": "./models/gfs_seamless.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/glm_aed_v1.json" + "href": "./models/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/historic_mean.json" + "href": "./models/historic_mean.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/monthly_mean.json" + "href": "./models/monthly_mean.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/persistenceRW.json" + "href": "./models/persistenceRW.json" }, { "rel": "parent", @@ -134,12 +134,17 @@ "interval": [ [ "2023-10-14T00:00:00Z", - "2024-05-22T00:00:00Z" + "2024-05-28T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -239,11 +244,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ], "assets": { diff --git a/catalog/scores/Chemical/collection.json b/catalog/scores/Chemical/collection.json index dbad9ac34f..a4be0c1245 100644 --- a/catalog/scores/Chemical/collection.json +++ b/catalog/scores/Chemical/collection.json @@ -69,12 +69,17 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2024-05-23T00:00:00Z" + "2024-05-28T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -174,11 +179,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ], "assets": { diff --git a/catalog/scores/Physical/Daily_Secchi/collection.json b/catalog/scores/Physical/Daily_Secchi/collection.json index c2396d32df..81e3f08ae7 100644 --- a/catalog/scores/Physical/Daily_Secchi/collection.json +++ b/catalog/scores/Physical/Daily_Secchi/collection.json @@ -15,82 +15,82 @@ { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.ets.json" + "href": "./models/asl.auto.arima.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.met.lm.step.json" + "href": "./models/asl.ets.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.met.lm.json" + "href": "./models/asl.met.lm.step.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.tbats.json" + "href": "./models/asl.met.lm.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.temp.lm.json" + "href": "./models/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/climatology.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/fableNNETAR.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/fableNNETAR_focal.json" + "href": "./models/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/gfs_seamless.json" + "href": "./models/fableNNETAR_focal.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/glm_aed_v1.json" + "href": "./models/gfs_seamless.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/historic_mean.json" + "href": "./models/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/monthly_mean.json" + "href": "./models/historic_mean.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/persistenceRW.json" + "href": "./models/monthly_mean.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/secchi_example_forecast.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/secchi_last3obs_mean.json" + "href": "./models/secchi_example_forecast.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.auto.arima.json" + "href": "./models/secchi_last3obs_mean.json" }, { "rel": "parent", @@ -141,6 +141,11 @@ } }, "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -240,11 +245,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ], "assets": { diff --git a/catalog/scores/Physical/Daily_Water_temperature/collection.json b/catalog/scores/Physical/Daily_Water_temperature/collection.json index 7440a40087..ea15de167b 100644 --- a/catalog/scores/Physical/Daily_Water_temperature/collection.json +++ b/catalog/scores/Physical/Daily_Water_temperature/collection.json @@ -15,122 +15,122 @@ { "rel": "item", "type": "application/json", - "href": "../../models/model_items/TESTclimatology.json" + "href": "./models/TESTclimatology.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/TempC_mean_example_forecast.json" + "href": "./models/TempC_mean_example_forecast.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/Temp_C_mean.json" + "href": "./models/Temp_C_mean.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.auto.arima.json" + "href": "./models/asl.auto.arima.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.ets.json" + "href": "./models/asl.ets.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.met.lm.step.json" + "href": "./models/asl.met.lm.step.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.met.lm.json" + "href": "./models/asl.met.lm.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.tbats.json" + "href": "./models/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.temp.lm.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/climatology.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/climatology2.json" + "href": "./models/climatology2.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/example_ID.json" + "href": "./models/example_ID.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/example_forecast.json" + "href": "./models/example_forecast.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/fableNNETAR.json" + "href": "./models/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/fableNNETAR_focal.json" + "href": "./models/fableNNETAR_focal.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/flareGOTM.json" + "href": "./models/flareGOTM.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/flareSimstrat.json" + "href": "./models/flareSimstrat.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/gfs_seamless.json" + "href": "./models/gfs_seamless.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/glm_aed_v1.json" + "href": "./models/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/historic_mean.json" + "href": "./models/historic_mean.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/inflow_gefsClimAED.json" + "href": "./models/inflow_gefsClimAED.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/monthly_mean.json" + "href": "./models/monthly_mean.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/persistenceFO.json" + "href": "./models/persistenceFO.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/persistenceRW.json" + "href": "./models/persistenceRW.json" }, { "rel": "parent", @@ -175,12 +175,17 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2024-05-22T00:00:00Z" + "2024-05-28T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -280,11 +285,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ], "assets": { diff --git a/catalog/scores/Physical/Hourly_Water_temperature/collection.json b/catalog/scores/Physical/Hourly_Water_temperature/collection.json index e4759996dc..1ce302e2b0 100644 --- a/catalog/scores/Physical/Hourly_Water_temperature/collection.json +++ b/catalog/scores/Physical/Hourly_Water_temperature/collection.json @@ -61,6 +61,11 @@ } }, "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -160,11 +165,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ], "assets": { diff --git a/catalog/scores/Physical/collection.json b/catalog/scores/Physical/collection.json index 3bdfcb3ceb..ac03c2c12b 100644 --- a/catalog/scores/Physical/collection.json +++ b/catalog/scores/Physical/collection.json @@ -70,12 +70,17 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2024-05-23T00:00:00Z" + "2024-05-28T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -175,11 +180,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ], "assets": { diff --git a/catalog/scores/collection.json b/catalog/scores/collection.json index a51ebe0813..326da3ab91 100644 --- a/catalog/scores/collection.json +++ b/catalog/scores/collection.json @@ -71,12 +71,17 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2024-05-23T00:00:00Z" + "2024-05-28T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -176,11 +181,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ], "assets": { diff --git a/catalog/scores/models/model_items/Flow_cms_mean.json b/catalog/scores/models/model_items/Flow_cms_mean.json index d8f310c6a3..0516bafd15 100644 --- a/catalog/scores/models/model_items/Flow_cms_mean.json +++ b/catalog/scores/models/model_items/Flow_cms_mean.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "Flow_cms_mean", + "id": "Flow_cms_mean_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8357, 37.3078, -79.8357, 37.3078] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8357, 37.3078] - ] + "coordinates": [] }, "properties": { + "title": "Flow_cms_mean", "description": [], - "start_datetime": "2024-02-06", - "end_datetime": "2024-03-15", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,10 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Inflow discharge" + "vera4cast" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -142,11 +145,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -198,9 +196,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Inflow discharge", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Flow_cms_mean/model_id=Flow_cms_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Flow_cms_mean/model_id=Flow_cms_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=Flow_cms_mean?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=Flow_cms_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + }, + "4": { + "type": "application/x-parquet", + "title": "Database Access for ", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=Flow_cms_mean?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=Flow_cms_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/TESTclimatology.json b/catalog/scores/models/model_items/TESTclimatology.json index 3fff99b5fc..1909c258d1 100644 --- a/catalog/scores/models/model_items/TESTclimatology.json +++ b/catalog/scores/models/model_items/TESTclimatology.json @@ -4,21 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "TESTclimatology", + "id": "TESTclimatology_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8159, 37.3129] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8159, 37.3129], - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { + "title": "TESTclimatology", "description": [], - "start_datetime": "2023-09-22", - "end_datetime": "2023-10-27", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -40,10 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Water_temperature" + "vera4cast" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -143,11 +145,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -199,9 +196,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=TESTclimatology?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=TESTclimatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=TESTclimatology?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=TESTclimatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + }, + "4": { + "type": "application/x-parquet", + "title": "Database Access for ", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=TESTclimatology?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=TESTclimatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/TempC_mean_example_forecast.json b/catalog/scores/models/model_items/TempC_mean_example_forecast.json index 611181cbcd..f8563bfb87 100644 --- a/catalog/scores/models/model_items/TempC_mean_example_forecast.json +++ b/catalog/scores/models/model_items/TempC_mean_example_forecast.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "TempC_mean_example_forecast", + "id": "TempC_mean_example_forecast_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8372, 37.3032] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { + "title": "TempC_mean_example_forecast", "description": [], - "start_datetime": "2023-11-07", - "end_datetime": "2024-05-22", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,10 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Water_temperature" + "vera4cast" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -142,11 +145,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -198,9 +196,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=TempC_mean_example_forecast?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=TempC_mean_example_forecast?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=TempC_mean_example_forecast?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=TempC_mean_example_forecast?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + }, + "4": { + "type": "application/x-parquet", + "title": "Database Access for ", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=TempC_mean_example_forecast?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=TempC_mean_example_forecast?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/Temp_C_mean.json b/catalog/scores/models/model_items/Temp_C_mean.json index 4fc0ec9252..77b04bcbf3 100644 --- a/catalog/scores/models/model_items/Temp_C_mean.json +++ b/catalog/scores/models/model_items/Temp_C_mean.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "Temp_C_mean", + "id": "Temp_C_mean_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8357, 37.3078, -79.8357, 37.3078] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8357, 37.3078] - ] + "coordinates": [] }, "properties": { + "title": "Temp_C_mean", "description": [], - "start_datetime": "2024-02-06", - "end_datetime": "2024-03-15", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,10 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Water_temperature" + "vera4cast" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -142,11 +145,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -198,9 +196,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=Temp_C_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=Temp_C_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=Temp_C_mean?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=Temp_C_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + }, + "4": { + "type": "application/x-parquet", + "title": "Database Access for ", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=Temp_C_mean?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=Temp_C_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/asl.auto.arima.json b/catalog/scores/models/model_items/asl.auto.arima.json index 5466725147..941dc9d2e5 100644 --- a/catalog/scores/models/model_items/asl.auto.arima.json +++ b/catalog/scores/models/model_items/asl.auto.arima.json @@ -4,7 +4,7 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "asl.auto.arima", + "id": "asl.auto.arima_DO_mgL_mean_P1D_forecast", "bbox": [ [-79.8372, 37.3032, -79.8159, 37.3129] ], @@ -16,9 +16,10 @@ ] }, "properties": { - "description": "\nmodel info: forecast::auto.arima() function in R, fit individually at each site/depth\n\nSites: bvre, fcre\n\nVariables: Daily Bloom_binary, Daily Chlorophyll-a, Daily Specific conductance, Daily Water_temperature, Daily oxygen_concentration, Daily Secchi, Daily Turbidity, Daily fluorescent dissolved organic matter", + "title": "asl.auto.arima", + "description": "\nmodel info: forecast::auto.arima() function in R, fit individually at each site/depth\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", "start_datetime": "2024-03-12", - "end_datetime": "2024-05-22", + "end_datetime": "2024-05-28", "providers": [ { "url": "pending", @@ -41,16 +42,14 @@ "keywords": [ "Forecasting", "vera4cast", - "Daily Bloom_binary", - "Daily Chlorophyll-a", - "Daily Specific conductance", - "Daily Water_temperature", - "Daily oxygen_concentration", - "Daily Secchi", - "Daily Turbidity", - "Daily fluorescent dissolved organic matter" + "Daily oxygen_concentration" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -150,11 +149,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -205,52 +199,10 @@ "description": "The link to the model code provided by the model submission team" }, "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Bloom_binary", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Specific conductance", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=SpCond_uScm_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=SpCond_uScm_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { "type": "application/x-parquet", "title": "Database Access for Daily oxygen_concentration", "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org", "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Turbidity", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Turbidity_FNU_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Turbidity_FNU_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily fluorescent dissolved organic matter", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/asl.ets.json b/catalog/scores/models/model_items/asl.ets.json index c89ce49af1..0064170029 100644 --- a/catalog/scores/models/model_items/asl.ets.json +++ b/catalog/scores/models/model_items/asl.ets.json @@ -4,7 +4,7 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "asl.ets", + "id": "asl.ets_DO_mgL_mean_P1D_forecast", "bbox": [ [-79.8372, 37.3032, -79.8159, 37.3129] ], @@ -16,9 +16,10 @@ ] }, "properties": { - "description": "\nmodel info: forecast::ets() function in R, fit individually at each site/depth\n\nSites: bvre, fcre\n\nVariables: Daily Bloom_binary, Daily Chlorophyll-a, Daily Secchi, Daily Specific conductance, Daily Water_temperature, Daily oxygen_concentration, Daily Turbidity, Daily fluorescent dissolved organic matter", + "title": "asl.ets", + "description": "\nmodel info: forecast::ets() function in R, fit individually at each site/depth\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", "start_datetime": "2024-03-12", - "end_datetime": "2024-05-22", + "end_datetime": "2024-05-28", "providers": [ { "url": "pending", @@ -41,16 +42,14 @@ "keywords": [ "Forecasting", "vera4cast", - "Daily Bloom_binary", - "Daily Chlorophyll-a", - "Daily Secchi", - "Daily Specific conductance", - "Daily Water_temperature", - "Daily oxygen_concentration", - "Daily Turbidity", - "Daily fluorescent dissolved organic matter" + "Daily oxygen_concentration" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -150,11 +149,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -205,52 +199,10 @@ "description": "The link to the model code provided by the model submission team" }, "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Bloom_binary", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.ets?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.ets?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Specific conductance", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=SpCond_uScm_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=SpCond_uScm_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { "type": "application/x-parquet", "title": "Database Access for Daily oxygen_concentration", "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org", "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Turbidity", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Turbidity_FNU_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Turbidity_FNU_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily fluorescent dissolved organic matter", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/asl.met.lm.json b/catalog/scores/models/model_items/asl.met.lm.json index 4ebdc81ac5..e3a4dc44c5 100644 --- a/catalog/scores/models/model_items/asl.met.lm.json +++ b/catalog/scores/models/model_items/asl.met.lm.json @@ -4,7 +4,7 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "asl.met.lm", + "id": "asl.met.lm_DO_mgL_mean_P1D_forecast", "bbox": [ [-79.8372, 37.3032, -79.8159, 37.3129] ], @@ -16,9 +16,10 @@ ] }, "properties": { - "description": "\nmodel info: Linear regression with air temp, humidity, precip, and wind speed\n\nSites: bvre, fcre\n\nVariables: Daily Bloom_binary, Daily Chlorophyll-a, Daily Secchi, Daily Specific conductance, Daily Water_temperature, Daily oxygen_concentration, Daily Turbidity, Daily fluorescent dissolved organic matter", + "title": "asl.met.lm", + "description": "\nmodel info: Linear regression with air temp, humidity, precip, and wind speed\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", "start_datetime": "2024-05-06", - "end_datetime": "2024-05-22", + "end_datetime": "2024-05-28", "providers": [ { "url": "pending", @@ -41,16 +42,14 @@ "keywords": [ "Forecasting", "vera4cast", - "Daily Bloom_binary", - "Daily Chlorophyll-a", - "Daily Secchi", - "Daily Specific conductance", - "Daily Water_temperature", - "Daily oxygen_concentration", - "Daily Turbidity", - "Daily fluorescent dissolved organic matter" + "Daily oxygen_concentration" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -150,11 +149,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -205,52 +199,10 @@ "description": "The link to the model code provided by the model submission team" }, "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Bloom_binary", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Specific conductance", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=SpCond_uScm_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=SpCond_uScm_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { "type": "application/x-parquet", "title": "Database Access for Daily oxygen_concentration", "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org", "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Turbidity", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Turbidity_FNU_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Turbidity_FNU_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily fluorescent dissolved organic matter", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/asl.met.lm.step.json b/catalog/scores/models/model_items/asl.met.lm.step.json index ef12a1b5ea..fb6dc8cc4f 100644 --- a/catalog/scores/models/model_items/asl.met.lm.step.json +++ b/catalog/scores/models/model_items/asl.met.lm.step.json @@ -4,7 +4,7 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "asl.met.lm.step", + "id": "asl.met.lm.step_DO_mgL_mean_P1D_forecast", "bbox": [ [-79.8372, 37.3032, -79.8159, 37.3129] ], @@ -16,9 +16,10 @@ ] }, "properties": { - "description": "\nmodel info: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC\n\nSites: bvre, fcre\n\nVariables: Daily Chlorophyll-a, Daily Secchi, Daily Specific conductance, Daily Water_temperature, Daily oxygen_concentration, Daily Turbidity, Daily fluorescent dissolved organic matter, Daily Bloom_binary", + "title": "asl.met.lm.step", + "description": "\nmodel info: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", "start_datetime": "2024-05-06", - "end_datetime": "2024-05-22", + "end_datetime": "2024-05-28", "providers": [ { "url": "pending", @@ -41,16 +42,14 @@ "keywords": [ "Forecasting", "vera4cast", - "Daily Chlorophyll-a", - "Daily Secchi", - "Daily Specific conductance", - "Daily Water_temperature", - "Daily oxygen_concentration", - "Daily Turbidity", - "Daily fluorescent dissolved organic matter", - "Daily Bloom_binary" + "Daily oxygen_concentration" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -150,11 +149,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -205,52 +199,10 @@ "description": "The link to the model code provided by the model submission team" }, "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Specific conductance", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=SpCond_uScm_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=SpCond_uScm_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { "type": "application/x-parquet", "title": "Database Access for Daily oxygen_concentration", "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org", "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Turbidity", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Turbidity_FNU_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Turbidity_FNU_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily fluorescent dissolved organic matter", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily Bloom_binary", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/asl.tbats.json b/catalog/scores/models/model_items/asl.tbats.json index 044d04e670..430531389c 100644 --- a/catalog/scores/models/model_items/asl.tbats.json +++ b/catalog/scores/models/model_items/asl.tbats.json @@ -4,7 +4,7 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "asl.tbats", + "id": "asl.tbats_DO_mgL_mean_P1D_forecast", "bbox": [ [-79.8372, 37.3032, -79.8159, 37.3129] ], @@ -16,9 +16,10 @@ ] }, "properties": { - "description": "\nmodel info: forecast::tbats() function in R, fit individually at each site/depth\n\nSites: bvre, fcre\n\nVariables: Daily Bloom_binary, Daily Chlorophyll-a, Daily Secchi, Daily Specific conductance, Daily Water_temperature, Daily oxygen_concentration, Daily Turbidity, Daily fluorescent dissolved organic matter", + "title": "asl.tbats", + "description": "\nmodel info: forecast::tbats() function in R, fit individually at each site/depth\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", "start_datetime": "2024-03-20", - "end_datetime": "2024-05-22", + "end_datetime": "2024-05-28", "providers": [ { "url": "pending", @@ -41,16 +42,14 @@ "keywords": [ "Forecasting", "vera4cast", - "Daily Bloom_binary", - "Daily Chlorophyll-a", - "Daily Secchi", - "Daily Specific conductance", - "Daily Water_temperature", - "Daily oxygen_concentration", - "Daily Turbidity", - "Daily fluorescent dissolved organic matter" + "Daily oxygen_concentration" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -150,11 +149,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -205,52 +199,10 @@ "description": "The link to the model code provided by the model submission team" }, "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Bloom_binary", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Specific conductance", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=SpCond_uScm_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=SpCond_uScm_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { "type": "application/x-parquet", "title": "Database Access for Daily oxygen_concentration", "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org", "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Turbidity", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Turbidity_FNU_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Turbidity_FNU_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily fluorescent dissolved organic matter", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/asl.temp.lm.json b/catalog/scores/models/model_items/asl.temp.lm.json index 6595532e47..0ab46ccd15 100644 --- a/catalog/scores/models/model_items/asl.temp.lm.json +++ b/catalog/scores/models/model_items/asl.temp.lm.json @@ -4,7 +4,7 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "asl.temp.lm", + "id": "asl.temp.lm_DO_mgL_mean_P1D_forecast", "bbox": [ [-79.8372, 37.3032, -79.8159, 37.3129] ], @@ -16,9 +16,10 @@ ] }, "properties": { - "description": "\nmodel info: Linear regression with air temperature\n\nSites: bvre, fcre\n\nVariables: Daily Bloom_binary, Daily Chlorophyll-a, Daily Secchi, Daily Specific conductance, Daily Water_temperature, Daily oxygen_concentration, Daily Turbidity, Daily fluorescent dissolved organic matter", + "title": "asl.temp.lm", + "description": "\nmodel info: Linear regression with air temperature\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", "start_datetime": "2024-05-06", - "end_datetime": "2024-05-22", + "end_datetime": "2024-05-28", "providers": [ { "url": "pending", @@ -41,16 +42,14 @@ "keywords": [ "Forecasting", "vera4cast", - "Daily Bloom_binary", - "Daily Chlorophyll-a", - "Daily Secchi", - "Daily Specific conductance", - "Daily Water_temperature", - "Daily oxygen_concentration", - "Daily Turbidity", - "Daily fluorescent dissolved organic matter" + "Daily oxygen_concentration" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -150,11 +149,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -205,52 +199,10 @@ "description": "The link to the model code provided by the model submission team" }, "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Bloom_binary", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Specific conductance", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=SpCond_uScm_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=SpCond_uScm_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { "type": "application/x-parquet", "title": "Database Access for Daily oxygen_concentration", "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org", "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Turbidity", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Turbidity_FNU_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Turbidity_FNU_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily fluorescent dissolved organic matter", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/cfs.json b/catalog/scores/models/model_items/cfs.json index 1405980e2c..e68889e20d 100644 --- a/catalog/scores/models/model_items/cfs.json +++ b/catalog/scores/models/model_items/cfs.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "cfs", + "id": "cfs_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8372, 37.3032] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { - "description": "\nmodel info: NOAA Climate Forecasting System forecasts as downloaded from open-meteo.com. Submitted forecasts only include the first ~6 months because there are 4 ensemble members available (only 1 is available from 6 - 9 months).\n\nSites: fcre\n\nVariables: Daily Air temperature, Daily Shortwave radiation, Daily Relative humdity, Daily Precipitation, Daily Wind speed", - "start_datetime": "2023-10-13", - "end_datetime": "2024-04-01", + "title": "cfs", + "description": "\nmodel info: NOAA Climate Forecasting System forecasts as downloaded from open-meteo.com. Submitted forecasts only include the first ~6 months because there are 4 ensemble members available (only 1 is available from 6 - 9 months).\n\nSites: \n\nVariables: ", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,14 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Air temperature", - "Daily Shortwave radiation", - "Daily Relative humdity", - "Daily Precipitation", - "Daily Wind speed" + "vera4cast" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -146,11 +145,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -202,33 +196,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Air temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=cfs?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=cfs?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" }, "4": { "type": "application/x-parquet", - "title": "Database Access for Daily Shortwave radiation", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Relative humdity", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=RH_percent_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=RH_percent_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Precipitation", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Rain_mm_sum/model_id=cfs?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Rain_mm_sum/model_id=cfs?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Wind speed", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=WindSpeed_ms_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=WindSpeed_ms_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for ", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=cfs?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=cfs?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/climatology.json b/catalog/scores/models/model_items/climatology.json index d5705552bc..b92d272c5e 100644 --- a/catalog/scores/models/model_items/climatology.json +++ b/catalog/scores/models/model_items/climatology.json @@ -4,22 +4,22 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "climatology", + "id": "climatology_DO_mgL_mean_P1D_forecast", "bbox": [ [-79.8372, 37.3032, -79.8159, 37.3129] ], "geometry": { "type": "MultiPoint", "coordinates": [ - [-79.8372, 37.3032], [-79.8159, 37.3129], - [-79.8357, 37.3078] + [-79.8372, 37.3032] ] }, "properties": { - "description": "\nmodel info: Historical DOY mean and sd. Assumes normal distribution\n\nSites: fcre, bvre, tubr\n\nVariables: Daily Air temperature, Daily Bloom_binary, Daily Chlorophyll-a, Daily Secchi, Daily Water_temperature, Daily oxygen_concentration, Daily oxygen % sat, Daily Inflow discharge, Daily fluorescent dissolved organic matter", - "start_datetime": "2023-09-21", - "end_datetime": "2024-05-23", + "title": "climatology", + "description": "\nmodel info: Historical DOY mean and sd. Assumes normal distribution\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", + "start_datetime": "2024-02-06", + "end_datetime": "2024-05-28", "providers": [ { "url": "pending", @@ -42,17 +42,14 @@ "keywords": [ "Forecasting", "vera4cast", - "Daily Air temperature", - "Daily Bloom_binary", - "Daily Chlorophyll-a", - "Daily Secchi", - "Daily Water_temperature", - "Daily oxygen_concentration", - "Daily oxygen % sat", - "Daily Inflow discharge", - "Daily fluorescent dissolved organic matter" + "Daily oxygen_concentration" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -152,11 +149,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -207,58 +199,10 @@ "description": "The link to the model code provided by the model submission team" }, "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Air temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Bloom_binary", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=climatology?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=climatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { "type": "application/x-parquet", "title": "Database Access for Daily oxygen_concentration", "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org", "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily oxygen % sat", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DOsat_percent_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DOsat_percent_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily Inflow discharge", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Flow_cms_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Flow_cms_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Daily fluorescent dissolved organic matter", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/climatology2.json b/catalog/scores/models/model_items/climatology2.json index 29d2a168c4..c8c8816f52 100644 --- a/catalog/scores/models/model_items/climatology2.json +++ b/catalog/scores/models/model_items/climatology2.json @@ -4,21 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "climatology2", + "id": "climatology2_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8159, 37.3129] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8159, 37.3129], - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { - "description": "\nmodel info: Same is the other climatology\n\nSites: bvre, fcre\n\nVariables: Daily Chlorophyll-a, Daily Water_temperature", - "start_datetime": "2023-10-07", - "end_datetime": "2023-11-10", + "title": "climatology2", + "description": "\nmodel info: Same is the other climatology\n\nSites: \n\nVariables: ", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -40,11 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Chlorophyll-a", - "Daily Water_temperature" + "vera4cast" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -144,11 +145,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -200,15 +196,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=climatology2?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=climatology2?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=climatology2?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=climatology2?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" }, "4": { "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=climatology2?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=climatology2?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for ", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=climatology2?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=climatology2?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/ecmwf_ifs04.json b/catalog/scores/models/model_items/ecmwf_ifs04.json index 4aed9ea3a5..08d8352981 100644 --- a/catalog/scores/models/model_items/ecmwf_ifs04.json +++ b/catalog/scores/models/model_items/ecmwf_ifs04.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "ecmwf_ifs04", + "id": "ecmwf_ifs04_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8372, 37.3032] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { - "description": "\nmodel info: ECMWF IFS Ensemble weather model downloaded from open-meteo.com. Since ECMWF IFS Ensemble does output solar radiation on open-meteo.com, the solar radiation from GFS seamless is used.\n\nSites: fcre\n\nVariables: Daily Air temperature, Daily surface pressure, Daily Relative humdity, Daily Precipitation, Daily Wind speed", - "start_datetime": "2023-10-14", - "end_datetime": "2024-05-23", + "title": "ecmwf_ifs04", + "description": "\nmodel info: ECMWF IFS Ensemble weather model downloaded from open-meteo.com. Since ECMWF IFS Ensemble does output solar radiation on open-meteo.com, the solar radiation from GFS seamless is used.\n\nSites: \n\nVariables: ", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,14 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Air temperature", - "Daily surface pressure", - "Daily Relative humdity", - "Daily Precipitation", - "Daily Wind speed" + "vera4cast" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -146,11 +145,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -202,33 +196,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Air temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" }, "4": { "type": "application/x-parquet", - "title": "Database Access for Daily surface pressure", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=BP_kPa_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=BP_kPa_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Relative humdity", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=RH_percent_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=RH_percent_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Precipitation", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Rain_mm_sum/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Rain_mm_sum/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Wind speed", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=WindSpeed_ms_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=WindSpeed_ms_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for ", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/example_ID.json b/catalog/scores/models/model_items/example_ID.json index 426800a4cf..e05de2d381 100644 --- a/catalog/scores/models/model_items/example_ID.json +++ b/catalog/scores/models/model_items/example_ID.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "example_ID", + "id": "example_ID_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8372, 37.3032] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { + "title": "example_ID", "description": [], - "start_datetime": "2024-04-03", - "end_datetime": "2024-05-22", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,10 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Water_temperature" + "vera4cast" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -142,11 +145,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -198,9 +196,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=example_ID?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=example_ID?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=example_ID?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=example_ID?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + }, + "4": { + "type": "application/x-parquet", + "title": "Database Access for ", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=example_ID?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=example_ID?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/example_forecast.json b/catalog/scores/models/model_items/example_forecast.json index 6ff7d51fa2..302b8a3ec2 100644 --- a/catalog/scores/models/model_items/example_forecast.json +++ b/catalog/scores/models/model_items/example_forecast.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "example_forecast", + "id": "example_forecast_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8372, 37.3032] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { + "title": "example_forecast", "description": [], - "start_datetime": "2023-11-07", - "end_datetime": "2023-12-06", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,10 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Water_temperature" + "vera4cast" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -142,11 +145,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -198,9 +196,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=example_forecast?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=example_forecast?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=example_forecast?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=example_forecast?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + }, + "4": { + "type": "application/x-parquet", + "title": "Database Access for ", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=example_forecast?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=example_forecast?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/fableARIMA.json b/catalog/scores/models/model_items/fableARIMA.json index 303d4f3c95..b2abc2b993 100644 --- a/catalog/scores/models/model_items/fableARIMA.json +++ b/catalog/scores/models/model_items/fableARIMA.json @@ -4,21 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "fableARIMA", + "id": "fableARIMA_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8159, 37.3129] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8159, 37.3129], - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { - "description": "\nmodel info: ARIMA fit using the ARIMA() function in the fable R package\n\nSites: bvre, fcre\n\nVariables: Daily Bloom_binary, Daily Chlorophyll-a", - "start_datetime": "2023-10-01", - "end_datetime": "2024-05-22", + "title": "fableARIMA", + "description": "\nmodel info: ARIMA fit using the ARIMA() function in the fable R package\n\nSites: \n\nVariables: ", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -40,11 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Bloom_binary", - "Daily Chlorophyll-a" + "vera4cast" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -144,11 +145,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -200,15 +196,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Bloom_binary", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=fableARIMA?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=fableARIMA?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=fableARIMA?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=fableARIMA?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" }, "4": { "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=fableARIMA?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=fableARIMA?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for ", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=fableARIMA?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=fableARIMA?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/fableETS.json b/catalog/scores/models/model_items/fableETS.json index 3d4618291c..902264be8d 100644 --- a/catalog/scores/models/model_items/fableETS.json +++ b/catalog/scores/models/model_items/fableETS.json @@ -4,21 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "fableETS", + "id": "fableETS_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8159, 37.3129] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8159, 37.3129], - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { - "description": "\nmodel info: fable package exponential smoothing model fable::ETS()\n\nSites: bvre, fcre\n\nVariables: Daily Bloom_binary, Daily Chlorophyll-a", - "start_datetime": "2023-10-01", - "end_datetime": "2024-05-22", + "title": "fableETS", + "description": "\nmodel info: fable package exponential smoothing model fable::ETS()\n\nSites: \n\nVariables: ", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -40,11 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Bloom_binary", - "Daily Chlorophyll-a" + "vera4cast" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -144,11 +145,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -200,15 +196,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Bloom_binary", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=fableETS?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=fableETS?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=fableETS?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=fableETS?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" }, "4": { "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=fableETS?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=fableETS?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for ", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=fableETS?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=fableETS?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/fableNNETAR.json b/catalog/scores/models/model_items/fableNNETAR.json index 0abf6bdda5..f160c8329f 100644 --- a/catalog/scores/models/model_items/fableNNETAR.json +++ b/catalog/scores/models/model_items/fableNNETAR.json @@ -4,7 +4,7 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "fableNNETAR", + "id": "fableNNETAR_DO_mgL_mean_P1D_forecast", "bbox": [ [-79.8372, 37.3032, -79.8159, 37.3129] ], @@ -16,9 +16,10 @@ ] }, "properties": { - "description": "\nmodel info: autoregressive neural net fit using the NNETAR() function in the fable R package\n\nSites: bvre, fcre\n\nVariables: Daily Bloom_binary, Daily Chlorophyll-a, Daily Secchi, Daily Water_temperature, Daily oxygen_concentration, Daily fluorescent dissolved organic matter", - "start_datetime": "2023-10-01", - "end_datetime": "2024-05-22", + "title": "fableNNETAR", + "description": "\nmodel info: autoregressive neural net fit using the NNETAR() function in the fable R package\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", + "start_datetime": "2024-04-26", + "end_datetime": "2024-05-28", "providers": [ { "url": "pending", @@ -41,14 +42,14 @@ "keywords": [ "Forecasting", "vera4cast", - "Daily Bloom_binary", - "Daily Chlorophyll-a", - "Daily Secchi", - "Daily Water_temperature", - "Daily oxygen_concentration", - "Daily fluorescent dissolved organic matter" + "Daily oxygen_concentration" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -148,11 +149,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -203,40 +199,10 @@ "description": "The link to the model code provided by the model submission team" }, "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Bloom_binary", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { "type": "application/x-parquet", "title": "Database Access for Daily oxygen_concentration", "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org", "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily fluorescent dissolved organic matter", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/fableNNETAR_focal.json b/catalog/scores/models/model_items/fableNNETAR_focal.json index f4594f994c..49d2dc0cd4 100644 --- a/catalog/scores/models/model_items/fableNNETAR_focal.json +++ b/catalog/scores/models/model_items/fableNNETAR_focal.json @@ -4,7 +4,7 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "fableNNETAR_focal", + "id": "fableNNETAR_focal_DO_mgL_mean_P1D_forecast", "bbox": [ [-79.8372, 37.3032, -79.8159, 37.3129] ], @@ -16,9 +16,10 @@ ] }, "properties": { - "description": "\nmodel info: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables\n\nSites: bvre, fcre\n\nVariables: Daily Secchi, Daily Water_temperature, Daily oxygen_concentration, Daily fluorescent dissolved organic matter", + "title": "fableNNETAR_focal", + "description": "\nmodel info: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", "start_datetime": "2024-04-26", - "end_datetime": "2024-05-22", + "end_datetime": "2024-05-28", "providers": [ { "url": "pending", @@ -41,12 +42,14 @@ "keywords": [ "Forecasting", "vera4cast", - "Daily Secchi", - "Daily Water_temperature", - "Daily oxygen_concentration", - "Daily fluorescent dissolved organic matter" + "Daily oxygen_concentration" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -146,11 +149,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -201,28 +199,10 @@ "description": "The link to the model code provided by the model submission team" }, "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=fableNNETAR_focal?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=fableNNETAR_focal?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=fableNNETAR_focal?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=fableNNETAR_focal?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { "type": "application/x-parquet", "title": "Database Access for Daily oxygen_concentration", "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=fableNNETAR_focal?endpoint_override=renc.osn.xsede.org", "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=fableNNETAR_focal?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily fluorescent dissolved organic matter", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=fableNNETAR_focal?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=fableNNETAR_focal?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/flareGOTM.json b/catalog/scores/models/model_items/flareGOTM.json index 912a3c7822..426e5c90d4 100644 --- a/catalog/scores/models/model_items/flareGOTM.json +++ b/catalog/scores/models/model_items/flareGOTM.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "flareGOTM", + "id": "flareGOTM_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8372, 37.3032] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { - "description": "\nmodel info: FLARE-GOTM combines the 1D hydrodynamic process-based model GOTM, a data assimilation algorithm, and NOAA weather data to forecast water column temperatures.\n\nSites: fcre\n\nVariables: Daily Water_temperature", - "start_datetime": "2024-01-21", - "end_datetime": "2024-03-19", + "title": "flareGOTM", + "description": "\nmodel info: FLARE-GOTM combines the 1D hydrodynamic process-based model GOTM, a data assimilation algorithm, and NOAA weather data to forecast water column temperatures.\n\nSites: \n\nVariables: ", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,10 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Water_temperature" + "vera4cast" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -142,11 +145,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -198,9 +196,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=flareGOTM?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=flareGOTM?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=flareGOTM?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=flareGOTM?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + }, + "4": { + "type": "application/x-parquet", + "title": "Database Access for ", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=flareGOTM?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=flareGOTM?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/flareSimstrat.json b/catalog/scores/models/model_items/flareSimstrat.json index 5a0ade97e1..5be07ebde0 100644 --- a/catalog/scores/models/model_items/flareSimstrat.json +++ b/catalog/scores/models/model_items/flareSimstrat.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "flareSimstrat", + "id": "flareSimstrat_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8372, 37.3032] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { - "description": "\nmodel info: FLARE-Simstrat combines the 1D process-based model Simstrat, a data assimilation algorithm (EnKF) and NOAA driver weather data to make predictions of water column temperatures.\n\nSites: fcre\n\nVariables: Daily Water_temperature", - "start_datetime": "2024-01-21", - "end_datetime": "2024-03-19", + "title": "flareSimstrat", + "description": "\nmodel info: FLARE-Simstrat combines the 1D process-based model Simstrat, a data assimilation algorithm (EnKF) and NOAA driver weather data to make predictions of water column temperatures.\n\nSites: \n\nVariables: ", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,10 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Water_temperature" + "vera4cast" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -142,11 +145,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -198,9 +196,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=flareSimstrat?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=flareSimstrat?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=flareSimstrat?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=flareSimstrat?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + }, + "4": { + "type": "application/x-parquet", + "title": "Database Access for ", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=flareSimstrat?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=flareSimstrat?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/gem_global.json b/catalog/scores/models/model_items/gem_global.json index 71634a8e63..335e63678f 100644 --- a/catalog/scores/models/model_items/gem_global.json +++ b/catalog/scores/models/model_items/gem_global.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "gem_global", + "id": "gem_global_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8372, 37.3032] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { - "description": "\nmodel info: Candian GEM Global Ensemble model downloaded from open-meteo.com\n\nSites: fcre\n\nVariables: Daily Air temperature, Daily surface pressure, Daily Shortwave radiation, Daily Relative humdity, Daily Precipitation, Daily Wind speed", - "start_datetime": "2023-10-14", - "end_datetime": "2024-05-23", + "title": "gem_global", + "description": "\nmodel info: Candian GEM Global Ensemble model downloaded from open-meteo.com\n\nSites: \n\nVariables: ", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,15 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Air temperature", - "Daily surface pressure", - "Daily Shortwave radiation", - "Daily Relative humdity", - "Daily Precipitation", - "Daily Wind speed" + "vera4cast" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -147,11 +145,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -203,39 +196,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Air temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=gem_global?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=gem_global?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" }, "4": { "type": "application/x-parquet", - "title": "Database Access for Daily surface pressure", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=BP_kPa_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=BP_kPa_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Shortwave radiation", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Relative humdity", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=RH_percent_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=RH_percent_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Precipitation", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Rain_mm_sum/model_id=gem_global?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Rain_mm_sum/model_id=gem_global?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Wind speed", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=WindSpeed_ms_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=WindSpeed_ms_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for ", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=gem_global?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=gem_global?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/gfs_seamless.json b/catalog/scores/models/model_items/gfs_seamless.json index 38e211d7ed..e16a74b6ab 100644 --- a/catalog/scores/models/model_items/gfs_seamless.json +++ b/catalog/scores/models/model_items/gfs_seamless.json @@ -4,21 +4,22 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "gfs_seamless", + "id": "gfs_seamless_DO_mgL_mean_P1D_forecast", "bbox": [ [-79.8372, 37.3032, -79.8159, 37.3129] ], "geometry": { "type": "MultiPoint", "coordinates": [ - [-79.8372, 37.3032], - [-79.8159, 37.3129] + [-79.8159, 37.3129], + [-79.8372, 37.3032] ] }, "properties": { - "description": "\nmodel info: NOAA Global Ensemble Forecasting Model downloaded using the https://open-meteo.com. The seamless model combines the 0.25 and 0.5 degree resolution products to get a full 35-day ahead forecast\n\nSites: fcre, bvre\n\nVariables: Daily Air temperature, Daily surface pressure, Daily Chlorophyll-a, Daily Secchi, Daily Shortwave radiation, Daily Specific conductance, Daily Water_temperature, Daily oxygen_concentration, Daily Relative humdity, Daily Precipitation, Daily Turbidity, Daily Wind speed, Daily fluorescent dissolved organic matter", - "start_datetime": "2023-10-13", - "end_datetime": "2024-05-23", + "title": "gfs_seamless", + "description": "\nmodel info: NOAA Global Ensemble Forecasting Model downloaded using the https://open-meteo.com. The seamless model combines the 0.25 and 0.5 degree resolution products to get a full 35-day ahead forecast\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", + "start_datetime": "2024-04-09", + "end_datetime": "2024-05-28", "providers": [ { "url": "pending", @@ -41,21 +42,14 @@ "keywords": [ "Forecasting", "vera4cast", - "Daily Air temperature", - "Daily surface pressure", - "Daily Chlorophyll-a", - "Daily Secchi", - "Daily Shortwave radiation", - "Daily Specific conductance", - "Daily Water_temperature", - "Daily oxygen_concentration", - "Daily Relative humdity", - "Daily Precipitation", - "Daily Turbidity", - "Daily Wind speed", - "Daily fluorescent dissolved organic matter" + "Daily oxygen_concentration" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -155,11 +149,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -210,82 +199,10 @@ "description": "The link to the model code provided by the model submission team" }, "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Air temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily surface pressure", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=BP_kPa_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=BP_kPa_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Shortwave radiation", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Specific conductance", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=SpCond_uScm_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=SpCond_uScm_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { "type": "application/x-parquet", "title": "Database Access for Daily oxygen_concentration", "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Daily Relative humdity", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=RH_percent_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=RH_percent_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "12": { - "type": "application/x-parquet", - "title": "Database Access for Daily Precipitation", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Rain_mm_sum/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Rain_mm_sum/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "13": { - "type": "application/x-parquet", - "title": "Database Access for Daily Turbidity", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Turbidity_FNU_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Turbidity_FNU_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "14": { - "type": "application/x-parquet", - "title": "Database Access for Daily Wind speed", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=WindSpeed_ms_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=WindSpeed_ms_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "15": { - "type": "application/x-parquet", - "title": "Database Access for Daily fluorescent dissolved organic matter", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/glm_aed_v1.json b/catalog/scores/models/model_items/glm_aed_v1.json index ab848a4541..fb4c6b6d52 100644 --- a/catalog/scores/models/model_items/glm_aed_v1.json +++ b/catalog/scores/models/model_items/glm_aed_v1.json @@ -4,7 +4,7 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "glm_aed_v1", + "id": "glm_aed_v1_DO_mgL_mean_P1D_forecast", "bbox": [ [-79.8372, 37.3032, -79.8372, 37.3032] ], @@ -15,9 +15,10 @@ ] }, "properties": { - "description": "\nmodel info: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters.\n\nSites: fcre\n\nVariables: Daily Bloom_binary, Daily Dissolved methane, Daily Chlorophyll-a, Daily Secchi, Daily Water_temperature, Daily Dissolved organic carbon concentration, Daily oxygen_concentration, Daily ammonium concentration, Daily Total soluble reactive phosphorus concentration, Daily fluorescent dissolved organic matter", + "title": "glm_aed_v1", + "description": "\nmodel info: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters.\n\nSites: fcre\n\nVariables: Daily oxygen_concentration", "start_datetime": "2023-10-14", - "end_datetime": "2024-05-22", + "end_datetime": "2024-05-28", "providers": [ { "url": "pending", @@ -40,18 +41,14 @@ "keywords": [ "Forecasting", "vera4cast", - "Daily Bloom_binary", - "Daily Dissolved methane", - "Daily Chlorophyll-a", - "Daily Secchi", - "Daily Water_temperature", - "Daily Dissolved organic carbon concentration", - "Daily oxygen_concentration", - "Daily ammonium concentration", - "Daily Total soluble reactive phosphorus concentration", - "Daily fluorescent dissolved organic matter" + "Daily oxygen_concentration" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -151,11 +148,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -206,64 +198,10 @@ "description": "The link to the model code provided by the model submission team" }, "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Bloom_binary", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved methane", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=CH4_umolL_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=CH4_umolL_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved organic carbon concentration", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DIC_mgL_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DIC_mgL_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { "type": "application/x-parquet", "title": "Database Access for Daily oxygen_concentration", "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily ammonium concentration", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=NH4_ugL_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=NH4_ugL_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Daily Total soluble reactive phosphorus concentration", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=SRP_ugL_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=SRP_ugL_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "12": { - "type": "application/x-parquet", - "title": "Database Access for Daily fluorescent dissolved organic matter", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/historic_mean.json b/catalog/scores/models/model_items/historic_mean.json index 65659df938..f8f993910d 100644 --- a/catalog/scores/models/model_items/historic_mean.json +++ b/catalog/scores/models/model_items/historic_mean.json @@ -4,22 +4,22 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "historic_mean", + "id": "historic_mean_DO_mgL_mean_P1D_forecast", "bbox": [ [-79.8372, 37.3032, -79.8159, 37.3129] ], "geometry": { "type": "MultiPoint", "coordinates": [ - [-79.8372, 37.3032], [-79.8159, 37.3129], - [-79.8357, 37.3078] + [-79.8372, 37.3032] ] }, "properties": { - "description": "\nmodel info: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model.\n\nSites: fcre, bvre, tubr\n\nVariables: Daily Air temperature, Daily Bloom_binary, Daily Chlorophyll-a, Daily Secchi, Daily Water_temperature, Daily oxygen_concentration, Daily oxygen % sat, Daily Inflow discharge, Daily fluorescent dissolved organic matter", + "title": "historic_mean", + "description": "\nmodel info: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model.\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", "start_datetime": "2024-02-06", - "end_datetime": "2024-05-23", + "end_datetime": "2024-05-28", "providers": [ { "url": "pending", @@ -42,17 +42,14 @@ "keywords": [ "Forecasting", "vera4cast", - "Daily Air temperature", - "Daily Bloom_binary", - "Daily Chlorophyll-a", - "Daily Secchi", - "Daily Water_temperature", - "Daily oxygen_concentration", - "Daily oxygen % sat", - "Daily Inflow discharge", - "Daily fluorescent dissolved organic matter" + "Daily oxygen_concentration" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -152,11 +149,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -207,58 +199,10 @@ "description": "The link to the model code provided by the model submission team" }, "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Air temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Bloom_binary", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=historic_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=historic_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { "type": "application/x-parquet", "title": "Database Access for Daily oxygen_concentration", "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org", "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily oxygen % sat", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DOsat_percent_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DOsat_percent_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily Inflow discharge", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Flow_cms_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Flow_cms_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Daily fluorescent dissolved organic matter", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/icon_seamless.json b/catalog/scores/models/model_items/icon_seamless.json index 88685b56a5..65887ea976 100644 --- a/catalog/scores/models/model_items/icon_seamless.json +++ b/catalog/scores/models/model_items/icon_seamless.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "icon_seamless", + "id": "icon_seamless_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8372, 37.3032] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { - "description": "\nmodel info: The DWD Icon EPS Seamless model downloaded from open-meteo.com\n\nSites: fcre\n\nVariables: Daily Air temperature, Daily surface pressure, Daily Shortwave radiation, Daily Relative humdity, Daily Precipitation, Daily Wind speed", - "start_datetime": "2023-10-14", - "end_datetime": "2024-05-23", + "title": "icon_seamless", + "description": "\nmodel info: The DWD Icon EPS Seamless model downloaded from open-meteo.com\n\nSites: \n\nVariables: ", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,15 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Air temperature", - "Daily surface pressure", - "Daily Shortwave radiation", - "Daily Relative humdity", - "Daily Precipitation", - "Daily Wind speed" + "vera4cast" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -147,11 +145,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -203,39 +196,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Air temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" }, "4": { "type": "application/x-parquet", - "title": "Database Access for Daily surface pressure", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=BP_kPa_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=BP_kPa_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Shortwave radiation", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Relative humdity", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=RH_percent_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=RH_percent_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Precipitation", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Rain_mm_sum/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Rain_mm_sum/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Wind speed", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=WindSpeed_ms_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=WindSpeed_ms_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for ", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/inflow_gefsClimAED.json b/catalog/scores/models/model_items/inflow_gefsClimAED.json index f50d6eb6c8..b118196970 100644 --- a/catalog/scores/models/model_items/inflow_gefsClimAED.json +++ b/catalog/scores/models/model_items/inflow_gefsClimAED.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "inflow_gefsClimAED", + "id": "inflow_gefsClimAED_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8357, 37.3078, -79.8357, 37.3078] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8357, 37.3078] - ] + "coordinates": [] }, "properties": { - "description": "\nmodel info: flow is forecasted as using a linear relationship between historical flow, month, and 5-day sum of precipitation. Temperature is forecasted using a linear relationship between historical water temperature, month, and 5-day mean air temperature. NOAA GEFS is then used to get the future values of 5-day sum precipitation and mean temperature. Nutrients are forecasting using the DOY climatology. The DOY climatology was developed using a linear interpolation of the historical samples.\n\nSites: tubr\n\nVariables: Daily Dissolved methane, Daily Total nitrogen concentration, Daily Total phosphorus concentration, Daily Water_temperature, Daily Dissolved organic carbon concentration, Daily dissolved organic carbon concentration, Daily Inflow discharge, Daily ammonium concentration, Daily Nitrate concentration, Daily Total soluble reactive phosphorus concentration", - "start_datetime": "2023-10-13", - "end_datetime": "2024-05-22", + "title": "inflow_gefsClimAED", + "description": "\nmodel info: flow is forecasted as using a linear relationship between historical flow, month, and 5-day sum of precipitation. Temperature is forecasted using a linear relationship between historical water temperature, month, and 5-day mean air temperature. NOAA GEFS is then used to get the future values of 5-day sum precipitation and mean temperature. Nutrients are forecasting using the DOY climatology. The DOY climatology was developed using a linear interpolation of the historical samples.\n\nSites: \n\nVariables: ", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,19 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Dissolved methane", - "Daily Total nitrogen concentration", - "Daily Total phosphorus concentration", - "Daily Water_temperature", - "Daily Dissolved organic carbon concentration", - "Daily dissolved organic carbon concentration", - "Daily Inflow discharge", - "Daily ammonium concentration", - "Daily Nitrate concentration", - "Daily Total soluble reactive phosphorus concentration" + "vera4cast" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -151,11 +145,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -207,63 +196,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved methane", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=CH4_umolL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=CH4_umolL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" }, "4": { "type": "application/x-parquet", - "title": "Database Access for Daily Total nitrogen concentration", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=TN_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=TN_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Total phosphorus concentration", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=TP_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=TP_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved organic carbon concentration", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DIC_mgL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DIC_mgL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily dissolved organic carbon concentration", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DOC_mgL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DOC_mgL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Inflow discharge", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Flow_cms_mean/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Flow_cms_mean/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily ammonium concentration", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=NH4_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=NH4_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Daily Nitrate concentration", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=NO3NO2_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=NO3NO2_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "12": { - "type": "application/x-parquet", - "title": "Database Access for Daily Total soluble reactive phosphorus concentration", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=SRP_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=SRP_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for ", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/monthly_mean.json b/catalog/scores/models/model_items/monthly_mean.json index bec50a200d..d678795fdd 100644 --- a/catalog/scores/models/model_items/monthly_mean.json +++ b/catalog/scores/models/model_items/monthly_mean.json @@ -4,22 +4,22 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "monthly_mean", + "id": "monthly_mean_DO_mgL_mean_P1D_forecast", "bbox": [ [-79.8372, 37.3032, -79.8159, 37.3129] ], "geometry": { "type": "MultiPoint", "coordinates": [ - [-79.8372, 37.3032], [-79.8159, 37.3129], - [-79.8357, 37.3078] + [-79.8372, 37.3032] ] }, "properties": { - "description": "\nmodel info: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast.\n\nSites: fcre, bvre, tubr\n\nVariables: Daily Air temperature, Daily Bloom_binary, Daily Chlorophyll-a, Daily Secchi, Daily Water_temperature, Daily oxygen_concentration, Daily oxygen % sat, Daily Inflow discharge, Daily fluorescent dissolved organic matter", + "title": "monthly_mean", + "description": "\nmodel info: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast.\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", "start_datetime": "2024-02-06", - "end_datetime": "2024-05-23", + "end_datetime": "2024-05-28", "providers": [ { "url": "pending", @@ -42,17 +42,14 @@ "keywords": [ "Forecasting", "vera4cast", - "Daily Air temperature", - "Daily Bloom_binary", - "Daily Chlorophyll-a", - "Daily Secchi", - "Daily Water_temperature", - "Daily oxygen_concentration", - "Daily oxygen % sat", - "Daily Inflow discharge", - "Daily fluorescent dissolved organic matter" + "Daily oxygen_concentration" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -152,11 +149,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -207,58 +199,10 @@ "description": "The link to the model code provided by the model submission team" }, "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Air temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Bloom_binary", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { "type": "application/x-parquet", "title": "Database Access for Daily oxygen_concentration", "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org", "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily oxygen % sat", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DOsat_percent_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DOsat_percent_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily Inflow discharge", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Flow_cms_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Flow_cms_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Daily fluorescent dissolved organic matter", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/persistenceFO.json b/catalog/scores/models/model_items/persistenceFO.json index 06e2db71f6..4ef74a012b 100644 --- a/catalog/scores/models/model_items/persistenceFO.json +++ b/catalog/scores/models/model_items/persistenceFO.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "persistenceFO", + "id": "persistenceFO_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8159, 37.3129, -79.8159, 37.3129] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8159, 37.3129] - ] + "coordinates": [] }, "properties": { - "description": "\nmodel info: another persistence forecast\n\nSites: bvre\n\nVariables: Daily Water_temperature", - "start_datetime": "2023-09-27", - "end_datetime": "2023-10-30", + "title": "persistenceFO", + "description": "\nmodel info: another persistence forecast\n\nSites: \n\nVariables: ", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,10 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Water_temperature" + "vera4cast" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -142,11 +145,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -198,9 +196,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=persistenceFO?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=persistenceFO?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=persistenceFO?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=persistenceFO?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + }, + "4": { + "type": "application/x-parquet", + "title": "Database Access for ", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=persistenceFO?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=persistenceFO?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/persistenceRW.json b/catalog/scores/models/model_items/persistenceRW.json index ab927b97fd..98266d40f8 100644 --- a/catalog/scores/models/model_items/persistenceRW.json +++ b/catalog/scores/models/model_items/persistenceRW.json @@ -4,22 +4,22 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "persistenceRW", + "id": "persistenceRW_DO_mgL_mean_P1D_forecast", "bbox": [ [-79.8372, 37.3032, -79.8159, 37.3129] ], "geometry": { "type": "MultiPoint", "coordinates": [ - [-79.8372, 37.3032], [-79.8159, 37.3129], - [-79.8357, 37.3078] + [-79.8372, 37.3032] ] }, "properties": { - "description": "\nmodel info: Random walk from the fable package with ensembles used to represent uncertainty\n\nSites: fcre, bvre, tubr\n\nVariables: Daily Air temperature, Daily Bloom_binary, Daily Surface methane flux, Daily Surface CO2 flux, Daily Chlorophyll-a, Daily Secchi, Daily Water_temperature, Daily oxygen_concentration, Daily oxygen % sat, Daily Inflow discharge, Daily fluorescent dissolved organic matter", - "start_datetime": "2023-09-21", - "end_datetime": "2024-05-23", + "title": "persistenceRW", + "description": "\nmodel info: Random walk from the fable package with ensembles used to represent uncertainty\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", + "start_datetime": "2024-02-06", + "end_datetime": "2024-05-28", "providers": [ { "url": "pending", @@ -42,19 +42,14 @@ "keywords": [ "Forecasting", "vera4cast", - "Daily Air temperature", - "Daily Bloom_binary", - "Daily Surface methane flux", - "Daily Surface CO2 flux", - "Daily Chlorophyll-a", - "Daily Secchi", - "Daily Water_temperature", - "Daily oxygen_concentration", - "Daily oxygen % sat", - "Daily Inflow discharge", - "Daily fluorescent dissolved organic matter" + "Daily oxygen_concentration" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -154,11 +149,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -209,70 +199,10 @@ "description": "The link to the model code provided by the model submission team" }, "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Air temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Bloom_binary", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Surface methane flux", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=CH4flux_umolm2s_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=CH4flux_umolm2s_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Surface CO2 flux", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=CO2flux_umolm2s_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=CO2flux_umolm2s_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { "type": "application/x-parquet", "title": "Database Access for Daily oxygen_concentration", "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Daily oxygen % sat", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DOsat_percent_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=DOsat_percent_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "12": { - "type": "application/x-parquet", - "title": "Database Access for Daily Inflow discharge", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Flow_cms_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Flow_cms_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "13": { - "type": "application/x-parquet", - "title": "Database Access for Daily fluorescent dissolved organic matter", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/secchi_example_forecast.json b/catalog/scores/models/model_items/secchi_example_forecast.json index c00c811fa1..761ffceed0 100644 --- a/catalog/scores/models/model_items/secchi_example_forecast.json +++ b/catalog/scores/models/model_items/secchi_example_forecast.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "secchi_example_forecast", + "id": "secchi_example_forecast_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8372, 37.3032] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { + "title": "secchi_example_forecast", "description": [], - "start_datetime": "2024-04-11", - "end_datetime": "2024-05-11", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,10 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Secchi" + "vera4cast" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -142,11 +145,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -198,9 +196,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=secchi_example_forecast?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=secchi_example_forecast?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=secchi_example_forecast?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=secchi_example_forecast?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + }, + "4": { + "type": "application/x-parquet", + "title": "Database Access for ", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=secchi_example_forecast?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=secchi_example_forecast?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/scores/models/model_items/secchi_last3obs_mean.json b/catalog/scores/models/model_items/secchi_last3obs_mean.json index 80aac85174..b0654f57c7 100644 --- a/catalog/scores/models/model_items/secchi_last3obs_mean.json +++ b/catalog/scores/models/model_items/secchi_last3obs_mean.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "secchi_last3obs_mean", + "id": "secchi_last3obs_mean_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8372, 37.3032] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { - "description": "\nmodel info: This forecast simply takes the mean of the last three secchi observations and uses the standard deviation of that mean for the uncertainty around the forecast.\n\nSites: fcre\n\nVariables: Daily Secchi", - "start_datetime": "2024-05-02", - "end_datetime": "2024-05-20", + "title": "secchi_last3obs_mean", + "description": "\nmodel info: This forecast simply takes the mean of the last three secchi observations and uses the standard deviation of that mean for the uncertainty around the forecast.\n\nSites: \n\nVariables: ", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,10 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Secchi" + "vera4cast" ], "table:columns": [ + { + "name": "21 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -142,11 +145,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "date", - "type": "string", - "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period." } ] }, @@ -198,9 +196,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=secchi_last3obs_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=secchi_last3obs_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=secchi_last3obs_mean?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=NA/duration=NA/variable=NA/model_id=secchi_last3obs_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + }, + "4": { + "type": "application/x-parquet", + "title": "Database Access for ", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=secchi_last3obs_mean?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/project_id=/duration=/variable=/model_id=secchi_last3obs_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/Biological/Daily_Bloom_binary/collection.json b/catalog/summaries/Biological/Daily_Bloom_binary/collection.json index bcf1c1a4a9..031f620b6c 100644 --- a/catalog/summaries/Biological/Daily_Bloom_binary/collection.json +++ b/catalog/summaries/Biological/Daily_Bloom_binary/collection.json @@ -15,72 +15,72 @@ { "rel": "item", "type": "application/json", - "href": "../../models/model_items/fableARIMA.json" + "href": "./models/fableARIMA.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/fableETS.json" + "href": "./models/fableETS.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/fableNNETAR.json" + "href": "./models/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/glm_aed_v1.json" + "href": "./models/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/climatology.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/historic_mean.json" + "href": "./models/historic_mean.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/monthly_mean.json" + "href": "./models/monthly_mean.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/persistenceRW.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.auto.arima.json" + "href": "./models/asl.auto.arima.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.ets.json" + "href": "./models/asl.ets.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.tbats.json" + "href": "./models/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.met.lm.json" + "href": "./models/asl.met.lm.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.met.lm.step.json" + "href": "./models/asl.met.lm.step.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.temp.lm.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "parent", @@ -125,12 +125,17 @@ "interval": [ [ "2023-10-01T00:00:00Z", - "2024-06-27T00:00:00Z" + "2024-07-03T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -215,11 +220,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ], "assets": { diff --git a/catalog/summaries/Biological/Daily_Chlorophyll-a/collection.json b/catalog/summaries/Biological/Daily_Chlorophyll-a/collection.json index 666a058765..dd9c1aed02 100644 --- a/catalog/summaries/Biological/Daily_Chlorophyll-a/collection.json +++ b/catalog/summaries/Biological/Daily_Chlorophyll-a/collection.json @@ -15,82 +15,82 @@ { "rel": "item", "type": "application/json", - "href": "../../models/model_items/climatology.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/fableARIMA.json" + "href": "./models/fableARIMA.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/fableETS.json" + "href": "./models/fableETS.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/fableNNETAR.json" + "href": "./models/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/glm_aed_v1.json" + "href": "./models/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/persistenceRW.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/climatology2.json" + "href": "./models/climatology2.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/historic_mean.json" + "href": "./models/historic_mean.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/monthly_mean.json" + "href": "./models/monthly_mean.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.auto.arima.json" + "href": "./models/asl.auto.arima.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.ets.json" + "href": "./models/asl.ets.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.tbats.json" + "href": "./models/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/gfs_seamless.json" + "href": "./models/gfs_seamless.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.met.lm.json" + "href": "./models/asl.met.lm.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.met.lm.step.json" + "href": "./models/asl.met.lm.step.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.temp.lm.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "parent", @@ -135,12 +135,17 @@ "interval": [ [ "2023-10-01T00:00:00Z", - "2024-06-27T00:00:00Z" + "2024-07-03T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -225,11 +230,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ], "assets": { diff --git a/catalog/summaries/Biological/collection.json b/catalog/summaries/Biological/collection.json index 7a163ac70b..ec2badd8e8 100644 --- a/catalog/summaries/Biological/collection.json +++ b/catalog/summaries/Biological/collection.json @@ -65,12 +65,17 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2439-08-21T00:00:00Z" + "2451-11-28T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -155,11 +160,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ], "assets": { diff --git a/catalog/summaries/Chemical/Daily_oxygen_concentration/collection.json b/catalog/summaries/Chemical/Daily_oxygen_concentration/collection.json index ad98ee6f02..7304aa2d59 100644 --- a/catalog/summaries/Chemical/Daily_oxygen_concentration/collection.json +++ b/catalog/summaries/Chemical/Daily_oxygen_concentration/collection.json @@ -24,72 +24,72 @@ { "rel": "item", "type": "application/json", - "href": "../../models/model_items/glm_aed_v1.json" + "href": "./models/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/climatology.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/historic_mean.json" + "href": "./models/historic_mean.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/monthly_mean.json" + "href": "./models/monthly_mean.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/persistenceRW.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.auto.arima.json" + "href": "./models/asl.auto.arima.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.ets.json" + "href": "./models/asl.ets.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.tbats.json" + "href": "./models/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/gfs_seamless.json" + "href": "./models/gfs_seamless.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/fableNNETAR.json" + "href": "./models/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/fableNNETAR_focal.json" + "href": "./models/fableNNETAR_focal.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.met.lm.json" + "href": "./models/asl.met.lm.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.met.lm.step.json" + "href": "./models/asl.met.lm.step.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.temp.lm.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "parent", @@ -134,12 +134,17 @@ "interval": [ [ "2023-10-14T00:00:00Z", - "2024-06-27T00:00:00Z" + "2024-07-03T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -224,11 +229,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ], "assets": { diff --git a/catalog/summaries/Chemical/collection.json b/catalog/summaries/Chemical/collection.json index b79fc0fd07..3ea28fbfa5 100644 --- a/catalog/summaries/Chemical/collection.json +++ b/catalog/summaries/Chemical/collection.json @@ -69,12 +69,17 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2439-08-21T00:00:00Z" + "2451-11-28T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -159,11 +164,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ], "assets": { diff --git a/catalog/summaries/Physical/Daily_Secchi/collection.json b/catalog/summaries/Physical/Daily_Secchi/collection.json index 37a159098f..b10449babc 100644 --- a/catalog/summaries/Physical/Daily_Secchi/collection.json +++ b/catalog/summaries/Physical/Daily_Secchi/collection.json @@ -15,82 +15,82 @@ { "rel": "item", "type": "application/json", - "href": "../../models/model_items/glm_aed_v1.json" + "href": "./models/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/climatology.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/historic_mean.json" + "href": "./models/historic_mean.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/monthly_mean.json" + "href": "./models/monthly_mean.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/persistenceRW.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.auto.arima.json" + "href": "./models/asl.auto.arima.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.ets.json" + "href": "./models/asl.ets.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.tbats.json" + "href": "./models/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/gfs_seamless.json" + "href": "./models/gfs_seamless.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/secchi_example_forecast.json" + "href": "./models/secchi_example_forecast.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/fableNNETAR.json" + "href": "./models/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/fableNNETAR_focal.json" + "href": "./models/fableNNETAR_focal.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/secchi_last3obs_mean.json" + "href": "./models/secchi_last3obs_mean.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.met.lm.json" + "href": "./models/asl.met.lm.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.met.lm.step.json" + "href": "./models/asl.met.lm.step.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.temp.lm.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "parent", @@ -135,12 +135,17 @@ "interval": [ [ "2023-10-14T00:00:00Z", - "2024-06-27T00:00:00Z" + "2024-07-03T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -225,11 +230,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ], "assets": { diff --git a/catalog/summaries/Physical/Daily_Water_temperature/collection.json b/catalog/summaries/Physical/Daily_Water_temperature/collection.json index 1670911f54..0ea890a029 100644 --- a/catalog/summaries/Physical/Daily_Water_temperature/collection.json +++ b/catalog/summaries/Physical/Daily_Water_temperature/collection.json @@ -15,122 +15,122 @@ { "rel": "item", "type": "application/json", - "href": "../../models/model_items/climatology.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/glm_aed_v1.json" + "href": "./models/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/inflow_gefsClimAED.json" + "href": "./models/inflow_gefsClimAED.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/persistenceRW.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/TESTclimatology.json" + "href": "./models/TESTclimatology.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/example_forecast.json" + "href": "./models/example_forecast.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/TempC_mean_example_forecast.json" + "href": "./models/TempC_mean_example_forecast.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/climatology2.json" + "href": "./models/climatology2.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/persistenceFO.json" + "href": "./models/persistenceFO.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/Temp_C_mean.json" + "href": "./models/Temp_C_mean.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/historic_mean.json" + "href": "./models/historic_mean.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/monthly_mean.json" + "href": "./models/monthly_mean.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/flareGOTM.json" + "href": "./models/flareGOTM.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/flareSimstrat.json" + "href": "./models/flareSimstrat.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.auto.arima.json" + "href": "./models/asl.auto.arima.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.ets.json" + "href": "./models/asl.ets.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.tbats.json" + "href": "./models/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/example_ID.json" + "href": "./models/example_ID.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/gfs_seamless.json" + "href": "./models/gfs_seamless.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/fableNNETAR.json" + "href": "./models/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/fableNNETAR_focal.json" + "href": "./models/fableNNETAR_focal.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.met.lm.json" + "href": "./models/asl.met.lm.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.met.lm.step.json" + "href": "./models/asl.met.lm.step.json" }, { "rel": "item", "type": "application/json", - "href": "../../models/model_items/asl.temp.lm.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "parent", @@ -175,12 +175,17 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2024-06-27T00:00:00Z" + "2024-07-03T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -265,11 +270,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ], "assets": { diff --git a/catalog/summaries/Physical/Hourly_Water_temperature/collection.json b/catalog/summaries/Physical/Hourly_Water_temperature/collection.json index 6341e97a1e..ba15d37e88 100644 --- a/catalog/summaries/Physical/Hourly_Water_temperature/collection.json +++ b/catalog/summaries/Physical/Hourly_Water_temperature/collection.json @@ -61,6 +61,11 @@ } }, "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -145,11 +150,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ], "assets": { diff --git a/catalog/summaries/Physical/collection.json b/catalog/summaries/Physical/collection.json index 046fced3db..c8c918f74d 100644 --- a/catalog/summaries/Physical/collection.json +++ b/catalog/summaries/Physical/collection.json @@ -70,12 +70,17 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2439-08-21T00:00:00Z" + "2451-11-28T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -160,11 +165,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ], "assets": { diff --git a/catalog/summaries/collection.json b/catalog/summaries/collection.json index fcd3c43f1b..fbd4af8f21 100644 --- a/catalog/summaries/collection.json +++ b/catalog/summaries/collection.json @@ -71,12 +71,17 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2439-08-21T00:00:00Z" + "2451-11-28T00:00:00Z" ] ] } }, "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -161,11 +166,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ], "assets": { diff --git a/catalog/summaries/models/model_items/Flow_cms_mean.json b/catalog/summaries/models/model_items/Flow_cms_mean.json index 2da085f83d..6f888f7110 100644 --- a/catalog/summaries/models/model_items/Flow_cms_mean.json +++ b/catalog/summaries/models/model_items/Flow_cms_mean.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "Flow_cms_mean", + "id": "Flow_cms_mean_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8357, 37.3078, -79.8357, 37.3078] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8357, 37.3078] - ] + "coordinates": [] }, "properties": { + "title": "Flow_cms_mean", "description": [], - "start_datetime": "2024-02-06", - "end_datetime": "2024-03-15", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,10 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Inflow discharge" + "vera4cast" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -127,11 +130,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -183,9 +181,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Inflow discharge", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Flow_cms_mean/model_id=Flow_cms_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Flow_cms_mean/model_id=Flow_cms_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=Flow_cms_mean?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=Flow_cms_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + }, + "4": { + "type": "application/x-parquet", + "title": "Database Access for ", + "href": "s3://anonymous@project_id=/duration=/variable=/model_id=Flow_cms_mean?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=/duration=/variable=/model_id=Flow_cms_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/TESTclimatology.json b/catalog/summaries/models/model_items/TESTclimatology.json index 7f470821c0..91aeb6146d 100644 --- a/catalog/summaries/models/model_items/TESTclimatology.json +++ b/catalog/summaries/models/model_items/TESTclimatology.json @@ -4,21 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "TESTclimatology", + "id": "TESTclimatology_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8159, 37.3129] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8159, 37.3129], - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { + "title": "TESTclimatology", "description": [], - "start_datetime": "2023-09-22", - "end_datetime": "2023-10-27", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -40,10 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Water_temperature" + "vera4cast" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -128,11 +130,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -184,9 +181,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=TESTclimatology?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=TESTclimatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=TESTclimatology?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=TESTclimatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + }, + "4": { + "type": "application/x-parquet", + "title": "Database Access for ", + "href": "s3://anonymous@project_id=/duration=/variable=/model_id=TESTclimatology?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=/duration=/variable=/model_id=TESTclimatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/TempC_mean_example_forecast.json b/catalog/summaries/models/model_items/TempC_mean_example_forecast.json index 4fc2af33a1..9d875c4608 100644 --- a/catalog/summaries/models/model_items/TempC_mean_example_forecast.json +++ b/catalog/summaries/models/model_items/TempC_mean_example_forecast.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "TempC_mean_example_forecast", + "id": "TempC_mean_example_forecast_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8372, 37.3032] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { + "title": "TempC_mean_example_forecast", "description": [], - "start_datetime": "2023-11-07", - "end_datetime": "2024-06-14", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,10 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Water_temperature" + "vera4cast" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -127,11 +130,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -183,9 +181,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=TempC_mean_example_forecast?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=TempC_mean_example_forecast?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=TempC_mean_example_forecast?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=TempC_mean_example_forecast?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + }, + "4": { + "type": "application/x-parquet", + "title": "Database Access for ", + "href": "s3://anonymous@project_id=/duration=/variable=/model_id=TempC_mean_example_forecast?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=/duration=/variable=/model_id=TempC_mean_example_forecast?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/Temp_C_mean.json b/catalog/summaries/models/model_items/Temp_C_mean.json index 5ccb0810d2..74659d5c19 100644 --- a/catalog/summaries/models/model_items/Temp_C_mean.json +++ b/catalog/summaries/models/model_items/Temp_C_mean.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "Temp_C_mean", + "id": "Temp_C_mean_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8357, 37.3078, -79.8357, 37.3078] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8357, 37.3078] - ] + "coordinates": [] }, "properties": { + "title": "Temp_C_mean", "description": [], - "start_datetime": "2024-02-06", - "end_datetime": "2024-03-15", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,10 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Water_temperature" + "vera4cast" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -127,11 +130,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -183,9 +181,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=Temp_C_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=Temp_C_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=Temp_C_mean?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=Temp_C_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + }, + "4": { + "type": "application/x-parquet", + "title": "Database Access for ", + "href": "s3://anonymous@project_id=/duration=/variable=/model_id=Temp_C_mean?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=/duration=/variable=/model_id=Temp_C_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/asl.auto.arima.json b/catalog/summaries/models/model_items/asl.auto.arima.json index a729c0c011..999af44ed6 100644 --- a/catalog/summaries/models/model_items/asl.auto.arima.json +++ b/catalog/summaries/models/model_items/asl.auto.arima.json @@ -4,7 +4,7 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "asl.auto.arima", + "id": "asl.auto.arima_DO_mgL_mean_P1D_forecast", "bbox": [ [-79.8372, 37.3032, -79.8159, 37.3129] ], @@ -16,9 +16,10 @@ ] }, "properties": { - "description": "\nmodel info: forecast::auto.arima() function in R, fit individually at each site/depth\n\nSites: bvre, fcre\n\nVariables: Daily Water_temperature, Daily Chlorophyll-a, Daily fluorescent dissolved organic matter, Daily oxygen_concentration, Daily Secchi, Daily Turbidity, Daily Specific conductance, Daily Bloom_binary, Daily Dissolved methane", + "title": "asl.auto.arima", + "description": "\nmodel info: forecast::auto.arima() function in R, fit individually at each site/depth\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", "start_datetime": "2024-03-12", - "end_datetime": "2024-06-26", + "end_datetime": "2024-07-02", "providers": [ { "url": "pending", @@ -41,17 +42,14 @@ "keywords": [ "Forecasting", "vera4cast", - "Daily Water_temperature", - "Daily Chlorophyll-a", - "Daily fluorescent dissolved organic matter", - "Daily oxygen_concentration", - "Daily Secchi", - "Daily Turbidity", - "Daily Specific conductance", - "Daily Bloom_binary", - "Daily Dissolved methane" + "Daily oxygen_concentration" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -136,11 +134,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -191,58 +184,10 @@ "description": "The link to the model code provided by the model submission team" }, "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily fluorescent dissolved organic matter", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { "type": "application/x-parquet", "title": "Database Access for Daily oxygen_concentration", "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org", "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Turbidity", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Turbidity_FNU_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Turbidity_FNU_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Specific conductance", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=SpCond_uScm_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=SpCond_uScm_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily Bloom_binary", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved methane", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=CH4_umolL_sample/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=CH4_umolL_sample/model_id=asl.auto.arima?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/asl.ets.json b/catalog/summaries/models/model_items/asl.ets.json index c8e48251d1..8a66b1cf69 100644 --- a/catalog/summaries/models/model_items/asl.ets.json +++ b/catalog/summaries/models/model_items/asl.ets.json @@ -4,7 +4,7 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "asl.ets", + "id": "asl.ets_DO_mgL_mean_P1D_forecast", "bbox": [ [-79.8372, 37.3032, -79.8159, 37.3129] ], @@ -16,9 +16,10 @@ ] }, "properties": { - "description": "\nmodel info: forecast::ets() function in R, fit individually at each site/depth\n\nSites: bvre, fcre\n\nVariables: Daily Water_temperature, Daily Chlorophyll-a, Daily fluorescent dissolved organic matter, Daily oxygen_concentration, Daily Secchi, Daily Turbidity, Daily Specific conductance, Daily Bloom_binary, Daily Dissolved methane", + "title": "asl.ets", + "description": "\nmodel info: forecast::ets() function in R, fit individually at each site/depth\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", "start_datetime": "2024-03-12", - "end_datetime": "2024-06-26", + "end_datetime": "2024-07-02", "providers": [ { "url": "pending", @@ -41,17 +42,14 @@ "keywords": [ "Forecasting", "vera4cast", - "Daily Water_temperature", - "Daily Chlorophyll-a", - "Daily fluorescent dissolved organic matter", - "Daily oxygen_concentration", - "Daily Secchi", - "Daily Turbidity", - "Daily Specific conductance", - "Daily Bloom_binary", - "Daily Dissolved methane" + "Daily oxygen_concentration" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -136,11 +134,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -191,58 +184,10 @@ "description": "The link to the model code provided by the model submission team" }, "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily fluorescent dissolved organic matter", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { "type": "application/x-parquet", "title": "Database Access for Daily oxygen_concentration", "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org", "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.ets?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.ets?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Turbidity", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Turbidity_FNU_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Turbidity_FNU_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Specific conductance", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=SpCond_uScm_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=SpCond_uScm_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily Bloom_binary", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.ets?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved methane", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=CH4_umolL_sample/model_id=asl.ets?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=CH4_umolL_sample/model_id=asl.ets?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/asl.met.lm.json b/catalog/summaries/models/model_items/asl.met.lm.json index 5673efff61..8eda38d113 100644 --- a/catalog/summaries/models/model_items/asl.met.lm.json +++ b/catalog/summaries/models/model_items/asl.met.lm.json @@ -4,7 +4,7 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "asl.met.lm", + "id": "asl.met.lm_DO_mgL_mean_P1D_forecast", "bbox": [ [-79.8372, 37.3032, -79.8159, 37.3129] ], @@ -16,9 +16,10 @@ ] }, "properties": { - "description": "\nmodel info: Linear regression with air temp, humidity, precip, and wind speed\n\nSites: bvre, fcre\n\nVariables: Daily Water_temperature, Daily Chlorophyll-a, Daily fluorescent dissolved organic matter, Daily oxygen_concentration, Daily Secchi, Daily Turbidity, Daily Specific conductance, Daily Bloom_binary, Daily Dissolved methane", + "title": "asl.met.lm", + "description": "\nmodel info: Linear regression with air temp, humidity, precip, and wind speed\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", "start_datetime": "2024-05-06", - "end_datetime": "2024-06-25", + "end_datetime": "2024-06-27", "providers": [ { "url": "pending", @@ -41,17 +42,14 @@ "keywords": [ "Forecasting", "vera4cast", - "Daily Water_temperature", - "Daily Chlorophyll-a", - "Daily fluorescent dissolved organic matter", - "Daily oxygen_concentration", - "Daily Secchi", - "Daily Turbidity", - "Daily Specific conductance", - "Daily Bloom_binary", - "Daily Dissolved methane" + "Daily oxygen_concentration" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -136,11 +134,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -191,58 +184,10 @@ "description": "The link to the model code provided by the model submission team" }, "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily fluorescent dissolved organic matter", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { "type": "application/x-parquet", "title": "Database Access for Daily oxygen_concentration", "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org", "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Turbidity", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Turbidity_FNU_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Turbidity_FNU_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Specific conductance", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=SpCond_uScm_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=SpCond_uScm_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily Bloom_binary", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved methane", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=CH4_umolL_sample/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=CH4_umolL_sample/model_id=asl.met.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/asl.met.lm.step.json b/catalog/summaries/models/model_items/asl.met.lm.step.json index 2fc1689293..5ca77dd90c 100644 --- a/catalog/summaries/models/model_items/asl.met.lm.step.json +++ b/catalog/summaries/models/model_items/asl.met.lm.step.json @@ -4,7 +4,7 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "asl.met.lm.step", + "id": "asl.met.lm.step_DO_mgL_mean_P1D_forecast", "bbox": [ [-79.8372, 37.3032, -79.8159, 37.3129] ], @@ -16,7 +16,8 @@ ] }, "properties": { - "description": "\nmodel info: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC\n\nSites: bvre, fcre\n\nVariables: Daily Water_temperature, Daily Chlorophyll-a, Daily fluorescent dissolved organic matter, Daily oxygen_concentration, Daily Secchi, Daily Turbidity, Daily Specific conductance, Daily Bloom_binary, Daily Dissolved methane", + "title": "asl.met.lm.step", + "description": "\nmodel info: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", "start_datetime": "2024-05-06", "end_datetime": "2024-06-19", "providers": [ @@ -41,17 +42,14 @@ "keywords": [ "Forecasting", "vera4cast", - "Daily Water_temperature", - "Daily Chlorophyll-a", - "Daily fluorescent dissolved organic matter", - "Daily oxygen_concentration", - "Daily Secchi", - "Daily Turbidity", - "Daily Specific conductance", - "Daily Bloom_binary", - "Daily Dissolved methane" + "Daily oxygen_concentration" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -136,11 +134,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -191,58 +184,10 @@ "description": "The link to the model code provided by the model submission team" }, "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily fluorescent dissolved organic matter", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { "type": "application/x-parquet", "title": "Database Access for Daily oxygen_concentration", "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org", "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Turbidity", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Turbidity_FNU_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Turbidity_FNU_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Specific conductance", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=SpCond_uScm_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=SpCond_uScm_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily Bloom_binary", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved methane", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=CH4_umolL_sample/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=CH4_umolL_sample/model_id=asl.met.lm.step?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/asl.tbats.json b/catalog/summaries/models/model_items/asl.tbats.json index 470ed6818b..b9b669fa62 100644 --- a/catalog/summaries/models/model_items/asl.tbats.json +++ b/catalog/summaries/models/model_items/asl.tbats.json @@ -4,7 +4,7 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "asl.tbats", + "id": "asl.tbats_DO_mgL_mean_P1D_forecast", "bbox": [ [-79.8372, 37.3032, -79.8159, 37.3129] ], @@ -16,9 +16,10 @@ ] }, "properties": { - "description": "\nmodel info: forecast::tbats() function in R, fit individually at each site/depth\n\nSites: bvre, fcre\n\nVariables: Daily Water_temperature, Daily Chlorophyll-a, Daily fluorescent dissolved organic matter, Daily oxygen_concentration, Daily Secchi, Daily Turbidity, Daily Specific conductance, Daily Bloom_binary, Daily Dissolved methane", + "title": "asl.tbats", + "description": "\nmodel info: forecast::tbats() function in R, fit individually at each site/depth\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", "start_datetime": "2024-03-20", - "end_datetime": "2024-06-26", + "end_datetime": "2024-07-02", "providers": [ { "url": "pending", @@ -41,17 +42,14 @@ "keywords": [ "Forecasting", "vera4cast", - "Daily Water_temperature", - "Daily Chlorophyll-a", - "Daily fluorescent dissolved organic matter", - "Daily oxygen_concentration", - "Daily Secchi", - "Daily Turbidity", - "Daily Specific conductance", - "Daily Bloom_binary", - "Daily Dissolved methane" + "Daily oxygen_concentration" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -136,11 +134,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -191,58 +184,10 @@ "description": "The link to the model code provided by the model submission team" }, "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily fluorescent dissolved organic matter", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { "type": "application/x-parquet", "title": "Database Access for Daily oxygen_concentration", "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org", "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Turbidity", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Turbidity_FNU_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Turbidity_FNU_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Specific conductance", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=SpCond_uScm_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=SpCond_uScm_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily Bloom_binary", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved methane", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=CH4_umolL_sample/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=CH4_umolL_sample/model_id=asl.tbats?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/asl.temp.lm.json b/catalog/summaries/models/model_items/asl.temp.lm.json index f4b399696c..8ab8927316 100644 --- a/catalog/summaries/models/model_items/asl.temp.lm.json +++ b/catalog/summaries/models/model_items/asl.temp.lm.json @@ -4,7 +4,7 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "asl.temp.lm", + "id": "asl.temp.lm_DO_mgL_mean_P1D_forecast", "bbox": [ [-79.8372, 37.3032, -79.8159, 37.3129] ], @@ -16,9 +16,10 @@ ] }, "properties": { - "description": "\nmodel info: Linear regression with air temperature\n\nSites: bvre, fcre\n\nVariables: Daily Water_temperature, Daily Chlorophyll-a, Daily fluorescent dissolved organic matter, Daily oxygen_concentration, Daily Secchi, Daily Turbidity, Daily Specific conductance, Daily Bloom_binary, Daily Dissolved methane", + "title": "asl.temp.lm", + "description": "\nmodel info: Linear regression with air temperature\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", "start_datetime": "2024-05-06", - "end_datetime": "2024-06-25", + "end_datetime": "2024-06-27", "providers": [ { "url": "pending", @@ -41,17 +42,14 @@ "keywords": [ "Forecasting", "vera4cast", - "Daily Water_temperature", - "Daily Chlorophyll-a", - "Daily fluorescent dissolved organic matter", - "Daily oxygen_concentration", - "Daily Secchi", - "Daily Turbidity", - "Daily Specific conductance", - "Daily Bloom_binary", - "Daily Dissolved methane" + "Daily oxygen_concentration" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -136,11 +134,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -191,58 +184,10 @@ "description": "The link to the model code provided by the model submission team" }, "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily fluorescent dissolved organic matter", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { "type": "application/x-parquet", "title": "Database Access for Daily oxygen_concentration", "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org", "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Turbidity", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Turbidity_FNU_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Turbidity_FNU_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Specific conductance", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=SpCond_uScm_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=SpCond_uScm_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily Bloom_binary", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved methane", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=CH4_umolL_sample/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=CH4_umolL_sample/model_id=asl.temp.lm?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/cfs.json b/catalog/summaries/models/model_items/cfs.json index 1f5645cbf3..f5dcda3e54 100644 --- a/catalog/summaries/models/model_items/cfs.json +++ b/catalog/summaries/models/model_items/cfs.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "cfs", + "id": "cfs_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8372, 37.3032] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { - "description": "\nmodel info: NOAA Climate Forecasting System forecasts as downloaded from open-meteo.com. Submitted forecasts only include the first ~6 months because there are 4 ensemble members available (only 1 is available from 6 - 9 months).\n\nSites: fcre\n\nVariables: Daily Air temperature, Daily Relative humdity, Daily Precipitation, Daily Shortwave radiation, Daily Wind speed", - "start_datetime": "2023-10-13", - "end_datetime": "2024-04-01", + "title": "cfs", + "description": "\nmodel info: NOAA Climate Forecasting System forecasts as downloaded from open-meteo.com. Submitted forecasts only include the first ~6 months because there are 4 ensemble members available (only 1 is available from 6 - 9 months).\n\nSites: \n\nVariables: ", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,14 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Air temperature", - "Daily Relative humdity", - "Daily Precipitation", - "Daily Shortwave radiation", - "Daily Wind speed" + "vera4cast" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -131,11 +130,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -187,33 +181,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Air temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=cfs?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=cfs?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" }, "4": { "type": "application/x-parquet", - "title": "Database Access for Daily Relative humdity", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=RH_percent_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=RH_percent_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Precipitation", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Rain_mm_sum/model_id=cfs?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Rain_mm_sum/model_id=cfs?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Shortwave radiation", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Wind speed", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=WindSpeed_ms_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=WindSpeed_ms_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for ", + "href": "s3://anonymous@project_id=/duration=/variable=/model_id=cfs?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=/duration=/variable=/model_id=cfs?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/climatology.json b/catalog/summaries/models/model_items/climatology.json index b8dd1d099c..642d4415da 100644 --- a/catalog/summaries/models/model_items/climatology.json +++ b/catalog/summaries/models/model_items/climatology.json @@ -4,7 +4,7 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "climatology", + "id": "climatology_DO_mgL_mean_P1D_forecast", "bbox": [ [-79.8372, 37.3032, -79.8159, 37.3129] ], @@ -12,14 +12,14 @@ "type": "MultiPoint", "coordinates": [ [-79.8372, 37.3032], - [-79.8159, 37.3129], - [-79.8357, 37.3078] + [-79.8159, 37.3129] ] }, "properties": { - "description": "\nmodel info: Historical DOY mean and sd. Assumes normal distribution\n\nSites: fcre, bvre, tubr\n\nVariables: Daily Air temperature, Daily Chlorophyll-a, Daily Inflow discharge, Daily Water_temperature, Daily oxygen_concentration, Daily oxygen % sat, Daily Secchi, Daily fluorescent dissolved organic matter, Daily Bloom_binary, Daily Dissolved methane", - "start_datetime": "2023-09-21", - "end_datetime": "2024-06-27", + "title": "climatology", + "description": "\nmodel info: Historical DOY mean and sd. Assumes normal distribution\n\nSites: fcre, bvre\n\nVariables: Daily oxygen_concentration", + "start_datetime": "2024-02-06", + "end_datetime": "2024-07-03", "providers": [ { "url": "pending", @@ -42,18 +42,14 @@ "keywords": [ "Forecasting", "vera4cast", - "Daily Air temperature", - "Daily Chlorophyll-a", - "Daily Inflow discharge", - "Daily Water_temperature", - "Daily oxygen_concentration", - "Daily oxygen % sat", - "Daily Secchi", - "Daily fluorescent dissolved organic matter", - "Daily Bloom_binary", - "Daily Dissolved methane" + "Daily oxygen_concentration" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -138,11 +134,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -193,64 +184,10 @@ "description": "The link to the model code provided by the model submission team" }, "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Air temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Inflow discharge", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Flow_cms_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Flow_cms_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { "type": "application/x-parquet", "title": "Database Access for Daily oxygen_concentration", "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org", "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily oxygen % sat", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=DOsat_percent_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=DOsat_percent_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=climatology?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=climatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily fluorescent dissolved organic matter", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Daily Bloom_binary", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=climatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "12": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved methane", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=CH4_umolL_sample/model_id=climatology?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=CH4_umolL_sample/model_id=climatology?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/climatology2.json b/catalog/summaries/models/model_items/climatology2.json index b109777e7c..e5d22d1386 100644 --- a/catalog/summaries/models/model_items/climatology2.json +++ b/catalog/summaries/models/model_items/climatology2.json @@ -4,21 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "climatology2", + "id": "climatology2_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8159, 37.3129] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8159, 37.3129], - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { - "description": "\nmodel info: Same is the other climatology\n\nSites: bvre, fcre\n\nVariables: Daily Chlorophyll-a, Daily Water_temperature", - "start_datetime": "2023-10-07", - "end_datetime": "2023-11-10", + "title": "climatology2", + "description": "\nmodel info: Same is the other climatology\n\nSites: \n\nVariables: ", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -40,11 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Chlorophyll-a", - "Daily Water_temperature" + "vera4cast" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -129,11 +130,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -185,15 +181,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=climatology2?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=climatology2?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=climatology2?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=climatology2?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" }, "4": { "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=climatology2?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=climatology2?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for ", + "href": "s3://anonymous@project_id=/duration=/variable=/model_id=climatology2?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=/duration=/variable=/model_id=climatology2?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/ecmwf_ifs04.json b/catalog/summaries/models/model_items/ecmwf_ifs04.json index 95e9c59eb1..d0e07a6015 100644 --- a/catalog/summaries/models/model_items/ecmwf_ifs04.json +++ b/catalog/summaries/models/model_items/ecmwf_ifs04.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "ecmwf_ifs04", + "id": "ecmwf_ifs04_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8372, 37.3032] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { - "description": "\nmodel info: ECMWF IFS Ensemble weather model downloaded from open-meteo.com. Since ECMWF IFS Ensemble does output solar radiation on open-meteo.com, the solar radiation from GFS seamless is used.\n\nSites: fcre\n\nVariables: Daily surface pressure, Daily Relative humdity, Daily Precipitation, Daily Wind speed, Daily Air temperature", - "start_datetime": "2023-10-14", - "end_datetime": "2024-06-01", + "title": "ecmwf_ifs04", + "description": "\nmodel info: ECMWF IFS Ensemble weather model downloaded from open-meteo.com. Since ECMWF IFS Ensemble does output solar radiation on open-meteo.com, the solar radiation from GFS seamless is used.\n\nSites: \n\nVariables: ", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,14 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily surface pressure", - "Daily Relative humdity", - "Daily Precipitation", - "Daily Wind speed", - "Daily Air temperature" + "vera4cast" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -131,11 +130,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -187,33 +181,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily surface pressure", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=BP_kPa_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=BP_kPa_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" }, "4": { "type": "application/x-parquet", - "title": "Database Access for Daily Relative humdity", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=RH_percent_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=RH_percent_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Precipitation", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Rain_mm_sum/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Rain_mm_sum/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Wind speed", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=WindSpeed_ms_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=WindSpeed_ms_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Air temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for ", + "href": "s3://anonymous@project_id=/duration=/variable=/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=/duration=/variable=/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/example_ID.json b/catalog/summaries/models/model_items/example_ID.json index 7351e3747d..51258179b1 100644 --- a/catalog/summaries/models/model_items/example_ID.json +++ b/catalog/summaries/models/model_items/example_ID.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "example_ID", + "id": "example_ID_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8372, 37.3032] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { + "title": "example_ID", "description": [], - "start_datetime": "2024-04-03", - "end_datetime": "2024-06-14", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,10 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Water_temperature" + "vera4cast" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -127,11 +130,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -183,9 +181,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=example_ID?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=example_ID?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=example_ID?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=example_ID?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + }, + "4": { + "type": "application/x-parquet", + "title": "Database Access for ", + "href": "s3://anonymous@project_id=/duration=/variable=/model_id=example_ID?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=/duration=/variable=/model_id=example_ID?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/example_forecast.json b/catalog/summaries/models/model_items/example_forecast.json index 001cc3aaf4..2b75b5e5fc 100644 --- a/catalog/summaries/models/model_items/example_forecast.json +++ b/catalog/summaries/models/model_items/example_forecast.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "example_forecast", + "id": "example_forecast_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8372, 37.3032] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { + "title": "example_forecast", "description": [], - "start_datetime": "2023-11-07", - "end_datetime": "2023-12-06", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,10 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Water_temperature" + "vera4cast" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -127,11 +130,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -183,9 +181,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=example_forecast?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=example_forecast?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=example_forecast?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=example_forecast?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + }, + "4": { + "type": "application/x-parquet", + "title": "Database Access for ", + "href": "s3://anonymous@project_id=/duration=/variable=/model_id=example_forecast?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=/duration=/variable=/model_id=example_forecast?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/fableARIMA.json b/catalog/summaries/models/model_items/fableARIMA.json index 22c9d2f1ed..d603bfaec8 100644 --- a/catalog/summaries/models/model_items/fableARIMA.json +++ b/catalog/summaries/models/model_items/fableARIMA.json @@ -4,21 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "fableARIMA", + "id": "fableARIMA_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8159, 37.3129] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8159, 37.3129], - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { - "description": "\nmodel info: ARIMA fit using the ARIMA() function in the fable R package\n\nSites: bvre, fcre\n\nVariables: Daily Bloom_binary, Daily Chlorophyll-a", - "start_datetime": "2023-10-01", - "end_datetime": "2024-06-26", + "title": "fableARIMA", + "description": "\nmodel info: ARIMA fit using the ARIMA() function in the fable R package\n\nSites: \n\nVariables: ", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -40,11 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Bloom_binary", - "Daily Chlorophyll-a" + "vera4cast" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -129,11 +130,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -185,15 +181,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Bloom_binary", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=fableARIMA?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=fableARIMA?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=fableARIMA?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=fableARIMA?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" }, "4": { "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=fableARIMA?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=fableARIMA?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for ", + "href": "s3://anonymous@project_id=/duration=/variable=/model_id=fableARIMA?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=/duration=/variable=/model_id=fableARIMA?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/fableETS.json b/catalog/summaries/models/model_items/fableETS.json index 15d1a1f616..22b1efa486 100644 --- a/catalog/summaries/models/model_items/fableETS.json +++ b/catalog/summaries/models/model_items/fableETS.json @@ -4,21 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "fableETS", + "id": "fableETS_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8159, 37.3129] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8159, 37.3129], - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { - "description": "\nmodel info: fable package exponential smoothing model fable::ETS()\n\nSites: bvre, fcre\n\nVariables: Daily Bloom_binary, Daily Chlorophyll-a", - "start_datetime": "2023-10-01", - "end_datetime": "2024-06-26", + "title": "fableETS", + "description": "\nmodel info: fable package exponential smoothing model fable::ETS()\n\nSites: \n\nVariables: ", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -40,11 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Bloom_binary", - "Daily Chlorophyll-a" + "vera4cast" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -129,11 +130,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -185,15 +181,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Bloom_binary", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=fableETS?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=fableETS?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=fableETS?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=fableETS?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" }, "4": { "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=fableETS?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=fableETS?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for ", + "href": "s3://anonymous@project_id=/duration=/variable=/model_id=fableETS?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=/duration=/variable=/model_id=fableETS?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/fableNNETAR.json b/catalog/summaries/models/model_items/fableNNETAR.json index d72712b560..b0899f870e 100644 --- a/catalog/summaries/models/model_items/fableNNETAR.json +++ b/catalog/summaries/models/model_items/fableNNETAR.json @@ -4,7 +4,7 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "fableNNETAR", + "id": "fableNNETAR_DO_mgL_mean_P1D_forecast", "bbox": [ [-79.8372, 37.3032, -79.8159, 37.3129] ], @@ -16,9 +16,10 @@ ] }, "properties": { - "description": "\nmodel info: autoregressive neural net fit using the NNETAR() function in the fable R package\n\nSites: bvre, fcre\n\nVariables: Daily Bloom_binary, Daily Chlorophyll-a, Daily Water_temperature, Daily oxygen_concentration, Daily Secchi, Daily fluorescent dissolved organic matter", - "start_datetime": "2023-10-01", - "end_datetime": "2024-06-26", + "title": "fableNNETAR", + "description": "\nmodel info: autoregressive neural net fit using the NNETAR() function in the fable R package\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", + "start_datetime": "2024-04-26", + "end_datetime": "2024-06-03", "providers": [ { "url": "pending", @@ -41,14 +42,14 @@ "keywords": [ "Forecasting", "vera4cast", - "Daily Bloom_binary", - "Daily Chlorophyll-a", - "Daily Water_temperature", - "Daily oxygen_concentration", - "Daily Secchi", - "Daily fluorescent dissolved organic matter" + "Daily oxygen_concentration" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -133,11 +134,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -188,40 +184,10 @@ "description": "The link to the model code provided by the model submission team" }, "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Bloom_binary", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { "type": "application/x-parquet", "title": "Database Access for Daily oxygen_concentration", "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org", "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily fluorescent dissolved organic matter", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/fableNNETAR_focal.json b/catalog/summaries/models/model_items/fableNNETAR_focal.json index a8925f9212..ef292aac61 100644 --- a/catalog/summaries/models/model_items/fableNNETAR_focal.json +++ b/catalog/summaries/models/model_items/fableNNETAR_focal.json @@ -4,7 +4,7 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "fableNNETAR_focal", + "id": "fableNNETAR_focal_DO_mgL_mean_P1D_forecast", "bbox": [ [-79.8372, 37.3032, -79.8159, 37.3129] ], @@ -16,9 +16,10 @@ ] }, "properties": { - "description": "\nmodel info: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables\n\nSites: bvre, fcre\n\nVariables: Daily Water_temperature, Daily oxygen_concentration, Daily Secchi, Daily fluorescent dissolved organic matter", + "title": "fableNNETAR_focal", + "description": "\nmodel info: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", "start_datetime": "2024-04-26", - "end_datetime": "2024-06-26", + "end_datetime": "2024-07-01", "providers": [ { "url": "pending", @@ -41,12 +42,14 @@ "keywords": [ "Forecasting", "vera4cast", - "Daily Water_temperature", - "Daily oxygen_concentration", - "Daily Secchi", - "Daily fluorescent dissolved organic matter" + "Daily oxygen_concentration" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -131,11 +134,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -186,28 +184,10 @@ "description": "The link to the model code provided by the model submission team" }, "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=fableNNETAR_focal?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=fableNNETAR_focal?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { "type": "application/x-parquet", "title": "Database Access for Daily oxygen_concentration", "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=fableNNETAR_focal?endpoint_override=renc.osn.xsede.org", "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=fableNNETAR_focal?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=fableNNETAR_focal?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=fableNNETAR_focal?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily fluorescent dissolved organic matter", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=fableNNETAR_focal?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=fableNNETAR_focal?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/flareGOTM.json b/catalog/summaries/models/model_items/flareGOTM.json index c3b672ed95..e9a34fcf81 100644 --- a/catalog/summaries/models/model_items/flareGOTM.json +++ b/catalog/summaries/models/model_items/flareGOTM.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "flareGOTM", + "id": "flareGOTM_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8372, 37.3032] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { - "description": "\nmodel info: FLARE-GOTM combines the 1D hydrodynamic process-based model GOTM, a data assimilation algorithm, and NOAA weather data to forecast water column temperatures.\n\nSites: fcre\n\nVariables: Daily NA, Daily Water_temperature", - "start_datetime": "2024-01-21", - "end_datetime": "2024-03-20", + "title": "flareGOTM", + "description": "\nmodel info: FLARE-GOTM combines the 1D hydrodynamic process-based model GOTM, a data assimilation algorithm, and NOAA weather data to forecast water column temperatures.\n\nSites: \n\nVariables: ", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,11 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily NA", - "Daily Water_temperature" + "vera4cast" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -128,11 +130,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -184,15 +181,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily NA", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=temperature/model_id=flareGOTM?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=temperature/model_id=flareGOTM?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=flareGOTM?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=flareGOTM?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" }, "4": { "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=flareGOTM?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=flareGOTM?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for ", + "href": "s3://anonymous@project_id=/duration=/variable=/model_id=flareGOTM?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=/duration=/variable=/model_id=flareGOTM?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/flareSimstrat.json b/catalog/summaries/models/model_items/flareSimstrat.json index 2578fa3813..bff076a66f 100644 --- a/catalog/summaries/models/model_items/flareSimstrat.json +++ b/catalog/summaries/models/model_items/flareSimstrat.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "flareSimstrat", + "id": "flareSimstrat_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8372, 37.3032] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { - "description": "\nmodel info: FLARE-Simstrat combines the 1D process-based model Simstrat, a data assimilation algorithm (EnKF) and NOAA driver weather data to make predictions of water column temperatures.\n\nSites: fcre\n\nVariables: Daily NA, Daily Water_temperature", - "start_datetime": "2024-01-21", - "end_datetime": "2024-03-19", + "title": "flareSimstrat", + "description": "\nmodel info: FLARE-Simstrat combines the 1D process-based model Simstrat, a data assimilation algorithm (EnKF) and NOAA driver weather data to make predictions of water column temperatures.\n\nSites: \n\nVariables: ", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,11 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily NA", - "Daily Water_temperature" + "vera4cast" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -128,11 +130,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -184,15 +181,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily NA", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=temperature/model_id=flareSimstrat?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=temperature/model_id=flareSimstrat?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=flareSimstrat?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=flareSimstrat?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" }, "4": { "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=flareSimstrat?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=flareSimstrat?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for ", + "href": "s3://anonymous@project_id=/duration=/variable=/model_id=flareSimstrat?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=/duration=/variable=/model_id=flareSimstrat?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/gem_global.json b/catalog/summaries/models/model_items/gem_global.json index 3dcf112a6c..95c56297bc 100644 --- a/catalog/summaries/models/model_items/gem_global.json +++ b/catalog/summaries/models/model_items/gem_global.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "gem_global", + "id": "gem_global_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8372, 37.3032] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { - "description": "\nmodel info: Candian GEM Global Ensemble model downloaded from open-meteo.com\n\nSites: fcre\n\nVariables: Daily Air temperature, Daily surface pressure, Daily Relative humdity, Daily Precipitation, Daily Shortwave radiation, Daily Wind speed", - "start_datetime": "2023-10-14", - "end_datetime": "2024-06-23", + "title": "gem_global", + "description": "\nmodel info: Candian GEM Global Ensemble model downloaded from open-meteo.com\n\nSites: \n\nVariables: ", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,15 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Air temperature", - "Daily surface pressure", - "Daily Relative humdity", - "Daily Precipitation", - "Daily Shortwave radiation", - "Daily Wind speed" + "vera4cast" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -132,11 +130,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -188,39 +181,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Air temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=gem_global?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=gem_global?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" }, "4": { "type": "application/x-parquet", - "title": "Database Access for Daily surface pressure", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=BP_kPa_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=BP_kPa_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Relative humdity", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=RH_percent_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=RH_percent_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Precipitation", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Rain_mm_sum/model_id=gem_global?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Rain_mm_sum/model_id=gem_global?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Shortwave radiation", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Wind speed", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=WindSpeed_ms_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=WindSpeed_ms_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for ", + "href": "s3://anonymous@project_id=/duration=/variable=/model_id=gem_global?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=/duration=/variable=/model_id=gem_global?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/gfs_seamless.json b/catalog/summaries/models/model_items/gfs_seamless.json index 74715ebf4c..8569a9f9a1 100644 --- a/catalog/summaries/models/model_items/gfs_seamless.json +++ b/catalog/summaries/models/model_items/gfs_seamless.json @@ -4,21 +4,22 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "gfs_seamless", + "id": "gfs_seamless_DO_mgL_mean_P1D_forecast", "bbox": [ [-79.8372, 37.3032, -79.8159, 37.3129] ], "geometry": { "type": "MultiPoint", "coordinates": [ - [-79.8372, 37.3032], - [-79.8159, 37.3129] + [-79.8159, 37.3129], + [-79.8372, 37.3032] ] }, "properties": { - "description": "\nmodel info: NOAA Global Ensemble Forecasting Model downloaded using the https://open-meteo.com. The seamless model combines the 0.25 and 0.5 degree resolution products to get a full 35-day ahead forecast\n\nSites: fcre, bvre\n\nVariables: Daily Air temperature, Daily surface pressure, Daily Relative humdity, Daily Precipitation, Daily Shortwave radiation, Daily Wind speed, Daily Water_temperature, Daily Chlorophyll-a, Daily fluorescent dissolved organic matter, Daily oxygen_concentration, Daily Secchi, Daily Turbidity, Daily Specific conductance", - "start_datetime": "2023-10-13", - "end_datetime": "2024-06-26", + "title": "gfs_seamless", + "description": "\nmodel info: NOAA Global Ensemble Forecasting Model downloaded using the https://open-meteo.com. The seamless model combines the 0.25 and 0.5 degree resolution products to get a full 35-day ahead forecast\n\nSites: bvre, fcre\n\nVariables: Daily oxygen_concentration", + "start_datetime": "2024-04-09", + "end_datetime": "2024-06-08", "providers": [ { "url": "pending", @@ -41,21 +42,14 @@ "keywords": [ "Forecasting", "vera4cast", - "Daily Air temperature", - "Daily surface pressure", - "Daily Relative humdity", - "Daily Precipitation", - "Daily Shortwave radiation", - "Daily Wind speed", - "Daily Water_temperature", - "Daily Chlorophyll-a", - "Daily fluorescent dissolved organic matter", - "Daily oxygen_concentration", - "Daily Secchi", - "Daily Turbidity", - "Daily Specific conductance" + "Daily oxygen_concentration" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -140,11 +134,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -195,82 +184,10 @@ "description": "The link to the model code provided by the model submission team" }, "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Air temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily surface pressure", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=BP_kPa_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=BP_kPa_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Relative humdity", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=RH_percent_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=RH_percent_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Precipitation", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Rain_mm_sum/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Rain_mm_sum/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Shortwave radiation", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Wind speed", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=WindSpeed_ms_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=WindSpeed_ms_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Daily fluorescent dissolved organic matter", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "12": { "type": "application/x-parquet", "title": "Database Access for Daily oxygen_concentration", "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "13": { - "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "14": { - "type": "application/x-parquet", - "title": "Database Access for Daily Turbidity", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Turbidity_FNU_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Turbidity_FNU_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "15": { - "type": "application/x-parquet", - "title": "Database Access for Daily Specific conductance", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=SpCond_uScm_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=SpCond_uScm_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/glm_aed_v1.json b/catalog/summaries/models/model_items/glm_aed_v1.json index 97aa4fc1a0..1806b20fd3 100644 --- a/catalog/summaries/models/model_items/glm_aed_v1.json +++ b/catalog/summaries/models/model_items/glm_aed_v1.json @@ -4,7 +4,7 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "glm_aed_v1", + "id": "glm_aed_v1_DO_mgL_mean_P1D_forecast", "bbox": [ [-79.8372, 37.3032, -79.8372, 37.3032] ], @@ -15,9 +15,10 @@ ] }, "properties": { - "description": "\nmodel info: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters.\n\nSites: fcre\n\nVariables: Daily Bloom_binary, Daily Chlorophyll-a, Daily Dissolved organic carbon concentration, Daily oxygen_concentration, Daily ammonium concentration, Daily Total soluble reactive phosphorus concentration, Daily Water_temperature, Daily fluorescent dissolved organic matter, Daily Secchi, Daily Dissolved methane, Daily Presence of ice cover", + "title": "glm_aed_v1", + "description": "\nmodel info: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters.\n\nSites: fcre\n\nVariables: Daily oxygen_concentration", "start_datetime": "2023-10-14", - "end_datetime": "2024-06-24", + "end_datetime": "2024-06-30", "providers": [ { "url": "pending", @@ -40,19 +41,14 @@ "keywords": [ "Forecasting", "vera4cast", - "Daily Bloom_binary", - "Daily Chlorophyll-a", - "Daily Dissolved organic carbon concentration", - "Daily oxygen_concentration", - "Daily ammonium concentration", - "Daily Total soluble reactive phosphorus concentration", - "Daily Water_temperature", - "Daily fluorescent dissolved organic matter", - "Daily Secchi", - "Daily Dissolved methane", - "Daily Presence of ice cover" + "Daily oxygen_concentration" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -137,11 +133,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -192,70 +183,10 @@ "description": "The link to the model code provided by the model submission team" }, "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Bloom_binary", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved organic carbon concentration", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=DIC_mgL_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=DIC_mgL_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { "type": "application/x-parquet", "title": "Database Access for Daily oxygen_concentration", "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily ammonium concentration", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=NH4_ugL_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=NH4_ugL_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Total soluble reactive phosphorus concentration", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=SRP_ugL_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=SRP_ugL_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily fluorescent dissolved organic matter", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "12": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved methane", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=CH4_umolL_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=CH4_umolL_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "13": { - "type": "application/x-parquet", - "title": "Database Access for Daily Presence of ice cover", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=IceCover_binary_max/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=IceCover_binary_max/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/historic_mean.json b/catalog/summaries/models/model_items/historic_mean.json index eceff99d8a..9115611f2b 100644 --- a/catalog/summaries/models/model_items/historic_mean.json +++ b/catalog/summaries/models/model_items/historic_mean.json @@ -4,22 +4,22 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "historic_mean", + "id": "historic_mean_DO_mgL_mean_P1D_forecast", "bbox": [ [-79.8372, 37.3032, -79.8159, 37.3129] ], "geometry": { "type": "MultiPoint", "coordinates": [ - [-79.8357, 37.3078], [-79.8372, 37.3032], [-79.8159, 37.3129] ] }, "properties": { - "description": "\nmodel info: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model.\n\nSites: tubr, fcre, bvre\n\nVariables: Daily Inflow discharge, Daily Water_temperature, Daily oxygen_concentration, Daily oxygen % sat, Daily Chlorophyll-a, Daily Secchi, Daily Air temperature, Daily fluorescent dissolved organic matter, Daily Bloom_binary, NA Dissolved methane", + "title": "historic_mean", + "description": "\nmodel info: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model.\n\nSites: fcre, bvre\n\nVariables: Daily oxygen_concentration", "start_datetime": "2024-02-06", - "end_datetime": "2024-06-25", + "end_datetime": "2024-07-01", "providers": [ { "url": "pending", @@ -42,18 +42,14 @@ "keywords": [ "Forecasting", "vera4cast", - "Daily Inflow discharge", - "Daily Water_temperature", - "Daily oxygen_concentration", - "Daily oxygen % sat", - "Daily Chlorophyll-a", - "Daily Secchi", - "Daily Air temperature", - "Daily fluorescent dissolved organic matter", - "Daily Bloom_binary", - "NA Dissolved methane" + "Daily oxygen_concentration" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -138,11 +134,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -193,64 +184,10 @@ "description": "The link to the model code provided by the model submission team" }, "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Inflow discharge", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Flow_cms_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Flow_cms_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { "type": "application/x-parquet", "title": "Database Access for Daily oxygen_concentration", "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org", "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily oxygen % sat", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=DOsat_percent_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=DOsat_percent_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=historic_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=historic_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Air temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily fluorescent dissolved organic matter", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Daily Bloom_binary", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=historic_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "12": { - "type": "application/x-parquet", - "title": "Database Access for NA Dissolved methane", - "href": "s3://anonymous@project_id=vera4cast/duration=NA/variable=CH4_umolL_sample/model_id=historic_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=NA/variable=CH4_umolL_sample/model_id=historic_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/icon_seamless.json b/catalog/summaries/models/model_items/icon_seamless.json index 6db72256d3..fd1f0a6f60 100644 --- a/catalog/summaries/models/model_items/icon_seamless.json +++ b/catalog/summaries/models/model_items/icon_seamless.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "icon_seamless", + "id": "icon_seamless_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8372, 37.3032] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { - "description": "\nmodel info: The DWD Icon EPS Seamless model downloaded from open-meteo.com\n\nSites: fcre\n\nVariables: Daily Air temperature, Daily surface pressure, Daily Relative humdity, Daily Precipitation, Daily Shortwave radiation, Daily Wind speed", - "start_datetime": "2023-10-14", - "end_datetime": "2024-05-29", + "title": "icon_seamless", + "description": "\nmodel info: The DWD Icon EPS Seamless model downloaded from open-meteo.com\n\nSites: \n\nVariables: ", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,15 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Air temperature", - "Daily surface pressure", - "Daily Relative humdity", - "Daily Precipitation", - "Daily Shortwave radiation", - "Daily Wind speed" + "vera4cast" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -132,11 +130,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -188,39 +181,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Air temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" }, "4": { "type": "application/x-parquet", - "title": "Database Access for Daily surface pressure", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=BP_kPa_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=BP_kPa_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Relative humdity", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=RH_percent_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=RH_percent_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Precipitation", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Rain_mm_sum/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Rain_mm_sum/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Shortwave radiation", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Wind speed", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=WindSpeed_ms_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=WindSpeed_ms_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for ", + "href": "s3://anonymous@project_id=/duration=/variable=/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=/duration=/variable=/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/inflow_gefsClimAED.json b/catalog/summaries/models/model_items/inflow_gefsClimAED.json index 6a723fca8e..f4164c574b 100644 --- a/catalog/summaries/models/model_items/inflow_gefsClimAED.json +++ b/catalog/summaries/models/model_items/inflow_gefsClimAED.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "inflow_gefsClimAED", + "id": "inflow_gefsClimAED_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8357, 37.3078, -79.8357, 37.3078] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8357, 37.3078] - ] + "coordinates": [] }, "properties": { - "description": "\nmodel info: flow is forecasted as using a linear relationship between historical flow, month, and 5-day sum of precipitation. Temperature is forecasted using a linear relationship between historical water temperature, month, and 5-day mean air temperature. NOAA GEFS is then used to get the future values of 5-day sum precipitation and mean temperature. Nutrients are forecasting using the DOY climatology. The DOY climatology was developed using a linear interpolation of the historical samples.\n\nSites: tubr\n\nVariables: Daily Dissolved methane, Daily Dissolved organic carbon concentration, Daily dissolved organic carbon concentration, Daily Dissolved silica concentration, Daily Inflow discharge, Daily ammonium concentration, Daily Nitrate concentration, Daily Total soluble reactive phosphorus concentration, Daily Total nitrogen concentration, Daily Total phosphorus concentration, Daily Water_temperature", - "start_datetime": "2023-10-13", - "end_datetime": "2024-06-26", + "title": "inflow_gefsClimAED", + "description": "\nmodel info: flow is forecasted as using a linear relationship between historical flow, month, and 5-day sum of precipitation. Temperature is forecasted using a linear relationship between historical water temperature, month, and 5-day mean air temperature. NOAA GEFS is then used to get the future values of 5-day sum precipitation and mean temperature. Nutrients are forecasting using the DOY climatology. The DOY climatology was developed using a linear interpolation of the historical samples.\n\nSites: \n\nVariables: ", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,20 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Dissolved methane", - "Daily Dissolved organic carbon concentration", - "Daily dissolved organic carbon concentration", - "Daily Dissolved silica concentration", - "Daily Inflow discharge", - "Daily ammonium concentration", - "Daily Nitrate concentration", - "Daily Total soluble reactive phosphorus concentration", - "Daily Total nitrogen concentration", - "Daily Total phosphorus concentration", - "Daily Water_temperature" + "vera4cast" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -137,11 +130,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -193,69 +181,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved methane", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=CH4_umolL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=CH4_umolL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" }, "4": { "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved organic carbon concentration", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=DIC_mgL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=DIC_mgL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily dissolved organic carbon concentration", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=DOC_mgL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=DOC_mgL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved silica concentration", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=DRSI_mgL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=DRSI_mgL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily Inflow discharge", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Flow_cms_mean/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Flow_cms_mean/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily ammonium concentration", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=NH4_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=NH4_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Nitrate concentration", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=NO3NO2_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=NO3NO2_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily Total soluble reactive phosphorus concentration", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=SRP_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=SRP_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Daily Total nitrogen concentration", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=TN_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=TN_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "12": { - "type": "application/x-parquet", - "title": "Database Access for Daily Total phosphorus concentration", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=TP_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=TP_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "13": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for ", + "href": "s3://anonymous@project_id=/duration=/variable=/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=/duration=/variable=/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/monthly_mean.json b/catalog/summaries/models/model_items/monthly_mean.json index 538d712209..562f7db8cc 100644 --- a/catalog/summaries/models/model_items/monthly_mean.json +++ b/catalog/summaries/models/model_items/monthly_mean.json @@ -4,7 +4,7 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "monthly_mean", + "id": "monthly_mean_DO_mgL_mean_P1D_forecast", "bbox": [ [-79.8372, 37.3032, -79.8159, 37.3129] ], @@ -12,14 +12,14 @@ "type": "MultiPoint", "coordinates": [ [-79.8372, 37.3032], - [-79.8357, 37.3078], [-79.8159, 37.3129] ] }, "properties": { - "description": "\nmodel info: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast.\n\nSites: fcre, tubr, bvre\n\nVariables: Daily Air temperature, Daily Inflow discharge, Daily Water_temperature, Daily oxygen_concentration, Daily oxygen % sat, Daily Chlorophyll-a, Daily Secchi, Daily fluorescent dissolved organic matter, Daily Bloom_binary, Daily Dissolved methane", + "title": "monthly_mean", + "description": "\nmodel info: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast.\n\nSites: fcre, bvre\n\nVariables: Daily oxygen_concentration", "start_datetime": "2024-02-06", - "end_datetime": "2024-06-27", + "end_datetime": "2024-07-03", "providers": [ { "url": "pending", @@ -42,18 +42,14 @@ "keywords": [ "Forecasting", "vera4cast", - "Daily Air temperature", - "Daily Inflow discharge", - "Daily Water_temperature", - "Daily oxygen_concentration", - "Daily oxygen % sat", - "Daily Chlorophyll-a", - "Daily Secchi", - "Daily fluorescent dissolved organic matter", - "Daily Bloom_binary", - "Daily Dissolved methane" + "Daily oxygen_concentration" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -138,11 +134,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -193,64 +184,10 @@ "description": "The link to the model code provided by the model submission team" }, "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Air temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Inflow discharge", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Flow_cms_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Flow_cms_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { "type": "application/x-parquet", "title": "Database Access for Daily oxygen_concentration", "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org", "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily oxygen % sat", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=DOsat_percent_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=DOsat_percent_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily fluorescent dissolved organic matter", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Daily Bloom_binary", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "12": { - "type": "application/x-parquet", - "title": "Database Access for Daily Dissolved methane", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=CH4_umolL_sample/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=CH4_umolL_sample/model_id=monthly_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/persistenceFO.json b/catalog/summaries/models/model_items/persistenceFO.json index 3e9f238d44..12a1c9fc2a 100644 --- a/catalog/summaries/models/model_items/persistenceFO.json +++ b/catalog/summaries/models/model_items/persistenceFO.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "persistenceFO", + "id": "persistenceFO_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8159, 37.3129, -79.8159, 37.3129] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8159, 37.3129] - ] + "coordinates": [] }, "properties": { - "description": "\nmodel info: another persistence forecast\n\nSites: bvre\n\nVariables: Daily Water_temperature", - "start_datetime": "2023-09-27", - "end_datetime": "2023-10-30", + "title": "persistenceFO", + "description": "\nmodel info: another persistence forecast\n\nSites: \n\nVariables: ", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,10 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Water_temperature" + "vera4cast" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -127,11 +130,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -183,9 +181,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=persistenceFO?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=persistenceFO?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=persistenceFO?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=persistenceFO?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + }, + "4": { + "type": "application/x-parquet", + "title": "Database Access for ", + "href": "s3://anonymous@project_id=/duration=/variable=/model_id=persistenceFO?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=/duration=/variable=/model_id=persistenceFO?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/persistenceRW.json b/catalog/summaries/models/model_items/persistenceRW.json index 24eb50422b..4659679752 100644 --- a/catalog/summaries/models/model_items/persistenceRW.json +++ b/catalog/summaries/models/model_items/persistenceRW.json @@ -4,22 +4,22 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "persistenceRW", + "id": "persistenceRW_DO_mgL_mean_P1D_forecast", "bbox": [ [-79.8372, 37.3032, -79.8159, 37.3129] ], "geometry": { "type": "MultiPoint", "coordinates": [ - [-79.8159, 37.3129], [-79.8372, 37.3032], - [-79.8357, 37.3078] + [-79.8159, 37.3129] ] }, "properties": { - "description": "\nmodel info: Random walk from the fable package with ensembles used to represent uncertainty\n\nSites: bvre, fcre, tubr\n\nVariables: Daily Water_temperature, Daily Chlorophyll-a, Daily Inflow discharge, Daily oxygen_concentration, Daily oxygen % sat, Daily Secchi, Daily Air temperature, Daily fluorescent dissolved organic matter, Daily Bloom_binary, NA Dissolved methane, NA Dissolved carbon dioxide, NA ammonium concentration, Daily ammonium concentration, NA dissolved organic carbon concentration, Daily dissolved organic carbon concentration, NA Nitrate concentration, Daily Nitrate concentration, NA Total phosphorus concentration, Daily Total phosphorus concentration, NA Total nitrogen concentration, Daily Total nitrogen concentration, NA Surface CO2 flux, Daily Surface CO2 flux, NA Surface methane flux, Daily Surface methane flux", - "start_datetime": "2023-09-21", - "end_datetime": "2439-08-21", + "title": "persistenceRW", + "description": "\nmodel info: Random walk from the fable package with ensembles used to represent uncertainty\n\nSites: fcre, bvre\n\nVariables: Daily oxygen_concentration", + "start_datetime": "2024-02-06", + "end_datetime": "2024-07-01", "providers": [ { "url": "pending", @@ -42,33 +42,14 @@ "keywords": [ "Forecasting", "vera4cast", - "Daily Water_temperature", - "Daily Chlorophyll-a", - "Daily Inflow discharge", - "Daily oxygen_concentration", - "Daily oxygen % sat", - "Daily Secchi", - "Daily Air temperature", - "Daily fluorescent dissolved organic matter", - "Daily Bloom_binary", - "NA Dissolved methane", - "NA Dissolved carbon dioxide", - "NA ammonium concentration", - "Daily ammonium concentration", - "NA dissolved organic carbon concentration", - "Daily dissolved organic carbon concentration", - "NA Nitrate concentration", - "Daily Nitrate concentration", - "NA Total phosphorus concentration", - "Daily Total phosphorus concentration", - "NA Total nitrogen concentration", - "Daily Total nitrogen concentration", - "NA Surface CO2 flux", - "Daily Surface CO2 flux", - "NA Surface methane flux", - "Daily Surface methane flux" + "Daily oxygen_concentration" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -153,11 +134,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -208,154 +184,10 @@ "description": "The link to the model code provided by the model submission team" }, "3": { - "type": "application/x-parquet", - "title": "Database Access for Daily Water_temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "4": { - "type": "application/x-parquet", - "title": "Database Access for Daily Chlorophyll-a", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "5": { - "type": "application/x-parquet", - "title": "Database Access for Daily Inflow discharge", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Flow_cms_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Flow_cms_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "6": { "type": "application/x-parquet", "title": "Database Access for Daily oxygen_concentration", "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "7": { - "type": "application/x-parquet", - "title": "Database Access for Daily oxygen % sat", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=DOsat_percent_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=DOsat_percent_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "8": { - "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "9": { - "type": "application/x-parquet", - "title": "Database Access for Daily Air temperature", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=AirTemp_C_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "10": { - "type": "application/x-parquet", - "title": "Database Access for Daily fluorescent dissolved organic matter", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=fDOM_QSU_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "11": { - "type": "application/x-parquet", - "title": "Database Access for Daily Bloom_binary", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "12": { - "type": "application/x-parquet", - "title": "Database Access for NA Dissolved methane", - "href": "s3://anonymous@project_id=vera4cast/duration=NA/variable=CH4_umolL_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=NA/variable=CH4_umolL_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "13": { - "type": "application/x-parquet", - "title": "Database Access for NA Dissolved carbon dioxide", - "href": "s3://anonymous@project_id=vera4cast/duration=NA/variable=CO2_umolL_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=NA/variable=CO2_umolL_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "14": { - "type": "application/x-parquet", - "title": "Database Access for NA ammonium concentration", - "href": "s3://anonymous@project_id=vera4cast/duration=NA/variable=NH4_ugL_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=NA/variable=NH4_ugL_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "15": { - "type": "application/x-parquet", - "title": "Database Access for Daily ammonium concentration", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=NH4_ugL_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=NH4_ugL_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "16": { - "type": "application/x-parquet", - "title": "Database Access for NA dissolved organic carbon concentration", - "href": "s3://anonymous@project_id=vera4cast/duration=NA/variable=DOC_mgL_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=NA/variable=DOC_mgL_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "17": { - "type": "application/x-parquet", - "title": "Database Access for Daily dissolved organic carbon concentration", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=DOC_mgL_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=DOC_mgL_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "18": { - "type": "application/x-parquet", - "title": "Database Access for NA Nitrate concentration", - "href": "s3://anonymous@project_id=vera4cast/duration=NA/variable=NO3NO2_ugL_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=NA/variable=NO3NO2_ugL_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "19": { - "type": "application/x-parquet", - "title": "Database Access for Daily Nitrate concentration", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=NO3NO2_ugL_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=NO3NO2_ugL_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "20": { - "type": "application/x-parquet", - "title": "Database Access for NA Total phosphorus concentration", - "href": "s3://anonymous@project_id=vera4cast/duration=NA/variable=TP_ugL_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=NA/variable=TP_ugL_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "21": { - "type": "application/x-parquet", - "title": "Database Access for Daily Total phosphorus concentration", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=TP_ugL_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=TP_ugL_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "22": { - "type": "application/x-parquet", - "title": "Database Access for NA Total nitrogen concentration", - "href": "s3://anonymous@project_id=vera4cast/duration=NA/variable=TN_ugL_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=NA/variable=TN_ugL_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "23": { - "type": "application/x-parquet", - "title": "Database Access for Daily Total nitrogen concentration", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=TN_ugL_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=TN_ugL_sample/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "24": { - "type": "application/x-parquet", - "title": "Database Access for NA Surface CO2 flux", - "href": "s3://anonymous@project_id=vera4cast/duration=NA/variable=CO2flux_umolm2s_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=NA/variable=CO2flux_umolm2s_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "25": { - "type": "application/x-parquet", - "title": "Database Access for Daily Surface CO2 flux", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=CO2flux_umolm2s_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=CO2flux_umolm2s_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "26": { - "type": "application/x-parquet", - "title": "Database Access for NA Surface methane flux", - "href": "s3://anonymous@project_id=vera4cast/duration=NA/variable=CH4flux_umolm2s_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=NA/variable=CH4flux_umolm2s_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" - }, - "27": { - "type": "application/x-parquet", - "title": "Database Access for Daily Surface methane flux", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=CH4flux_umolm2s_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=CH4flux_umolm2s_mean/model_id=persistenceRW?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/secchi_example_forecast.json b/catalog/summaries/models/model_items/secchi_example_forecast.json index c0265bd492..9a7542ce67 100644 --- a/catalog/summaries/models/model_items/secchi_example_forecast.json +++ b/catalog/summaries/models/model_items/secchi_example_forecast.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "secchi_example_forecast", + "id": "secchi_example_forecast_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8372, 37.3032] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { + "title": "secchi_example_forecast", "description": [], - "start_datetime": "2024-04-11", - "end_datetime": "2024-05-11", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,10 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Secchi" + "vera4cast" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -127,11 +130,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -183,9 +181,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=secchi_example_forecast?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=secchi_example_forecast?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=secchi_example_forecast?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=secchi_example_forecast?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + }, + "4": { + "type": "application/x-parquet", + "title": "Database Access for ", + "href": "s3://anonymous@project_id=/duration=/variable=/model_id=secchi_example_forecast?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=/duration=/variable=/model_id=secchi_example_forecast?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/summaries/models/model_items/secchi_last3obs_mean.json b/catalog/summaries/models/model_items/secchi_last3obs_mean.json index a3166a5613..486e5b8786 100644 --- a/catalog/summaries/models/model_items/secchi_last3obs_mean.json +++ b/catalog/summaries/models/model_items/secchi_last3obs_mean.json @@ -4,20 +4,19 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json" ], "type": "Feature", - "id": "secchi_last3obs_mean", + "id": "secchi_last3obs_mean_DO_mgL_mean_P1D_forecast", "bbox": [ - [-79.8372, 37.3032, -79.8372, 37.3032] + ["Inf", "Inf", "-Inf", "-Inf"] ], "geometry": { "type": "MultiPoint", - "coordinates": [ - [-79.8372, 37.3032] - ] + "coordinates": [] }, "properties": { - "description": "\nmodel info: This forecast simply takes the mean of the last three secchi observations and uses the standard deviation of that mean for the uncertainty around the forecast.\n\nSites: fcre\n\nVariables: Daily Secchi", - "start_datetime": "2024-05-02", - "end_datetime": "2024-06-21", + "title": "secchi_last3obs_mean", + "description": "\nmodel info: This forecast simply takes the mean of the last three secchi observations and uses the standard deviation of that mean for the uncertainty around the forecast.\n\nSites: \n\nVariables: ", + "start_datetime": "Inf", + "end_datetime": "-Inf", "providers": [ { "url": "pending", @@ -39,10 +38,14 @@ "license": "CC0-1.0", "keywords": [ "Forecasting", - "vera4cast", - "Daily Secchi" + "vera4cast" ], "table:columns": [ + { + "name": "18 columns", + "type": null, + "description": {} + }, { "name": "reference_datetime", "type": "timestamp[us, tz=UTC]", @@ -127,11 +130,6 @@ "name": "model_id", "type": "string", "description": "unique model identifier" - }, - { - "name": "reference_date", - "type": "string", - "description": "date that the forecast was initiated" } ] }, @@ -183,9 +181,15 @@ }, "3": { "type": "application/x-parquet", - "title": "Database Access for Daily Secchi", - "href": "s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=secchi_last3obs_mean?endpoint_override=renc.osn.xsede.org", - "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=vera4cast/duration=P1D/variable=Secchi_m_sample/model_id=secchi_last3obs_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + "title": "Database Access for NA", + "href": "s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=secchi_last3obs_mean?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=NA/duration=NA/variable=NA/model_id=secchi_last3obs_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + }, + "4": { + "type": "application/x-parquet", + "title": "Database Access for ", + "href": "s3://anonymous@project_id=/duration=/variable=/model_id=secchi_last3obs_mean?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@project_id=/duration=/variable=/model_id=secchi_last3obs_mean?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" } } } diff --git a/catalog/targets/collection.json b/catalog/targets/collection.json index da7f289d74..d32e0ce6ff 100644 --- a/catalog/targets/collection.json +++ b/catalog/targets/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2013-03-07T00:00:00Z", - "2024-05-22T00:00:00Z" + "2024-05-28T00:00:00Z" ] ] }