Update README.md

README.md

@@ -1,12 +1,59 @@
 # newCAM-Emulation
-This is a DNN written with PyTorch to Emulate the gravity wave drag (GWD, both zonal and meridional ) in the WACCM Simulation.
+This repo is to build a coupling tool between NN and Fortran.
 
+The example we show here is to couple a NN emultor of the gravity wave drag (GWD, both zonal and meridional ) scheme back to the mid-top-CAM model.
+* GWD Emulator: PyTorch
+* [mid-top-CAM](https://github.com/ESCOMP/CAM): Fortran code
 
-# DemoData
-Sample output data from CAM.
-It is 3D global output from the mid-top CAM model, on the original model grid.
 
-However, the demo data here is one very small part of the CAM output due to storage limit of Github. NN trained on this Demodata will not work.
+## mid-top-CAM model ##
+More introduction on the NCAR CAM model can be found in the official GitHub page of CAM model.
+>https://github.com/ESCOMP/CAM
+
+
+## GWD emulator 
+
+### training emulator
+This emulator is trained offline with data generated from the physics scheme in mid-top-CAM model
+
+More detail on the emulator can be found here in [this paper : arxiv link for now](https://arxiv.org/abs/2311.17078)
+
+### Sample training data from mid-top-CAM.
+We provide some demodata here for the training. The original training data is 3D global output from the mid-top CAM model, on the original model grid, with a total size of 2Tb. The demo data here is to illustrate the CAM output only, due to storage limit of Github. NN trained on this Demodata will not work.
+
+```
+Variable name in the Demo file:
+The same variables are used by the original physics scheme in the CAM model.
+
+U : zonal wind
+V : meridional wind 
+T : temperature
+Z3: geopotential height (above sea level)
+DSE: dry static energy
+NETDT: net heating rate
+NMBV: Brunt Vaisala frequency
+RHOI: density at interfaces
+PS: surface pressure
+lat: latitude
+lon: longitude
+
+--
+GWD:
+UTGWSPEC: (zonal drag)
+VTGWSPEC: (meridional drag)
+
+
+In CAM, different names are used for GWD to distinguish them based on their sources:
+
+  * For convective gravity waves: BUTGWSPEC, BVTGWSPEC
+  * For frontal gravity waves: UTGWSPEC, VTGWSPEC
+  * For orographic gravity waves: UTGWORO, VTGWORO
+
+```
+
+### NN architechure of the emulator
+we are using a basic fully connected neural network for now. CNN and FNO will be added in the future.
+<img width="651" alt="Screenshot 2024-02-26 at 7 28 10 AM" src="https://github.com/DataWaveProject/newCAM_emulation/assets/85260799/01c42044-6e1c-4bf2-8b56-1ed999933d15">
 
 
 # data loader