Data and code for training and testing a ResMLP model with experience replay for machine-learning physics parameterization

This directory contains the training data and code for training and testing a ResMLP with experience replay for creating a machine-learning physics parameterization for the Community Atmospheric Model. The directory is structured as follows: 1. Download training and testing data: https://portal.nersc.gov/archive/home/z/zhangtao/www/hybird_GCM_ML 2. Unzip nncam_training.zip nncam_training - models model definition of ResMLP and other models for comparison purposes - dataloader utility scripts to load data into pytorch dataset - training_scripts scripts to train ResMLP model with/without experience replay - offline_test scripts to perform offline test (Table 2, Figure 2) 3. Unzip nncam_coupling.zip nncam_srcmods - SourceMods SourceMods to be used with CAM modules for coupling with neural network - otherfiles additional configuration files to setup and run SPCAM with neural network - pythonfiles python scripts to run neural network and couple with CAM - ClimAnalysis - paper_plots.ipynb scripts to produce online evaluation figures (Figure 1, Figure 3-10)