Author: Stephan Rasp - raspstephan@gmail.com - https://raspstephan.github.io
Hi, thanks for checking out this repository. This is a working repository, which means that the most corrent commit might not always be the most functional or documented.
A Guide for collaborators People hoping to collaborate with me on this project, please check out some guidelines here: https://github.com/raspstephan/CBRAIN-CAM/wiki/A-guide-for-collaborators
If you are looking for the version of the code that corresponds to the PNAS paper. Check out this release: https://github.com/raspstephan/CBRAIN-CAM/releases/tag/PNAS_final
The modified climate model code is available at https://gitlab.com/mspritch/spcam3.0-neural-net.
To create the training data (regular SPCAM) the correct branch is fluxbypass. To implement the trained neural network, check out revision.
S. Rasp, M. Pritchard and P. Gentine, 2018. Deep learning to represent sub-grid processes in climate models https://arxiv.org/abs/1806.04731
P. Gentine, M. Pritchard, S. Rasp, G. Reinaudi and G. Yacalis, 2018. Could machine learning break the convection parameterization deadlock? Geophysical Research Letters. http://doi.wiley.com/10.1029/2018GL078202
The main components of the repository are:
cbrain: Contains the cbrain module with all code to preprocess the raw data, run the neural network experiments and analyze the data.pp_config: Contains configuration files and shell scripts to preprocess the climate model data to be used as neural network inputsnn_config: Contains neural network configuration files to be used withrun_experiment.py.notebooks: Contains Jupyter notebooks used to analyze data. All plotting and data analysis for the papers is done in the subfolderpresentation.devcontains development notebooks.wkspectra: Contains code to compute Wheeler-Kiladis figures. These were created by Mike S. Pritchardsave_weights.py: Saves the weights, biases and normalization vectors in text files. These are then used as input for the climate model.