Fork Author: Gunnar Behrens - [email protected]

Main Repository Author: Stephan Rasp - [email protected] - https://raspstephan.github.io

Thank you for checking out this fork of the CBRAIN repository (https://github.com/raspstephan/CBRAIN-CAM), dedicated to building VEDs and VAEs for learning convective processes in SPCAM. This is a working repository, which means that the most current commit might not always be the most functional or documented.

The current release on zenodo can be found here:

If you are looking for the exact version of the code that corresponds to the PNAS paper, check out this release: https://github.com/raspstephan/CBRAIN-CAM/releases/tag/PNAS_final

For a sample of the SPCAM data used, click here:

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.

G.Behrens, T. Beucler, P. Gentine, F. Iglesias-Suarez, M. Pritchard and V. Eyring, 2022. Non-linear dimensionality reduction with a Variational Encoder Decoder to Understand Convective Processes in Climate Models

T. Beucler, M. Pritchard, P. Gentine and S. Rasp, 2020. Towards Physically-consistent, Data-driven Models of Convection. https://arxiv.org/abs/2002.08525

T. Beucler, M. Pritchard, S. Rasp, P. Gentine, J. Ott and P. Baldi, 2019. Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems. https://arxiv.org/abs/1909.00912

S. Rasp, M. Pritchard and P. Gentine, 2018. Deep learning to represent sub-grid processes in climate models. PNAS. https://doi.org/10.1073/pnas.1810286115

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.
• environments: Contains the .yml files of the conda environments used for this repository
• pp_config: Contains configuration files and shell scripts to preprocess the climate model data to be used as neural network inputs
• perf_analy: Contains files for benchmarking the reproduction skills of evaluated models
• R_2_val: Contains saved data sets for the computation of values of determination R² of all networks
• nn_config: Contains neural network configuration files to be used with run_experiment.py.
• notebooks: Contains Jupyter notebooks used to analyze data. All plotting and data analysis for the papers of the main repository is done in the subfolder
• presentation. dev contains development notebooks.
• saved_models: Contains the saved models (weights, biases,..) of the VAE paper in respective subfolders.
• wkspectra: Contains code to compute Wheeler-Kiladis figures. These were created by Mike S. Pritchard
• wk_spectrum: Contains python code to compute Wheeler-Kiladis diagrams provided by Shuguang Wang.
• wheeler-kiladis: Contains W-K diagrams shown in the VAE paper.
• save_weights.py: Saves the weights, biases and normalization vectors in text files. These are then used as input for the climate model.
• List_of_Figures.txt: Contains a description where to find the python code to reproduce the figures of the VAE paper
• License: Contains the license information of this repository