Hi, thanks for checking out this repository. It contains the code that was used for Rasp et al. 2018 and serves as the basis for ongoing work. In particular, check out the climate_invariant branch for Tom Beucler's work on physically consistent ML parameterizations.
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:
Set up the conda environment using the env.yml file
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 =======
climate_invar_classification
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.
The process of creating a model is as follows.
To preprocess the data you the major files are
preprocessing.py and convert_dataset.py. You can check out this notebook for more information about it.
Once the data is processed we can train the model as a whole or in progression. Below is the architecture of the whole network
Inp -> RH Transformation -> LH Transformation -> T-TNS Transformation -> Split + Scaling -> Vertical Interpolation.
Check out this notebook to know more about training the network.
Once the model is trained you can run model diagnostics to visualize the learnings.
Check out this notebook to know more about model diagnostics.