Open NoraLoose opened 1 year ago
@karlotness Is there some code (repo for differentiable model and / or training) related to your recent work that we could highlight on the website?
@aakashsane is there some code related to your recent paper that we could highlight on the website? I'm thinking either a github repo with code for training the NN and / or a PR that implements the NN in MOM6.
@dhruvbalwada have you submitted the ANN module as a PR to MOM6 already? Any other code to highlight from your work?
@chzhangudel have you submitted the GZ21 module as a PR to MOM6? Otherwise / alternatively we can link this repo on the website: https://github.com/chzhangudel/Forpy_CNN_GZ21
Are folks using xgcm? Would love to get some more publicity to that if possible.
I couldn't imagine a life without xgcm! 😎 We can add it to the software section.
For the work I've been doing recently, I haven't opened up the actual experiment code quite yet (but hopefully soon).
The JAX QG model we've been using as part of that project is available though. The code for that is here: https://github.com/karlotness/pyqg-jax/
Here is a list of datasets and repos that we could highlight on the website (including the ones that are already there, but with more meta info). Sometimes there are several repos associated with an item. I suggest to link the one that I did not put into parentheses.
Machine learning tutorials and tools
[x] Machine Learning tutorial for Lorenz 96:
Book: https://m2lines.github.io/L96_demo/intro.html
Repo: https://github.com/m2lines/L96_demo
[x] Equation discovery:
Paper: https://onlinelibrary.wiley.com/doi/abs/10.1029/2022MS003258
Repo: https://github.com/m2lines/EquationDisco
[x] Geospatial ML prediction workflow
Repo: https://github.com/anastasiaGor/geoTrainFlow
Software packages
[x] GCM-Filters
Paper: https://joss.theoj.org/papers/10.21105/joss.03947
Documentation: https://gcm-filters.readthedocs.io/en/latest/index.html
Repo: https://github.com/ocean-eddy-cpt/gcm-filters
[x] xgcm
Documentation: https://xgcm.readthedocs.io/en/latest/index.html
Repo: https://github.com/xgcm/xgcm
Benchmark datasets
[x] Pyqg parameterization benchmarks (Ross et al., 2023)
Paper: https://onlinelibrary.wiley.com/doi/abs/10.1029/2022MS003258
Book: https://m2lines.github.io/MLwithQG/intro.html
Repos:
Datasets: hosted on globus, access as described here, e.g.:
[x] Bias corrected ERA5 skin temperature over Arctic sea ice
Paper: https://doi.org/10.1175/MWR-D-22-0130.1
Dataset: https://zenodo.org/record/8338265
Datasets accessible in the cloud
[x] CM2.6
[ ] MOM6 simulations with ML parameterizations
simulation with ZB20
simulation with ePBL_NN
need status update from @jbusecke
Models and implementation of parameterizations
[x] Differentiable QG model in pytorch
Repo: https://github.com/hrkz/torchqg
Paper: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022MS003124
[x] Differentiable QG model in JAX
Repo: https://github.com/karlotness/pyqg-jax/
Documentation: https://pyqg-jax.readthedocs.io/en/latest/index.html
Paper: https://www.climatechange.ai/papers/iclr2023/60
[x] Stacked shallow water model with stochastic subgrid momentum parameterization
Repo: https://github.com/arthurBarthe/swe_stochastic_param/tree/0.1
Paper: http://onlinelibrary.wiley.com/doi/abs/10.1029/2021MS002534
[x] Implementation of parameterizations in pyqg
Pull request: https://github.com/pyqg/pyqg/pull/266
Paper: https://onlinelibrary.wiley.com/doi/abs/10.1029/2022MS003258
[x] Implementation of data-driven parameterizations in MOM6
Pull request: https://github.com/NOAA-GFDL/MOM6/pull/356
Paper: Perezhogin et al. (2023), in prep.
[x] Implementation of stochastic parameterization in MOM6
Code: https://github.com/chzhangudel/Forpy_CNN_GZ21
Paper: http://arxiv.org/abs/2303.00962
Ocean parameterizations
[x] Stochastic parameterization of subgrid momentum forcing (Guillaumin and Zanna, 2021)
Paper: http://onlinelibrary.wiley.com/doi/abs/10.1029/2021MS002534
Repo:
[x] Generative data-driven approaches for stochastic subgrid parameterizations (Perezhogin et al., 2023)
Preprint: https://arxiv.org/abs/2302.07984
Repo: https://github.com/m2lines/pyqg_generative
[x] Neural network parameterization for vertical mixing
Preprint: https://arxiv.org/abs/2306.09045
Dataset and code: https://doi.org/10.5281/zenodo.7955323
Atmospheric parameterizations
[x] Neural networks for parameterization of subgrid atmospheric processes (Yuval et al., 2021)
Paper: https://onlinelibrary.wiley.com/doi/abs/10.1029/2020GL091363
Repo:
Datasets: https://drive.google.com/drive/folders/1TRPDL6JkcLjgTHJL9Ib_Z4XuPyvNVIyY
[x] Random forest to learn atmospheric parameterization
Paper: https://www.nature.com/articles/s41467-020-17142-3
Repo:
[x] Neural-network parameterization of subgrid momentum transport in the atmosphere (Yuval and O’Gorman, 2023)
Paper: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023MS003606
Repo:
Datasets: https://drive.google.com/drive/folders/1TRPDL6JkcLjgTHJL9Ib_Z4XuPyvNVIyY
Sea ice parameterizations