A curated list of open technology projects to sustain a stable climate, energy supply, biodiversity and natural resources.

ClimateLearn

A Python library for accessing state-of-the-art climate data and machine learning models in a standardized, straightforward way.
https://github.com/aditya-grover/climate-learn

Category: Climate Change
Sub Category: Climate Data Access and Visualization

Keywords

climate-change climate-science deep-learning machine-learning

Last synced: about 13 hours ago
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Repository metadata

Source code for ClimateLearn

README.md

Documentation Status
CI Build Status
Code style: black
Google Colab

ClimateLearn is a Python library for accessing state-of-the-art climate data and machine learning models in a standardized, straightforward way. This library provides access to multiple datasets, a zoo of baseline approaches, and a suite of metrics and visualizations for large-scale benchmarking of statistical downscaling and temporal forecasting methods. For further context on our past motivation and future plans, check out our announcement blog post. Also, check out our arxiv preprint that showcases the flexibility of ClimateLearn in performing benchmarking and analysis on the robustness and transfer performance of deep learning models.

Usage

Python 3.8+ is required. The xESMF package has to be installed separately since one of its dependencies, ESMpy, is available only through Conda.

conda install -c conda-forge xesmf
pip install climate-learn

Quickstart

We have a quickstart notebook in the notebooks folder titled Quickstart.ipynb. It is intended for use in Google Colab and can be launched by clicking the Google Colab badge above or this link: https://colab.research.google.com/drive/1LcecQLgLtwaHOwbvJAxw9UjCxfM0RMrX?usp=sharing.

We also previewed some key features of ClimateLearn at a spotlight tutorial in the "Tackling Climate Change with Machine Learning" Workshop at the Neural Information Processing Systems 2022 Conference. The slides and recorded talk can be found on Climate Change AI's website.

Documentation

Find us on ReadTheDocs.

About Us

ClimateLearn is managed by the Machine Intelligence Group at UCLA, headed by Professor Aditya Grover.

Contributing

Contributions are welcome! See our contributing guide.

Citing ClimateLearn

If you use ClimateLearn in your research, please cite our paper:

@article{nguyen2023climatelearn,
  title={ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling},
  author={Nguyen, Tung and Jewik, Jason and Bansal, Hritik and Sharma, Prakhar and Grover, Aditya},
  journal={arXiv preprint arXiv:2307.01909},
  year={2023}
}

Citation (CITATION.cff)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: "ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling"
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Tung
    family-names: Nguyen
    email: [email protected]
    affiliation: 'University of California, Los Angeles'
  - given-names: Jason
    family-names: Jewik
    email: [email protected]
    affiliation: 'University of California, Los Angeles'
  - given-names: Hritik
    family-names: Bansal
    email: [email protected]
    affiliation: 'University of California, Los Angeles'
  - given-names: Prakhar
    family-names: Sharma
    email: [email protected]
    affiliation: 'University of California, Los Angeles'
  - given-names: Aditya
    family-names: Grover
    email: [email protected]
    affiliation: 'University of California, Los Angeles'
license: MIT
repository-code: "https://github.com/aditya-grover/climate-learn"

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Committers metadata

Last synced: 6 days ago

Total Commits: 326
Total Committers: 18
Avg Commits per committer: 18.111
Development Distribution Score (DDS): 0.798

Commits in past year: 50
Committers in past year: 8
Avg Commits per committer in past year: 6.25
Development Distribution Score (DDS) in past year: 0.56

Name Email Commits
jasonjewik j****k@c****u 66
tung-nd d****7@g****m 41
Jason Jewik j****k@g****m 39
Shashank Goel s****l@S****l 29
Shashank Goel s****l@S****n 23
Prakhar Sharma p****a@g****m 22
Jason Jewik j****k@u****u 21
Siddharth Nandy s****y@g****u 16
BRYAN(Jingchen) TANG t****8@u****u 15
Seongbin Park s****k@g****m 14
Prakhar Sharma 3****a 10
Siddharth Nandy s****y@g****m 8
Jingchen Tang [email protected] t****g@m****u 7
Hritikbansal h****n@g****m 4
Shashank Goel s****l@w****u 4
Aditya Grover a****1@g****m 3
se0ngbin 6****n 2
Jason Jewik j****b@g****m 2

Committer domains:


Issue and Pull Request metadata

Last synced: 1 day ago

Total issues: 42
Total pull requests: 86
Average time to close issues: about 1 month
Average time to close pull requests: 3 days
Total issue authors: 19
Total pull request authors: 12
Average comments per issue: 3.02
Average comments per pull request: 0.99
Merged pull request: 73
Bot issues: 0
Bot pull requests: 0

Past year issues: 5
Past year pull requests: 2
Past year average time to close issues: N/A
Past year average time to close pull requests: N/A
Past year issue authors: 4
Past year pull request authors: 2
Past year average comments per issue: 1.0
Past year average comments per pull request: 0.0
Past year merged pull request: 0
Past year bot issues: 0
Past year bot pull requests: 0

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/aditya-grover/climate-learn

Top Issue Authors

  • prakhar6sharma (16)
  • Escape142 (3)
  • se0ngbin (3)
  • Teenye (2)
  • bulaienTang (2)
  • jasonjewik (2)
  • patel-zeel (2)
  • linustws (1)
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  • CalibrationMe (1)
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Top Pull Request Authors

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  • se0ngbin (13)
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  • omid-bagheri-cee (1)
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  • aditya-grover (1)
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  • bercowsky (1)

Top Issue Labels

  • bug (19)
  • enhancement (12)
  • documentation (6)
  • good first issue (2)
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  • documentation (1)

Package metadata

pypi.org: climate-learn

ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling

  • Homepage:
  • Documentation: https://climatelearn.readthedocs.io/en/latest/
  • Licenses: MIT License
  • Latest release: 1.0.0 (published almost 2 years ago)
  • Last Synced: 2025-04-25T12:31:50.006Z (1 day ago)
  • Versions: 3
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 242 Last month
  • Rankings:
    • Stargazers count: 5.44%
    • Dependent packages count: 6.633%
    • Forks count: 7.047%
    • Downloads: 12.139%
    • Average: 12.374%
    • Dependent repos count: 30.611%
  • Maintainers (1)

Dependencies

requirements.txt pypi
  • cdsapi *
  • importlib-metadata ==4.13.0
  • pytorch-lightning *
  • rich *
  • timm *
  • wandb *
.github/workflows/ci.yaml actions
  • actions/checkout v3 composite
  • actions/setup-python v4 composite
  • psf/black stable composite
pyproject.toml pypi
  • cdsapi >=0.5.1
  • dask >=2022.2.0
  • importlib-metadata ==4.13.0
  • matplotlib >=3.5.3
  • netcdf4 >=1.6.2
  • pytorch-lightning >=1.9.0
  • rasterio >=1.3.7
  • scikit-learn >=1.0.2
  • tensorboard ==2.11.2
  • timm ==0.9.2
  • wandb >=0.13.9
  • xarray >=0.20.2

Score: 14.209433308617163