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 2 hours ago
JSON representation
Repository metadata
Source code for ClimateLearn
- Host: GitHub
- URL: https://github.com/aditya-grover/climate-learn
- Owner: aditya-grover
- License: mit
- Created: 2022-09-07T07:18:30.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-03-16T00:32:13.000Z (almost 2 years ago)
- Last Synced: 2025-12-21T01:47:30.670Z (6 days ago)
- Topics: climate-change, climate-science, deep-learning, machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 15.5 MB
- Stars: 351
- Watchers: 4
- Forks: 52
- Open Issues: 9
- Releases: 1
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Citation: CITATION.cff
- Codeowners: .github/CODEOWNERS
README.md
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: tungnd@cs.ucla.edu
affiliation: 'University of California, Los Angeles'
- given-names: Jason
family-names: Jewik
email: jason.jewik@ucla.edu
affiliation: 'University of California, Los Angeles'
- given-names: Hritik
family-names: Bansal
email: hbansal@ucla.edu
affiliation: 'University of California, Los Angeles'
- given-names: Prakhar
family-names: Sharma
email: prakhar6sharma@gmail.com
affiliation: 'University of California, Los Angeles'
- given-names: Aditya
family-names: Grover
email: adityag@cs.ucla.edu
affiliation: 'University of California, Los Angeles'
license: MIT
repository-code: "https://github.com/aditya-grover/climate-learn"
Owner metadata
- Name: Aditya Grover
- Login: aditya-grover
- Email:
- Kind: user
- Description: Assistant Professor of Computer Science at UCLA
- Website: http://aditya-grover.github.io
- Location:
- Twitter:
- Company:
- Icon url: https://avatars.githubusercontent.com/u/9116230?u=66ed6dcb0bc82ec7d8a99c9a654cb6fe328fda33&v=4
- Repositories: 15
- Last ynced at: 2023-08-02T05:33:35.387Z
- Profile URL: https://github.com/aditya-grover
GitHub Events
Total
- Issues event: 1
- Watch event: 39
- Issue comment event: 2
- Pull request event: 2
- Fork event: 4
Last Year
- Watch event: 24
- Pull request event: 1
- Fork event: 3
Committers metadata
Last synced: 1 day ago
Total Commits: 326
Total Committers: 13
Avg Commits per committer: 25.077
Development Distribution Score (DDS): 0.798
Commits in past year: 0
Committers in past year: 0
Avg Commits per committer in past year: 0.0
Development Distribution Score (DDS) in past year: 0.0
| Name | Commits | |
|---|---|---|
| jasonjewik | j****k@c****u | 66 |
| Jason Jewik | j****k@g****m | 62 |
| tung-nd | d****7@g****m | 41 |
| Prakhar Sharma | p****a@g****m | 32 |
| Shashank Goel | s****l@S****l | 29 |
| Siddharth Nandy | s****y@g****u | 24 |
| Shashank Goel | s****l@S****n | 23 |
| Seongbin Park | s****k@g****m | 16 |
| BRYAN(Jingchen) TANG | t****8@u****u | 15 |
| Jingchen Tang tangtang1228@g.ucla.edu | 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 |
Committer domains:
- wifi-131-179-38-137.host.ucla.edu: 1
- mint1.cs.ucla.edu: 1
- ucla.edu: 1
- shashanks-air.lan: 1
- g.ucla.edu: 1
- cs.ucla.edu: 1
Issue and Pull Request metadata
Last synced: 4 months ago
Total issues: 40
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: 3.1
Average comments per pull request: 0.99
Merged pull request: 73
Bot issues: 0
Bot pull requests: 0
Past year issues: 2
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: 2
Past year pull request authors: 2
Past year average comments per issue: 2.5
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
Top Issue Authors
- prakhar6sharma (15)
- se0ngbin (3)
- Escape142 (2)
- bulaienTang (2)
- jasonjewik (2)
- linustws (1)
- ajikmr (1)
- CalibrationMe (1)
- arthurfeeney (1)
- blue-ocean-climate (1)
- patel-zeel (1)
- vargpt (1)
- noeliaof (1)
- Skerre (1)
- jovidsilva (1)
Top Pull Request Authors
- jasonjewik (35)
- se0ngbin (13)
- prakhar6sharma (13)
- tung-nd (7)
- bulaienTang (6)
- siddharthnandy (5)
- omid-bagheri-cee (2)
- arthurfeeney (2)
- aditya-grover (1)
- srikeerthi207 (1)
- bercowsky (1)
- rohanshah13 (1)
Top Issue Labels
- bug (18)
- enhancement (9)
- documentation (5)
- good first issue (2)
- help wanted (1)
Top Pull Request Labels
- documentation (1)
Package metadata
- Total packages: 1
-
Total downloads:
- pypi: 82 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 3
- Total maintainers: 1
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 over 2 years ago)
- Last Synced: 2025-12-23T19:05:08.882Z (3 days ago)
- Versions: 3
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 82 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
- cdsapi *
- importlib-metadata ==4.13.0
- pytorch-lightning *
- rich *
- timm *
- wandb *
- actions/checkout v3 composite
- actions/setup-python v4 composite
- psf/black stable composite
- 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: 12.86989399670829