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
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 2 years ago)
- Default Branch: main
- Last Pushed: 2024-03-16T00:32:13.000Z (about 1 year ago)
- Last Synced: 2025-04-18T21:19:39.130Z (8 days ago)
- Topics: climate-change, climate-science, deep-learning, machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 15.5 MB
- Stars: 330
- 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: [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"
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: 29
- Issue comment event: 2
- Pull request event: 2
- Fork event: 4
Last Year
- Issues event: 1
- Watch event: 29
- Issue comment event: 2
- Pull request event: 2
- Fork event: 4
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 | 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:
- ucla.edu: 2
- wifi-131-179-38-137.host.ucla.edu: 1
- mint1.cs.ucla.edu: 1
- g.ucla.edu: 1
- shashanks-air.lan: 1
- cs.ucla.edu: 1
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
Top Issue Authors
- prakhar6sharma (16)
- Escape142 (3)
- se0ngbin (3)
- Teenye (2)
- bulaienTang (2)
- jasonjewik (2)
- patel-zeel (2)
- linustws (1)
- jdberman (1)
- ajikmr (1)
- CalibrationMe (1)
- arthurfeeney (1)
- blue-ocean-climate (1)
- vargpt (1)
- noeliaof (1)
Top Pull Request Authors
- jasonjewik (36)
- se0ngbin (13)
- prakhar6sharma (13)
- tung-nd (7)
- bulaienTang (6)
- siddharthnandy (5)
- omid-bagheri-cee (1)
- arthurfeeney (1)
- aditya-grover (1)
- rohanshah13 (1)
- srikeerthi207 (1)
- bercowsky (1)
Top Issue Labels
- bug (19)
- enhancement (12)
- documentation (6)
- good first issue (2)
- help wanted (2)
Top Pull Request Labels
- documentation (1)
Package metadata
- Total packages: 1
-
Total downloads:
- pypi: 242 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 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
- 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: 14.209433308617163