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https://github.com/aditya-grover/climate-learn
Source code for ClimateLearn
https://github.com/aditya-grover/climate-learn
climate-change climate-science deep-learning machine-learning
Last synced: 2 days ago
JSON representation
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 (10 months ago)
- Last Synced: 2024-10-29T21:01:14.464Z (3 months ago)
- Topics: climate-change, climate-science, deep-learning, machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 15.5 MB
- Stars: 308
- Watchers: 6
- Forks: 49
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Citation: CITATION.cff
- Codeowners: .github/CODEOWNERS
Awesome Lists containing this project
- awesome-meteo - ClimateLearn - 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. (Uncategorized / Uncategorized)
- open-sustainable-technology - ClimateLearn - A Python library for accessing state-of-the-art climate data and machine learning models in a standardized, straightforward way. (Climate Change / Climate Data Access and Visualization)
README
ClimateLearn
[](https://climatelearn.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/aditya-grover/climate-learn/actions/workflows/ci.yaml)
[](https://github.com/psf/black)
[](https://colab.research.google.com/drive/1LcecQLgLtwaHOwbvJAxw9UjCxfM0RMrX?usp=sharing)
**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](https://aditya-grover.github.io/blog/2023/climate-learn/). Also, check out our [arxiv preprint](https://arxiv.org/abs/2307.01909) 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+**](https://www.python.org/) 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](https://www.climatechange.ai/papers/neurips2022/114).
### Documentation
Find us on [ReadTheDocs](https://climatelearn.readthedocs.io/).
## About Us
ClimateLearn is managed by the Machine Intelligence Group at UCLA, headed by [Professor Aditya Grover](https://aditya-grover.github.io).
## Contributing
Contributions are welcome! See our [contributing guide](https://github.com/aditya-grover/climate-learn/blob/main/CONTRIBUTING.md).
## 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}
}
```