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Notebook","category":"Climate Change","sub_category":"Climate Data Access and Visualization","monthly_downloads":76,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"\u003ch1 align=\"center\"\u003eClimateLearn\u003c/h1\u003e\n\n[![Documentation Status](https://readthedocs.org/projects/climatelearn/badge/?version=latest)](https://climatelearn.readthedocs.io/en/latest/?badge=latest)\n[![CI Build Status](https://github.com/aditya-grover/climate-learn/actions/workflows/ci.yaml/badge.svg)](https://github.com/aditya-grover/climate-learn/actions/workflows/ci.yaml)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n[![Google Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1LcecQLgLtwaHOwbvJAxw9UjCxfM0RMrX?usp=sharing)\n\n**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.\n\n## Usage\n\n[**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.\n```\nconda install -c conda-forge xesmf\npip install climate-learn\n```\n\n### Quickstart\nWe 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.\n\nWe 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).\n\n### Documentation\nFind us on [ReadTheDocs](https://climatelearn.readthedocs.io/).\n\n## About Us\nClimateLearn is managed by the Machine Intelligence Group at UCLA, headed by [Professor Aditya Grover](https://aditya-grover.github.io).\n\n## Contributing\nContributions are welcome! 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