Apply machine learning to predict climate variables into the future and transform low-resolution outputs of climate models into high-resolution regional forecasts.
Author(s):
- Hritik Bansal, University of California Los Angeles, hbansal@g.ucla.edu
- Shashank Goel, University of California Los Angeles, shashankgoel@g.ucla.edu
- Tung Nguyen, University of California Los Angeles, tungnd@g.ucla.edu
- Aditya Grover, University of California Los Angeles, adityag@cs.ucla.edu
Originally presented at NeurIPS 2022
We recommend executing this notebook in a Colab environment to gain access to GPUs and to manage all necessary dependencies.
Estimated time to execute end-to-end: 20 minutes
Please refer to these GitHub instructions to open a pull request via the "fork and pull request" workflow.
Pull requests will be reviewed by members of the Climate Change AI Tutorials team for relevance, accuracy, and conciseness.
Check out the tutorials page on our website for a full list of tutorials demonstrating how AI can be used to tackle problems related to climate change.
Usage of this tutorial is subject to the MIT License.
Bansal, H., Goel, S., Nguyen, T., & Grover, A. (2022). ClimateLearn: Machine Learning for Predicting Weather and Climate [Tutorial]. In Conference on Neural Information Processing Systems. Climate Change AI. https://doi.org/10.5281/zenodo.11620005
@misc{bansal2022climatelearn:,
title={ClimateLearn: Machine Learning for Predicting Weather and Climate},
author={Bansal, Hritik and Goel, Shashank and Nguyen, Tung and Grover, Aditya},
year={2022},
organization={Climate Change AI},
type={Tutorial},
doi={https://doi.org/10.5281/zenodo.11620005},
booktitle={Conference on Neural Information Processing Systems},
howpublished={\url{https://github.com/climatechange-ai-tutorials/climatelearn}}
}