Existing El Niño forecasts use dynamical models that rely on the physics of the atmosphere and ocean. Learn how to create El Niño forecasts using machine learning instead, which uses statistical optimization to issue forecasts.
Author(s):
- Ankur Mahesh (University of California Berkeley)
- Mel Hanna (Climate Change AI)
Originally presented at Climate Change AI Summer School 2022
Introductory slides can be accessed here.
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: 10 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.
Mahesh, A., & Hanna, M. (2024). Forecasting the El Nino/ Southern Oscillation with Machine Learning [Tutorial]. In Climate Change AI Summer School. Climate Change AI. https://doi.org/10.5281/zenodo.11624957
@misc{mahesh2024forecasting,
title={Forecasting the El Nino/ Southern Oscillation with Machine Learning},
author={Mahesh, Ankur and Hanna, Melanie},
year={2024},
organization={Climate Change AI},
type={Tutorial},
doi={https://doi.org/10.5281/zenodo.11624957},
booktitle={Climate Change AI Summer School},
howpublished={\url{https://github.com/climatechange-ai-tutorials/seasonal-forecasting}}
}