FourCastNet: A practical introduction to a state-of-the-art deep learning global weather emulator

Learn how to use FourCastNet, a weather model based on deep learning, to obtain short to medium-range forecasts of crucial atmospheric variables such as surface wind velocities.

Author(s):

• Jaideep Pathak, NVIDIA, jpathak@nvidia.com
• Shashank Subramanian, Lawrence Berkeley National Laboratory, shashanksubramanian@lbl.gov
• Peter Harrington, Lawrence Berkeley National Laboratory, pharrington@lbl.gov
• Thorsten Kurth, NVIDIA, tkurth@nvidia.com
• Andre Graubner, NVIDIA, agraubner@nvidia.com
• Morteza Mardani, NVIDIA, mmardani@nvidia.com
• David Hall, NVIDIA, dhall@nvidia.com
• Karthik Kashinath, NVIDIA, kkashinath@nvidia.com
• Anima Anandkumar, NVIDIA, aanandkumar@nvidia.com

Originally presented at NeurIPS 2022

Access this tutorial

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: 15 minutes

Contribute to this tutorial

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.

Climate Change AI Tutorials

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.

License

Usage of this tutorial is subject to the MIT License.

Cite

Plain Text

Pathak, J., Subramanian, S., Harrington, P., Kurth, T., Graubner, A., Mardani, M., Hall, D., Kashinath, K., & Anandkumar, A. (2022). FourCastNet: A practical introduction to a state-of-the-art deep learning global weather emulator [Tutorial]. In Conference on Neural Information Processing Systems. Climate Change AI. https://doi.org/10.5281/zenodo.11621432

BibTeX

@misc{pathak2022fourcastnet:,
  title={FourCastNet: A practical introduction to a state-of-the-art deep learning global weather emulator},
  author={Pathak, Jaideep and Subramanian, Shashank and Harrington, Peter and Kurth, Thorsten and Graubner, Andre and Mardani, Morteza and Hall, David and Kashinath, Karthik and Anandkumar, Anima},
  year={2022},
  organization={Climate Change AI},
  type={Tutorial},
  doi={https://doi.org/10.5281/zenodo.11621432},
  booktitle={Conference on Neural Information Processing Systems},
  howpublished={\url{https://github.com/climatechange-ai-tutorials/fourcastnet}}
}