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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.

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# 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. <a target="_blank" href="https://colab.research.google.com/github/climatechange-ai-tutorials/fourcastnet/blob/main/FourCastNet_A_practical_introduction_to_a_state_of_the_art_deep_learning_global_weather_emulator.ipynb">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>

Estimated time to execute end-to-end: 15 minutes

## Contribute to this tutorial

Please refer to these [GitHub instructions](https://docs.github.com/en/get-started/exploring-projects-on-github/contributing-to-a-project#about-forking) 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](https://www.climatechange.ai/tutorials?) 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}}
}
```

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