Aurora: A Foundation Model of the Atmosphere

Welcome to the documentation of Aurora! Here you will detailed instructions for using the model. If you just want to see the model in action, you can skip to a full-fledged example that runs the model on ERA5. For details on how exactly the model works, please see the paper on arXiv.

Aurora is a machine learning model that can predict atmospheric variables, such as temperature. It is a foundation model, which means that it was first generally trained on a lot of data, and then can adapted to specialised atmospheric forecasting tasks with relatively little data. We provide three such specialised versions: one for medium-resolution weather prediction, one for high-resolution weather prediction, and one for air pollution prediction.

The package currently includes the pretrained model and the fine-tuned version for high-resolution weather forecasting. We are working on the fine-tuned version for air pollution forecasting, which will be included in due time.

Cite us as follows:

@misc{bodnar2024aurora,
    title = {Aurora: A Foundation Model of the Atmosphere},
    author = {Cristian Bodnar and Wessel P. Bruinsma and Ana Lucic and Megan Stanley and Johannes Brandstetter and Patrick Garvan and Maik Riechert and Jonathan Weyn and Haiyu Dong and Anna Vaughan and Jayesh K. Gupta and Kit Tambiratnam and Alex Archibald and Elizabeth Heider and Max Welling and Richard E. Turner and Paris Perdikaris},
    year = {2024},
    url = {https://arxiv.org/abs/2405.13063},
    eprint = {2405.13063},
    archivePrefix = {arXiv},
    primaryClass = {physics.ao-ph},
}