Oskarsson, Joel;
Landelius, Tomas;
Deisenroth, Marc Peter;
Lindsten, Fredrik;
(2024)
Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks.
In: Globersons, Amir and Mackey, Lester and Belgrave, Danielle and Fan, Angela and Paquet, Ulrich and Tomczak, Jakub M and Zhang, Cheng, (eds.)
Proceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS 2024).
(pp. pp. 1-72).
NeurIPS
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Abstract
In recent years, machine learning has established itself as a powerful tool for high-resolution weather forecasting. While most current machine learning models focus on deterministic forecasts, accurately capturing the uncertainty in the chaotic weather system calls for probabilistic modeling. We propose a probabilistic weather forecasting model called Graph-EFM, combining a flexible latent-variable formulation with the successful graph-based forecasting framework. The use of a hierarchical graph construction allows for efficient sampling of spatially coherent forecasts. Requiring only a single forward pass per time step, Graph-EFM allows for fast generation of arbitrarily large ensembles. We experiment with the model on both global and limited area forecasting. Ensemble forecasts from Graph-EFM achieve equivalent or lower errors than comparable deterministic models, with the added benefit of accurately capturing forecast uncertainty.
| Type: | Proceedings paper |
|---|---|
| Title: | Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks |
| Event: | 38th Conference on Neural Information Processing Systems (NeurIPS 2024) |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://papers.nips.cc/paper_files/paper/2024 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher's terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10205688 |
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