Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866914991539486720 |
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| author | Oskarsson, Joel Landelius, Tomas Deisenroth, Marc Peter Lindsten, Fredrik |
| author_facet | Oskarsson, Joel Landelius, Tomas Deisenroth, Marc Peter Lindsten, Fredrik |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_04759 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks Oskarsson, Joel Landelius, Tomas Deisenroth, Marc Peter Lindsten, Fredrik Machine Learning 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. |
| title | Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.04759 |