AI-boosted rare event sampling to characterize extreme weather
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914268060844032 |
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| author | Lancelin, Amaury Wikner, Alex Dubus, Laurent Priol, Clément Le Abbot, Dorian S. Bouchet, Freddy Hassanzadeh, Pedram Weare, Jonathan |
| author_facet | Lancelin, Amaury Wikner, Alex Dubus, Laurent Priol, Clément Le Abbot, Dorian S. Bouchet, Freddy Hassanzadeh, Pedram Weare, Jonathan |
| contents | Weather extremes pose major societal risks, especially in a changing climate, but due to their rarity, they are difficult to study using limited observations or complex climate models. We introduce AI+RES, a framework coupling fast AI weather forecasts with a high-fidelity physics model using a rare-event algorithm to efficiently characterize extremes. This approach enables the study of the statistics and physics of very rare events, such as once per millennium heatwaves at two orders-of-magnitude lower computational cost. AI+RES can be applied broadly across climate science and other fields concerned with rare events. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_27066 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-boosted rare event sampling to characterize extreme weather Lancelin, Amaury Wikner, Alex Dubus, Laurent Priol, Clément Le Abbot, Dorian S. Bouchet, Freddy Hassanzadeh, Pedram Weare, Jonathan Atmospheric and Oceanic Physics Computation Machine Learning Weather extremes pose major societal risks, especially in a changing climate, but due to their rarity, they are difficult to study using limited observations or complex climate models. We introduce AI+RES, a framework coupling fast AI weather forecasts with a high-fidelity physics model using a rare-event algorithm to efficiently characterize extremes. This approach enables the study of the statistics and physics of very rare events, such as once per millennium heatwaves at two orders-of-magnitude lower computational cost. AI+RES can be applied broadly across climate science and other fields concerned with rare events. |
| title | AI-boosted rare event sampling to characterize extreme weather |
| topic | Atmospheric and Oceanic Physics Computation Machine Learning |
| url | https://arxiv.org/abs/2510.27066 |