An update to ECMWF's machine-learned weather forecast model AIFS
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866909802418929664 |
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| author | Moldovan, Gabriel Pinnington, Ewan Nemesio, Ana Prieto Lang, Simon Bouallègue, Zied Ben Dramsch, Jesper Alexe, Mihai Cruz, Mario Santa Hahner, Sara Cook, Harrison Theissen, Helen Clare, Mariana O'Brien, Cathal Polster, Jan Magnusson, Linus Mertes, Gert Pinault, Florian Raoult, Baudouin de Rosnay, Patricia Forbes, Richard Chantry, Matthew |
| author_facet | Moldovan, Gabriel Pinnington, Ewan Nemesio, Ana Prieto Lang, Simon Bouallègue, Zied Ben Dramsch, Jesper Alexe, Mihai Cruz, Mario Santa Hahner, Sara Cook, Harrison Theissen, Helen Clare, Mariana O'Brien, Cathal Polster, Jan Magnusson, Linus Mertes, Gert Pinault, Florian Raoult, Baudouin de Rosnay, Patricia Forbes, Richard Chantry, Matthew |
| contents | We present an update to ECMWF's machine-learned weather forecasting model AIFS Single with several key improvements. The model now incorporates physical consistency constraints through bounding layers, an updated training schedule, and an expanded set of variables. The physical constraints substantially improve precipitation forecasts and the new variables show a high level of skill. Upper-air headline scores also show improvement over the previous AIFS version. The AIFS has been fully operational at ECMWF since the 25th of February 2025. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18994 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An update to ECMWF's machine-learned weather forecast model AIFS Moldovan, Gabriel Pinnington, Ewan Nemesio, Ana Prieto Lang, Simon Bouallègue, Zied Ben Dramsch, Jesper Alexe, Mihai Cruz, Mario Santa Hahner, Sara Cook, Harrison Theissen, Helen Clare, Mariana O'Brien, Cathal Polster, Jan Magnusson, Linus Mertes, Gert Pinault, Florian Raoult, Baudouin de Rosnay, Patricia Forbes, Richard Chantry, Matthew Atmospheric and Oceanic Physics We present an update to ECMWF's machine-learned weather forecasting model AIFS Single with several key improvements. The model now incorporates physical consistency constraints through bounding layers, an updated training schedule, and an expanded set of variables. The physical constraints substantially improve precipitation forecasts and the new variables show a high level of skill. Upper-air headline scores also show improvement over the previous AIFS version. The AIFS has been fully operational at ECMWF since the 25th of February 2025. |
| title | An update to ECMWF's machine-learned weather forecast model AIFS |
| topic | Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2509.18994 |