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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2410.15076 |
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| _version_ | 1866917826579660800 |
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| author | Molinaro, Roberto Daubinet, Jordan Dane Dautel, Alexander Jakob Schlueter, Andreas Grigoryev, Alex Ekhtiari, Nikoo Steunebrink, Bas Thiart, Kevin Song, Roan John Martin, Henry Wagner, Leonie Giussani, Andrea Gabler, Marvin Vincent |
| author_facet | Molinaro, Roberto Daubinet, Jordan Dane Dautel, Alexander Jakob Schlueter, Andreas Grigoryev, Alex Ekhtiari, Nikoo Steunebrink, Bas Thiart, Kevin Song, Roan John Martin, Henry Wagner, Leonie Giussani, Andrea Gabler, Marvin Vincent |
| contents | We announce the release of EPT-1.5, the latest iteration in our Earth Physics Transformer (EPT) family of foundation AI earth system models. EPT-1.5 demonstrates substantial improvements over its predecessor, EPT-1. Built specifically for the European energy industry, EPT-1.5 shows remarkable performance in predicting energy-relevant variables, particularly 10m & 100m wind speed and solar radiation. Especially in wind prediction, it outperforms existing AI weather models like GraphCast, FuXi, and Pangu-Weather, as well as the leading numerical weather model, IFS HRES by the European Centre for Medium-Range Weather Forecasts (ECMWF), setting a new state of the art. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_15076 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EPT-1.5 Technical Report Molinaro, Roberto Daubinet, Jordan Dane Dautel, Alexander Jakob Schlueter, Andreas Grigoryev, Alex Ekhtiari, Nikoo Steunebrink, Bas Thiart, Kevin Song, Roan John Martin, Henry Wagner, Leonie Giussani, Andrea Gabler, Marvin Vincent Artificial Intelligence We announce the release of EPT-1.5, the latest iteration in our Earth Physics Transformer (EPT) family of foundation AI earth system models. EPT-1.5 demonstrates substantial improvements over its predecessor, EPT-1. Built specifically for the European energy industry, EPT-1.5 shows remarkable performance in predicting energy-relevant variables, particularly 10m & 100m wind speed and solar radiation. Especially in wind prediction, it outperforms existing AI weather models like GraphCast, FuXi, and Pangu-Weather, as well as the leading numerical weather model, IFS HRES by the European Centre for Medium-Range Weather Forecasts (ECMWF), setting a new state of the art. |
| title | EPT-1.5 Technical Report |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2410.15076 |