GraphDOP: Towards skilful data-driven medium-range weather forecasts learnt and initialised directly from observations
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arXiv
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| Autori principali: | , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| author | Alexe, Mihai Boucher, Eulalie Lean, Peter Pinnington, Ewan Laloyaux, Patrick McNally, Anthony Lang, Simon Chantry, Matthew Burrows, Chris Chrust, Marcin Pinault, Florian Villeneuve, Ethel Bormann, Niels Healy, Sean |
| author_facet | Alexe, Mihai Boucher, Eulalie Lean, Peter Pinnington, Ewan Laloyaux, Patrick McNally, Anthony Lang, Simon Chantry, Matthew Burrows, Chris Chrust, Marcin Pinault, Florian Villeneuve, Ethel Bormann, Niels Healy, Sean |
| contents | We introduce GraphDOP, a new data-driven, end-to-end forecast system developed at the European Centre for Medium-Range Weather Forecasts (ECMWF) that is trained and initialised exclusively from Earth System observations, with no physics-based (re)analysis inputs or feedbacks. GraphDOP learns the correlations between observed quantities - such as brightness temperatures from polar orbiters and geostationary satellites - and geophysical quantities of interest (that are measured by conventional observations), to form a coherent latent representation of Earth System state dynamics and physical processes, and is capable of producing skilful predictions of relevant weather parameters up to five days into the future. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_15687 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GraphDOP: Towards skilful data-driven medium-range weather forecasts learnt and initialised directly from observations Alexe, Mihai Boucher, Eulalie Lean, Peter Pinnington, Ewan Laloyaux, Patrick McNally, Anthony Lang, Simon Chantry, Matthew Burrows, Chris Chrust, Marcin Pinault, Florian Villeneuve, Ethel Bormann, Niels Healy, Sean Atmospheric and Oceanic Physics Machine Learning We introduce GraphDOP, a new data-driven, end-to-end forecast system developed at the European Centre for Medium-Range Weather Forecasts (ECMWF) that is trained and initialised exclusively from Earth System observations, with no physics-based (re)analysis inputs or feedbacks. GraphDOP learns the correlations between observed quantities - such as brightness temperatures from polar orbiters and geostationary satellites - and geophysical quantities of interest (that are measured by conventional observations), to form a coherent latent representation of Earth System state dynamics and physical processes, and is capable of producing skilful predictions of relevant weather parameters up to five days into the future. |
| title | GraphDOP: Towards skilful data-driven medium-range weather forecasts learnt and initialised directly from observations |
| topic | Atmospheric and Oceanic Physics Machine Learning |
| url | https://arxiv.org/abs/2412.15687 |