GraphDOP: Towards skilful data-driven medium-range weather forecasts learnt and initialised directly from observations

Mihai Alexe , Eulalie Boucher , Peter Lean , Ewan Pinnington , Patrick Laloyaux , A. P. McNally , Simon Lang , Matthew Chantry , C. J. Burrows , Marcin Chrust , Florian Pinault , Ethel Villeneuve , Niels Bormann , S. B. Healy ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2412.15687

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.

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Climatology Environmental science Range (aeronautics) Meteorology Computer science Geography Geology Aerospace engineering Engineering

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2412.15687
PDF: https://arxiv.org/pdf/2412.15687
OA Status: green
Cited By: 1
Related Works: 10
OpenAlex ID: https://openalex.org/W4405715399

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