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

• URL: http://arxiv.org/abs/2412.15687v1
• Date: Fri, 20 Dec 2024 09:06:14 GMT
• Title: GraphDOP: Towards skilful data-driven medium-range weather forecasts learnt and initialised directly from observations
• Authors: Mihai Alexe, Eulalie Boucher, Peter Lean, Ewan Pinnington, Patrick Laloyaux, Anthony McNally, Simon Lang, Matthew Chantry, Chris Burrows, Marcin Chrust, Florian Pinault, Ethel Villeneuve, Niels Bormann, Sean Healy,
• Abstract summary: GraphDOP learns the correlations between observed quantities and geophysical quantities of interest. It is capable of producing skilful predictions of relevant weather parameters up to five days into the future.
• Score: 0.22824658065924633
• License:
• Abstract: 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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