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GraphDOP: Towards skilful data-driven medium-range weather forecasts learnt and initialised directly from observations
Mihai Alexe, Eulalie Boucher, Peter Lean, Ewan Pinnington, Patri · 2024-12-20 · via cs.LG updates on arXiv.org

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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