GenCast: learning skillful ensemble forecasting of medium-range weather

Abstract

A probabilistic weather forecast is critical for decision-making, from the everyday to preparation for extreme events. Forecasts of the joint distribution of weather trajectories via spatio-temporally coherent ensembles have further importance: they provide a powerful tool for decision-making in complex and high-impact domains including energy system planning, transportation routing, flood forecasting and more. State-of-the-art ML forecast models for medium-range weather, however, are largely trained to produce deterministic forecasts which lose physical consistency at longer lead times. We introduce GenCast, a ML-based generative model for ensemble weather forecasting, trained from reanalysis data. It forecasts ensembles of trajectories for hundreds of weather variables, up to 15 days at 1 degree resolution globally, using under TC secs per ensemble member. We show that GenCast is more skilful than ENS, a top operational ensemble forecast, for TC% of TC verification targets, while maintaining good calibration and physically consistent power spectra.

Authors

Ilan Price, Matthew Willson, Alvaro Sanchez, Peter Battaglia, Remi Lam, Ferran Alet, Jacklynn Stott, Timo Ewalds, Shakir Mohamed

Venue

arXiv