GenCast: Diffusion-based ensemble forecasting for medium-range weather

• URL: http://arxiv.org/abs/2312.15796v2
• Date: Wed, 1 May 2024 16:30:43 GMT
• Title: GenCast: Diffusion-based ensemble forecasting for medium-range weather
• Authors: Ilan Price, Alvaro Sanchez-Gonzalez, Ferran Alet, Tom R. Andersson, Andrew El-Kadi, Dominic Masters, Timo Ewalds, Jacklynn Stott, Shakir Mohamed, Peter Battaglia, Remi Lam, Matthew Willson,
• Abstract summary: We introduce GenCast, a probabilistic weather model with greater skill and speed than the top operational medium-range weather forecast in the world. GenCast generates an ensemble of 15-day global forecasts, at 12-hour steps and 0.25 degree latitude-longitude, for over 80 surface and atmospheric variables in 8 minutes. It has greater skill than ENS on 97.4% of 1320 targets we evaluated, and better predicts extreme weather, tropical cyclones, and wind power production.
• Score: 10.845679586464026
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Weather forecasts are fundamentally uncertain, so predicting the range of probable weather scenarios is crucial for important decisions, from warning the public about hazardous weather, to planning renewable energy use. Here, we introduce GenCast, a probabilistic weather model with greater skill and speed than the top operational medium-range weather forecast in the world, the European Centre for Medium-Range Forecasts (ECMWF)'s ensemble forecast, ENS. Unlike traditional approaches, which are based on numerical weather prediction (NWP), GenCast is a machine learning weather prediction (MLWP) method, trained on decades of reanalysis data. GenCast generates an ensemble of stochastic 15-day global forecasts, at 12-hour steps and 0.25 degree latitude-longitude resolution, for over 80 surface and atmospheric variables, in 8 minutes. It has greater skill than ENS on 97.4% of 1320 targets we evaluated, and better predicts extreme weather, tropical cyclones, and wind power production. This work helps open the next chapter in operational weather forecasting, where critical weather-dependent decisions are made with greater accuracy and efficiency.

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