Learning skillful medium-range global weather forecasting

• 成果类型: Article
• 署名作者: Lam, Remi; Sanchez-Gonzalez, Alvaro; Willson, Matthew; Wirnsberger, Peter; Fortunato, Meire; Alet, Ferran; Ravuri, Suman; Ewalds, Timo; Eaton-Rosen, Zach; Hu, Weihua; Merose, Alexander; Hoyer, Stephan; Holland, George; Vinyals, Oriol; Stott, Jacklynn; Pritzel, Alexander; Mohamed, Shakir; Battaglia, Peter
• 署名单位: Alphabet Inc.; Google Incorporated; DeepMind; Alphabet Inc.; Google Incorporated
• 刊物名称: SCIENCE
• ISSN/ISSBN: 0036-12484
• DOI: 10.1126/science.adi2336
• 发表日期: 2023-12-22
• 页码: 1416-1421
• 关键词: precipitation; seeps

摘要

Global medium-range weather forecasting is critical to decision-making across many social and economic domains. Traditional numerical weather prediction uses increased compute resources to improve forecast accuracy but does not directly use historical weather data to improve the underlying model. Here, we introduce GraphCast, a machine learning-based method trained directly from reanalysis data. It predicts hundreds of weather variables for the next 10 days at 0.25(degrees) resolution globally in under 1 minute. GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets, and its forecasts support better severe event prediction, including tropical cyclone tracking, atmospheric rivers, and extreme temperatures. GraphCast is a key advance in accurate and efficient weather forecasting and helps realize the promise of machine learning for modeling complex dynamical systems.