Learning skillful medium-range global weather forecasting

• Published in: Science (American Association for the Advancement of Science) Vol. 382; no. 6677; pp. 1416 - 1421
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: United States The American Association for the Advancement of Science 22.12.2023
• Subjects: Atmospheric conditions; Atmospheric models; Cyclones; Global weather; Machine learning; Mathematical models; Numerical models; Tropical cyclones; Weather; Weather forecasting; Weather patterns
• DOI: 10.1126/science.adi2336
• ISSN: 0036-8075; 1095-9203

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° 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. The numerical models used to predict weather are large, complex, and computationally demanding and do not learn from past weather patterns. Lam et al . introduced a machine learning–based method that has been trained directly from reanalysis data of past atmospheric conditions. In this way, the authors were able to quickly predict hundreds of weather variables globally up to 10 days in advance and at high resolution. Their predictions were more accurate than those of traditional weather models in 90% of tested cases and displayed better severe event prediction for tropical cyclones, atmospheric rivers, and extreme temperatures. —H. Jesse Smith Machine learning leads to better, faster, and cheaper weather forecasting.