Artificial Intelligence (AI)

Global medium-range weather forecasting is critical to decision-making across many social and economic domains. Traditional numerical weather prediction uses increased computing resources to improve forecast accuracy, but historical weather data cannot be directly used to improve the underlying model. We introduce a machine learning-based method called “GraphCast”, which can be trained directly from reanalysis data. It predicts hundreds of weather variables over ten days at 0.25-degree resolution globally in under one minute. We show that 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 cyclones, atmospheric rivers, and extreme temperatures. GraphCast is a key advancement in accurate and efficient weather forecasting, and it helps realize the promise of machine learning for modeling complex dynamic systems.

GraphCast: Learning skillful medium-range global weather forecasting. (2023, Aug. 4). Lam, Sanchez-Gonzalez, Willson, Wirnsberger, Fortunato, Alet, Ravuri, Ewalds, Eaton-Rosen, Hu, Merose, Hoyer, Holland, Vinyals, Stott, Pritzel, Mohamed, Battaglia. GraphCast: Learning skillful medium-range global weather forecasting