GraphCast: Learning skillful medium-range global weather forecasting

Understand

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 cannot directly use historical weather data 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 10 days at 0.25 degree resolution globally, in under one minute.