Learning skillful medium-range global weather forecasting

Global medium-range weather forecasting is critical to decision-making across many social and economic domains. 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.

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• 1 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.
• 2 Here, we introduce GraphCast, a machine learning-based method trained directly from reanalysis data.
• 3 It predicts hundreds of weather variables for the next 10 days at 0.25° resolution globally in under 1 minute.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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