A weather model that keeps up with the supercomputers
orig. “GraphCast: Learning skillful medium-range global weather forecasting” · Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson, Peter Battaglia
This model predicts ten days of global weather in under a minute, and it is often more accurate than the traditional forecast.
Standard forecasts run huge physics simulations on supercomputers. GraphCast instead learned from decades of past weather and predicts the next steps directly. It produces a ten-day global forecast in well under a minute, and in tests it matched or beat the leading physics-based system on most measures, including the tracks of major storms.
Faster, cheaper forecasts help with everything from daily planning to early warnings for extreme weather. It also shows learned models can rival decades of carefully built physics code in a serious scientific field. That makes it a strong example of AI pointed at a real-world problem.
Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson, Peter Battaglia, Google DeepMind
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