Accurate medium-range global weather forecasting with 3D neural networks

Abstract Weather forecasting is important for science and society. Our method also works well with extreme weather forecasts and ensemble forecasts.

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• 1 At present, the most accurate forecast system is the numerical weather prediction (NWP) method, which represents atmospheric states as discretized grids and numerically solves partial differential equations that describe the transition between those states 1 .
• 2 However, this procedure is computationally expensive.
• 3 Recently, artificial-intelligence-based methods 2 have shown potential in accelerating weather forecasting by orders of magnitude, but the forecast accuracy is still significantly lower than that of NWP methods.

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

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