Skilful precipitation nowcasting using deep generative models of radar

Abstract Precipitation nowcasting, the high-resolution forecasting of precipitation up to two hours ahead, supports the real-world socioeconomic needs of many sectors reliant on weather-dependent decision-making 1,2 . When verified quantitatively, these nowcasts are skillful without resorting to blurring.

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• 1 advanced operational nowcasting methods typically advect precipitation fields with radar-based wind estimates, and struggle to capture important non-linear events such as convective initiations 3,4 .
• 2 Recently introduced deep learning methods use radar to directly predict future rain rates, free of physical constraints 5,6 .
• 3 While they accurately predict low-intensity rainfall, their operational utility is limited because their lack of constraints produces blurry nowcasts at longer lead times, yielding poor performance on rarer medium-to-heavy rain events.

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

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