DeepMind’s Generative AI Can Now Forecast Dangerous Rainfall in Real Time

Authors:

1. Suman Ravuri
2. Karel Lenc
3. Matthew Willson
4. Dmitry Kangin
5. Remi Lam
6. Piotr Mirowski
7. Megan Fitzsimons
8. Maria Athanassiadou
9. Sheleem Kashem
10. Sam Madge
11. Rachel Prudden
12. Amol Mandhane
13. Aidan Clark
14. Andrew Brock
15. Karen Simonyan
16. Raia Hadsell
17. Niall Robinson
18. Ellen Clancy
19. Alberto Arribas
20. Shakir Mohamed

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-making1,2. State-of-the-art operational nowcasting methods typically advect precipitation fields with radar-based wind estimates, and struggle to capture important non-linear events such as convective initiations3,4. Recently introduced deep learning methods use radar to directly predict future rain rates, free of physical constraints5,6. 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. Here...

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