AI technology for precise extreme weather forecasts: A breakthrough at KIT

All Weather! With Artificial Intelligence for More Precise Heavy Rain Forecasting

2025-07-14 Source: KIT | Translated by AI 1 min Reading Time

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The more reliable assessment of dangerous weather events can save lives and protect property. Researchers at the Karlsruhe Institute of Technology (KIT) have something for this ...

Never be caught unprepared in (heavy) rain again! At KIT in Karlsruhe, they have succeeded in using generative artificial intelligence to turn coarse weather data (left) into more detailed forecasts (right).(Image: KIT / Ch. Chwala) — Never be caught unprepared in (heavy) rain again! At KIT in Karlsruhe, they have succeeded in using generative artificial intelligence to turn coarse weather data (left) into more detailed forecasts (right).
(Image: KIT / Ch. Chwala)

Extreme weather events such as heavy rainfall are increasing worldwide, according to Karlsruhe, Germany. Previous forecasts, however, had their shortcomings. But now, coarse global weather data can be transformed into highly precise precipitation maps using artificial intelligence (AI)—independently of location, quickly, and resource-efficiently, as reported by KIT. This creates a previously unique tool for analyzing and estimating extreme weather, which also works for regions with a lack of weather data, such as the global south. For this purpose, KIT researchers use historical data from weather models that describe global precipitation with a spatial resolution of around 15 miles and hourly intervals.

AI Extreme Weather Forecasting Resolves Down to 1.2 miles

Their generative AI model ("SpateGAN-ERA5") not only trains with these data but also learns from high-resolution weather radar measurements in Germany how precipitation patterns and extreme events at different scales—from coarse to fine—relate to each other. The new AI model thus does not simply create a sharpened version of the input data but generates multiple realizations of physically plausible, finely resolved precipitation maps. This allows details as small as two kilometers to become visible in 10-minute intervals. At the same time, the model provides information about the statistical uncertainty of the results, which is particularly relevant for mapping localized heavy rainfall events, as the researchers note. Validation with weather radar data in the USA and Australia also shows that the method can be applied to completely different climatic conditions.

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