@GoogleDeepMind: Predicting cyclones accurately can help save lives - and every hour of lead time counts. Published in @Nature, our AI m…
Summary
Google DeepMind's WeatherNext AI model, published in Nature, achieves state-of-the-art accuracy in cyclone track and intensity forecasting, providing an average of 24 extra hours of preparation time.
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Cached at: 08/06/26, 04:54 PM
Predicting cyclones accurately can help save lives - and every hour of lead time counts.
Published in @Nature, our AI model WeatherNext achieves state-of-the-art accuracy in forecasting a storm’s track and intensity, giving us a critical extra 24 hours to prepare on average.
WeatherNext delivers a decade’s worth of forecasting progress in a single leap.
On average, 3-day predictions now match the quality that prior models could only provide 2 days out.
The model learned from years of everyday global atmospheric data alongside a curated database of almost 5,000 historical cyclones.
It generates each 15-day probabilistic forecast scenario in under a minute on a TPU.
During Hurricane Melissa, WeatherNext gave forecasters early predictions of its Category 5 landfall 5 days in advance with 80% confidence.
This year, we’re providing 1,000 probabilistic predictions per storm to support forecasters, now accessible via WeatherLab → https://goo.gle/4yXzyf6
We’re open sourcing the code and model weights on @Github, making them freely available for anyone to build on.
This could be for academic purposes, operational forecasting, or developing more specialized, localized models. Explore the research →
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