Estimating precipitation at the global scale is vital for Earth system science and disaster response, yet observational coverage and measurement precision remain constrained. Existing solutions provide options that trade off desirable traits such as skill, latency, geographical coverage, temporal coverage, and spatial...
Guy Shalev, S. Rasp, B. Babenko et al.· 0 citations
WeatherNext Cyclones (WN-C), an artificial intelligence-based operational weather model that produces state-of-the-art ensemble forecasts of the track, intensity and size of tropical cyclones worldwide, can predict the track, intensity and size of tropical cyclones with an accuracy gain over leading operational models...
Ferran Alet, Tom R. Andersson, Ilan Price et al.· Nature· 8 citations
Together, WeatherNext 3's capabilities move operational AI-based weather forecasting beyond emulating the traditionally distinct stages of data assimilation, forecasting and post-processing, which helps to further push the frontier of performance and granularity for global weather prediction.
S. Rasp, B. Babenko, Dominic Masters et al.· 3 citations
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