End-to-end data-driven weather prediction

Anna Allen (), Stratis Markou (), Will Tebbutt, James Requeima, Wessel P. Bruinsma, Tom R. Andersson, Michael Herzog, Nicholas D. Lane, Matthew Chantry, J. Scott Hosking and Richard E. Turner ()
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Anna Allen: University of Cambridge
Stratis Markou: University of Cambridge
Will Tebbutt: University of Cambridge
James Requeima: University of Toronto
Wessel P. Bruinsma: Microsoft Research AI for Science
Tom R. Andersson: British Antarctic Survey
Michael Herzog: University of Cambridge
Nicholas D. Lane: University of Cambridge
Matthew Chantry: European Centre for Medium-Range Weather Forecasts
J. Scott Hosking: British Antarctic Survey
Richard E. Turner: University of Cambridge

Nature, 2025, vol. 641, issue 8065, 1172-1179

Abstract: Abstract Weather prediction is critical for a range of human activities, including transportation, agriculture and industry, as well as for the safety of the general public. Machine learning transforms numerical weather prediction (NWP) by replacing the numerical solver with neural networks, improving the speed and accuracy of the forecasting component of the prediction pipeline1–6. However, current models rely on numerical systems at initialization and to produce local forecasts, thereby limiting their achievable gains. Here we show that a single machine learning model can replace the entire NWP pipeline. Aardvark Weather, an end-to-end data-driven weather prediction system, ingests observations and produces global gridded forecasts and local station forecasts. The global forecasts outperform an operational NWP baseline for several variables and lead times. The local station forecasts are skilful for up to ten days of lead time, competing with a post-processed global NWP baseline and a state-of-the-art end-to-end forecasting system with input from human forecasters. End-to-end tuning further improves the accuracy of local forecasts. Our results show that skilful forecasting is possible without relying on NWP at deployment time, which will enable the realization of the full speed and accuracy benefits of data-driven models. We believe that Aardvark Weather will be the starting point for a new generation of end-to-end models that will reduce computational costs by orders of magnitude and enable the rapid, affordable creation of customized models for a range of end users.

Date: 2025
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DOI: 10.1038/s41586-025-08897-0

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