Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 21
Articles | Volume 17, issue 21
https://doi.org/10.5194/gmd-17-7915-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-7915-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 21
Model evaluation paper
 | 
07 Nov 2024
Model evaluation paper |  | 07 Nov 2024

Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast

Leonardo Olivetti and Gabriele Messori

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Latest update: 08 Oct 2025
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Short summary
Data-driven models are becoming a viable alternative to physics-based models for weather forecasting up to 15 d into the future. However, it is unclear whether they are as reliable as physics-based models when forecasting weather extremes. We evaluate their performance in forecasting near-surface cold, hot, and windy extremes globally. We find that data-driven models can compete with physics-based models and that the choice of the best model mainly depends on the region and type of extreme.
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