Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 6
Articles | Volume 17, issue 6
https://doi.org/10.5194/gmd-17-2347-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-2347-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 6
Review and perspective paper
 | Highlight paper
 | 
21 Mar 2024
Review and perspective paper | Highlight paper |  | 21 Mar 2024

Advances and prospects of deep learning for medium-range extreme weather forecasting

Leonardo Olivetti and Gabriele Messori

Data sets

ERA5 hourly data on single levels from 1940 to present H. Hersbach et al. https://doi.org/10.24381/cds.adbb2d47

ERA5 hourly data on pressure levels from 1940 to present H. Hersbach et al. https://doi.org/10.24381/cds.bd0915c6

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Executive editor
This article provides a concise and well-written review of the current state of numerical weather prediction using machine learning models. Given how quickly this field is evolving, it's difficult for the traditional peer review process to capture all developments in this space, but this manuscript provides an excellent snapshot of the current state of the art.
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Short summary
In the last decades, weather forecasting up to 15 d into the future has been dominated by physics-based numerical models. Recently, deep learning models have challenged this paradigm. However, the latter models may struggle when forecasting weather extremes. In this article, we argue for deep learning models specifically designed to handle extreme events, and we propose a foundational framework to develop such models.
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