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

Olivetti, Leonardo, Messori, Gabriele·2024·Geoscientific Model Development
DOI 10.5194/gmd-17-2347-2024

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Abstract

In recent years, deep learning models have rapidly emerged as a stand-alone alternative to physics-based numerical models for medium-range weather forecasting. Several independent research groups claim to have developed deep learning weather forecasts that outperform those from state-of-the-art physics-based models, and operational implementation of data-driven forecasts appears to be drawing near. However, questions remain about the capabilities of deep learning models with respect to providing robust forecasts of extreme weather. This paper provides an overview of recent developments in the field of deep learning weather forecasts and scrutinises the challenges that extreme weather events pose to leading deep learning models. Lastly, it argues for the need to tailor data-driven models to forecast extreme events and proposes a foundational workflow to develop such models.

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Cited by

Sorted by citations

Authors
Olivetti, L., & Messori, G.
Affiliations
Uppsala University (Uppsala, Sweden)
ORCID
Venue
Geoscientific Model Development
Publisher
Copernicus GmbH
Published
2024-03-21
Volume
17
Issue
6
Pages
2347-2358
ISSN
1991-9603
DOI
10.5194/gmd-17-2347-2024
Cited by
51
References
5
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
H2020 European Research Council (948309)

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