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

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.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Advances and prospects of deep learning for medium-range extreme weather forecasting ; volume:17 ; number:6 ; year:2024 ; pages:2347-2358 ; extent:12 ; Geoscientific model development ; 17, Heft 6 (2024), 2347-2358 (gesamt 12)

Contributors, Places and Time

• Creator: Olivetti, Leonardo ; Messori, Gabriele

Further information

• DOI: 10.5194/gmd-17-2347-2024
• URN: urn:nbn:de:101:1-2024032803355478368692
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 14.08.2025, 10:47 AM CEST