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

Viewed

Total article views: 2,118 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,555 518 45 2,118 47 42
  • HTML: 1,555
  • PDF: 518
  • XML: 45
  • Total: 2,118
  • BibTeX: 47
  • EndNote: 42
Views and downloads (calculated since 24 Nov 2023)
Cumulative views and downloads (calculated since 24 Nov 2023)

Viewed (geographical distribution)

Total article views: 2,118 (including HTML, PDF, and XML) Thereof 2,097 with geography defined and 21 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
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.
Read more
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.
Read more