Articles | Volume 17, issue 21
https://doi.org/10.5194/gmd-17-7915-2024
https://doi.org/10.5194/gmd-17-7915-2024
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

Viewed

Total article views: 1,763 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,163 552 48 1,763 34 27
  • HTML: 1,163
  • PDF: 552
  • XML: 48
  • Total: 1,763
  • BibTeX: 34
  • EndNote: 27
Views and downloads (calculated since 10 Apr 2024)
Cumulative views and downloads (calculated since 10 Apr 2024)

Viewed (geographical distribution)

Total article views: 1,763 (including HTML, PDF, and XML) Thereof 1,713 with geography defined and 50 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 20 Nov 2024
Download
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.