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: 1,948 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,318 580 50 1,948 40 29
  • HTML: 1,318
  • PDF: 580
  • XML: 50
  • Total: 1,948
  • BibTeX: 40
  • EndNote: 29
Views and downloads (calculated since 10 Apr 2024)
Cumulative views and downloads (calculated since 10 Apr 2024)

Viewed (geographical distribution)

Total article views: 1,948 (including HTML, PDF, and XML) Thereof 1,891 with geography defined and 57 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 24 Dec 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.