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

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-1042', Anonymous Referee #1, 22 Apr 2024
    • AC1: 'Reply on RC1', Leonardo Olivetti, 19 Jul 2024
  • RC2: 'Comment on egusphere-2024-1042', Anonymous Referee #2, 24 Jun 2024
    • AC2: 'Reply on RC2', Leonardo Olivetti, 19 Jul 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Leonardo Olivetti on behalf of the Authors (16 Aug 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (21 Aug 2024) by Yuefei Zeng
RR by Anonymous Referee #1 (06 Sep 2024)
ED: Publish as is (07 Sep 2024) by Yuefei Zeng
AR by Leonardo Olivetti on behalf of the Authors (11 Sep 2024)  Manuscript 
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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.