Articles | Volume 31, issue 1
Research article
13 Feb 2024
Research article |  | 13 Feb 2024

A two-fold deep-learning strategy to correct and downscale winds over mountains

Louis Le Toumelin, Isabelle Gouttevin, Clovis Galiez, and Nora Helbig


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on npg-2023-10', Anonymous Referee #1, 05 Jul 2023
    • AC1: 'Reply on RC1', Louis Le Toumelin, 15 Oct 2023
  • RC2: 'Comment on npg-2023-10', Anonymous Referee #2, 18 Aug 2023
    • AC2: 'Reply on RC2', Louis Le Toumelin, 15 Oct 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Louis Le Toumelin on behalf of the Authors (02 Nov 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (14 Nov 2023) by Pierre Tandeo
AR by Louis Le Toumelin on behalf of the Authors (24 Nov 2023)  Manuscript 
Short summary
Forecasting wind fields over mountains is of high importance for several applications and particularly for understanding how wind erodes and disperses snow. Forecasters rely on operational wind forecasts over mountains, which are currently only available on kilometric scales. These forecasts can also be affected by errors of diverse origins. Here we introduce a new strategy based on artificial intelligence to correct large-scale wind forecasts in mountains and increase their spatial resolution.