Articles | Volume 19, issue 14
https://doi.org/10.5194/gmd-19-6497-2026
https://doi.org/10.5194/gmd-19-6497-2026
Model description paper
 | 
17 Jul 2026
Model description paper |  | 17 Jul 2026

SNOWstorm (v1.0) – a deep-learning based model for near-surface winds and drifting snow in mountain environments

Manuel Saigger, Brigitta Goger, and Thomas Mölg

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on egusphere-2025-5608 - No compliance with the policy of the journal', Juan Antonio Añel, 11 Feb 2026
    • AC1: 'Reply on CEC1', Manuel Saigger, 17 Feb 2026
      • CEC2: 'Reply on AC1', Juan Antonio Añel, 17 Feb 2026
  • RC1: 'Comment on egusphere-2025-5608', Anonymous Referee #1, 15 Feb 2026
    • AC2: 'Reply on RC1', Manuel Saigger, 20 Mar 2026
  • RC2: 'Comment on egusphere-2025-5608', Anonymous Referee #2, 17 Feb 2026
    • AC3: 'Reply on RC2', Manuel Saigger, 20 Mar 2026

Peer review completion

AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload
AR by Manuel Saigger on behalf of the Authors (22 Apr 2026)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (24 Apr 2026) by Nicola Bodini
RR by Anonymous Referee #1 (12 May 2026)
RR by Anonymous Referee #2 (02 Jun 2026)
ED: Reconsider after major revisions (08 Jun 2026) by Nicola Bodini
AR by Manuel Saigger on behalf of the Authors (18 Jun 2026)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (25 Jun 2026) by Nicola Bodini
RR by Anonymous Referee #1 (07 Jul 2026)
ED: Publish as is (08 Jul 2026) by Nicola Bodini
AR by Manuel Saigger on behalf of the Authors (09 Jul 2026)  Author's response   Manuscript 

Post-review adjustments

AA – Author's adjustment | EA – Editor approval
AA by Manuel Saigger on behalf of the Authors (17 Jul 2026)   Author's adjustment   Manuscript
EA: Adjustments approved (17 Jul 2026) by Nicola Bodini
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Short summary
We present a new model to predict near-surface winds and wind-driven transport of snow in mountain regions at high resolutions. With its deep-learning based design, it is several orders of magnitude less computationally expensive compared to traditional numerical methods, while being applicable over a wide range of topographic settings and atmospheric conditions. A first application case study on a glacier in the European Alps showed good agreement with numerical simulations and observations.
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