Articles | Volume 18, issue 8
https://doi.org/10.5194/gmd-18-2427-2025
https://doi.org/10.5194/gmd-18-2427-2025
Model description paper
 | 
24 Apr 2025
Model description paper |  | 24 Apr 2025

TemDeep: a self-supervised framework for temporal downscaling of atmospheric fields at arbitrary time resolutions

Liwen Wang, Qian Li, Qi Lv, Xuan Peng, and Wei You

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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-2023-1775', Anonymous Referee #1, 16 Oct 2023
  • CEC1: 'Executive Editor comment on egusphere-2023-1775', Astrid Kerkweg, 23 Oct 2023
    • AC1: 'Reply on CEC1', Qian Li, 12 Nov 2024
  • RC2: 'Comment on egusphere-2023-1775', Zhenxin Liu, 31 Oct 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Qian Li on behalf of the Authors (14 Nov 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (19 Dec 2024) by Rohitash Chandra
RR by Anonymous Referee #2 (23 Dec 2024)
ED: Publish subject to minor revisions (review by editor) (30 Jan 2025) by Rohitash Chandra
AR by Qian Li on behalf of the Authors (10 Feb 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to technical corrections (20 Feb 2025) by Rohitash Chandra
AR by Qian Li on behalf of the Authors (21 Feb 2025)  Manuscript 
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Short summary
Our research presents a novel deep learning approach called "TemDeep" for downscaling atmospheric variables at arbitrary time resolutions based on temporal coherence. Results show that our method can accurately recover evolution details superior to other methods, reaching 53.7 % in the restoration rate. Our findings are important for advancing weather forecasting models and enabling more precise and reliable predictions to support disaster preparedness, agriculture, and sustainable development.
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