Preprints
https://doi.org/10.5194/egusphere-2023-1775
https://doi.org/10.5194/egusphere-2023-1775
18 Sep 2023
 | 18 Sep 2023

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

Abstract. Numerical forecast products with high temporal resolution are crucial tools in atmospheric studies, allowing for accurate identification of rapid transitions and subtle changes that may be missed by lower-resolution data. However, the acquisition of high-resolution data is limited due to excessive computational demands and substantial storage needs in numerical models. Current deep learning methods for statistical downscaling still require massive ground truth with high temporal resolution for model training. In this paper, we present a self-supervised framework for downscaling atmospheric variables at arbitrary time resolutions by imposing a temporal coherence constraint. Firstly, we construct an encoder-decoder structured temporal downscaling network, and then pretrain this downscaling network on a subset of data that exhibits rapid transitions and is filtered out based on a composite index. Subsequently, this pretrained network is utilized to downscale the fields from adjacent time periods and generate the field at the middle time point. By leveraging the temporal coherence inherent in meteorological variables, the network is further trained based on the difference between the generated field and the actual middle field. To track the evolving trends in meteorological system movements, a flow estimation module is designed to assist with generating interpolated fields. Results show that our method can accurately recover evolution details superior to other methods, reaching 53.7 % in the restoration rate on the test set. In addition, to avoid generating abnormal values and guide the model out of local optima, two regularization terms are integrated into the loss function to enforce spatial and temporal continuity, which further improves the performance by 7.6 %.

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Journal article(s) based on this preprint

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
Geosci. Model Dev., 18, 2427–2442, https://doi.org/10.5194/gmd-18-2427-2025,https://doi.org/10.5194/gmd-18-2427-2025, 2025
Short summary
Liwen Wang, Qian Li, Qi Lv, Xuan Peng, and Wei You

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

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 

Journal article(s) based on this preprint

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
Geosci. Model Dev., 18, 2427–2442, https://doi.org/10.5194/gmd-18-2427-2025,https://doi.org/10.5194/gmd-18-2427-2025, 2025
Short summary
Liwen Wang, Qian Li, Qi Lv, Xuan Peng, and Wei You
Liwen Wang, Qian Li, Qi Lv, Xuan Peng, and Wei You

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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, enabling more precise and reliable predictions to support disaster preparedness, agriculture, and sustainable development.
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