Articles | Volume 15, issue 19
Geosci. Model Dev., 15, 7353–7370, 2022
https://doi.org/10.5194/gmd-15-7353-2022
Geosci. Model Dev., 15, 7353–7370, 2022
https://doi.org/10.5194/gmd-15-7353-2022
Development and technical paper
05 Oct 2022
Development and technical paper | 05 Oct 2022

Repeatable high-resolution statistical downscaling through deep learning

Dánnell Quesada-Chacón et al.

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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 gmd-2022-14', Anonymous Referee #1, 27 May 2022
    • AC1: 'Reply on RC1', Dánnell Quesada-Chacón, 02 Aug 2022
  • RC2: 'Comment on gmd-2022-14', Anonymous Referee #2, 24 Jun 2022
    • AC2: 'Reply on RC2', Dánnell Quesada-Chacón, 02 Aug 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Dánnell Quesada-Chacón on behalf of the Authors (02 Aug 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to minor revisions (review by editor) (16 Aug 2022) by Travis O'Brien
AR by Dánnell Quesada-Chacón on behalf of the Authors (17 Aug 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (20 Aug 2022) by Travis O'Brien
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Short summary
We improved the performance of past perfect prognosis statistical downscaling methods while achieving full model repeatability with GPU-calculated deep learning models using the TensorFlow, climate4R, and VALUE frameworks. We employed the ERA5 reanalysis as predictors and ReKIS (eastern Ore Mountains, Germany, 1 km resolution) as precipitation predictand, while incorporating modern deep learning architectures. The achieved repeatability is key to accomplish further milestones with deep learning.