Articles | Volume 14, issue 10
https://doi.org/10.5194/gmd-14-6355-2021
https://doi.org/10.5194/gmd-14-6355-2021
Development and technical paper
 | 
22 Oct 2021
Development and technical paper |  | 22 Oct 2021

Fast and accurate learned multiresolution dynamical downscaling for precipitation

Jiali Wang, Zhengchun Liu, Ian Foster, Won Chang, Rajkumar Kettimuthu, and V. Rao Kotamarthi

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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-2020-412', Anonymous Referee #1, 29 Mar 2021
  • CEC1: 'Comment on gmd-2020-412', Juan Antonio Añel, 03 Apr 2021
    • CC1: 'Reply on CEC1', Zhengchun Liu, 30 Apr 2021
  • RC2: 'Comment on gmd-2020-412', Anonymous Referee #2, 25 May 2021
  • AC1: 'Comment on gmd-2020-412', V. Rao Kotamarthi, 08 Jul 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by V. Rao Kotamarthi on behalf of the Authors (08 Jul 2021)  Author's response   Author's tracked changes   Manuscript 
ED: Reconsider after major revisions (21 Jul 2021) by Simone Marras
AR by V. Rao Kotamarthi on behalf of the Authors (21 Jul 2021)  Author's response   Manuscript 
EF by Svenja Lange (26 Jul 2021)  Author's tracked changes 
ED: Publish as is (28 Sep 2021) by Simone Marras
AR by V. Rao Kotamarthi on behalf of the Authors (29 Sep 2021)  Manuscript 
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Short summary
Downscaling, the process of generating a higher spatial or time dataset from a coarser observational or model dataset, is a widely used technique. Two common methodologies for performing downscaling are to use either dynamic (physics-based) or statistical (empirical). Here we develop a novel methodology, using a conditional generative adversarial network (CGAN), to perform the downscaling of a model's precipitation forecasts and describe the advantages of this method compared to the others.