Articles | Volume 14, issue 10
Geosci. Model Dev., 14, 6355–6372, 2021
https://doi.org/10.5194/gmd-14-6355-2021
Geosci. Model Dev., 14, 6355–6372, 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 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-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
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
ED: Publish as is (28 Sep 2021) by Simone Marras
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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.