Articles | Volume 15, issue 15
https://doi.org/10.5194/gmd-15-5967-2022
https://doi.org/10.5194/gmd-15-5967-2022
Model description paper
 | 
01 Aug 2022
Model description paper |  | 01 Aug 2022

Rad-cGAN v1.0: Radar-based precipitation nowcasting model with conditional generative adversarial networks for multiple dam domains

Suyeon Choi and Yeonjoo Kim

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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-2021-405', Anonymous Referee #1, 18 Jan 2022
  • RC2: 'Comment on gmd-2021-405', Anonymous Referee #2, 24 Jan 2022
  • RC3: 'Comment on gmd-2021-405', Anonymous Referee #3, 26 Jan 2022
  • AC1: 'Responses to RC1, RC2 and RC3', Yeonjoo Kim, 13 Apr 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Yeonjoo Kim on behalf of the Authors (13 Apr 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (16 Apr 2022) by Charles Onyutha
RR by Anonymous Referee #1 (22 Apr 2022)
RR by Anonymous Referee #2 (03 May 2022)
ED: Reconsider after major revisions (03 May 2022) by Charles Onyutha
AR by Yeonjoo Kim on behalf of the Authors (14 Jun 2022)  Author's response    Author's tracked changes    Manuscript
ED: Reconsider after major revisions (15 Jun 2022) by Charles Onyutha
AR by Yeonjoo Kim on behalf of the Authors (17 Jun 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (21 Jun 2022) by Charles Onyutha
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Short summary
Here we present the cGAN-based precipitation nowcasting model, named Rad-cGAN, trained to predict a radar reflectivity map with a lead time of 10 min. Rad-cGAN showed superior performance at a lead time of up to 90 min compared with the reference models. Furthermore, we demonstrate the successful implementation of the transfer learning strategies using pre-trained Rad-cGAN to develop the models for different dam domains.