Articles | Volume 15, issue 23
https://doi.org/10.5194/gmd-15-8931-2022
https://doi.org/10.5194/gmd-15-8931-2022
Model experiment description paper
 | 
13 Dec 2022
Model experiment description paper |  | 13 Dec 2022

Temperature forecasting by deep learning methods

Bing Gong, Michael Langguth, Yan Ji, Amirpasha Mozaffari, Scarlet Stadtler, Karim Mache, and Martin G. Schultz

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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-2021-430', Anonymous Referee #1, 05 Apr 2022
  • CEC1: 'Comment on gmd-2021-430', Juan Antonio Añel, 21 Apr 2022
    • AC1: 'Reply on CEC1', BING GONG, 21 Apr 2022
  • CEC2: 'Comment on gmd-2021-430', Juan Antonio Añel, 21 Apr 2022
  • RC2: 'Comment on gmd-2021-430', Anonymous Referee #2, 05 May 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by BING GONG on behalf of the Authors (24 Jun 2022)  Author's response
ED: Publish subject to minor revisions (review by editor) (09 Jul 2022) by Sergey Gromov
AR by BING GONG on behalf of the Authors (27 Jul 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to minor revisions (review by editor) (04 Aug 2022) by Sergey Gromov
AR by BING GONG on behalf of the Authors (01 Sep 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (07 Sep 2022) by Sergey Gromov
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Short summary
Inspired by the success of deep learning in various domains, we test the applicability of video prediction methods by generative adversarial network (GAN)-based deep learning to predict the 2 m temperature over Europe. Our video prediction models have skill in predicting the diurnal cycle of 2 m temperature up to 12 h ahead. Complemented by probing the relevance of several model parameters, this study confirms the potential of deep learning in meteorological forecasting applications.