Articles | Volume 28, issue 3
Nonlin. Processes Geophys., 28, 347–370, 2021
https://doi.org/10.5194/npg-28-347-2021
Nonlin. Processes Geophys., 28, 347–370, 2021
https://doi.org/10.5194/npg-28-347-2021

Research article 30 Jul 2021

Research article | 30 Jul 2021

Producing realistic climate data with generative adversarial networks

Camille Besombes 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 npg-2021-6', Anonymous Referee #1, 17 Mar 2021
    • AC1: 'Reply on RC1', Camille Besombes, 05 Jun 2021
  • RC2: 'Comment on npg-2021-6', Anonymous Referee #2, 25 Apr 2021
    • AC2: 'Reply on RC2', Camille Besombes, 05 Jun 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Camille Besombes on behalf of the Authors (08 Jun 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (17 Jun 2021) by Takemasa Miyoshi
AR by Camille Besombes on behalf of the Authors (17 Jun 2021)  Author's response    Manuscript
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Short summary
This paper investigates the potential of a type of deep generative neural network to produce realistic weather situations when trained from the climate of a general circulation model. The generator represents the climate in a compact latent space. It is able to reproduce many aspects of the targeted multivariate distribution. Some properties of our method open new perspectives such as the exploration of the extremes close to a given state or how to connect two realistic weather states.