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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Latest update: 16 Jan 2022
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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.