Producing realistic climate data with generative adversarial networks

Besombes, Camille; Pannekoucke, Olivier; Lapeyre, Corentin; Sanderson, Benjamin; Thual, Olivier

doi:https://doi.org/10.5194/npg-28-347-2021

Articles | Volume 28, issue 3

https://doi.org/10.5194/npg-28-347-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/npg-28-347-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 28, issue 3

Research article

|

30 Jul 2021

Research article |

| 30 Jul 2021

Producing realistic climate data with generative adversarial networks

Camille Besombes, Olivier Pannekoucke, Corentin Lapeyre, Benjamin Sanderson, and Olivier Thual

Data sets

Producing-realistic-climate-data-with-GANs Camille Besombes https://doi.org/10.5281/zenodo.4442450

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.