Preprints
https://doi.org/10.5194/npg-2021-6
https://doi.org/10.5194/npg-2021-6

  16 Feb 2021

16 Feb 2021

Review status: a revised version of this preprint was accepted for the journal NPG and is expected to appear here in due course.

Producing realistic climate data with GANs

Camille Besombes1,4, Olivier Pannekoucke2, Corentin Lapeyre1, Benjamin Sanderson1, and Olivier Thual1,3 Camille Besombes et al.
  • 1CERFACS, Toulouse, France
  • 2CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
  • 3Institut de Mécanique des Fluides de Toulouse (IMFT), Université de Toulouse, CNRS, Toulouse, France
  • 4Institut National Polytechnique de Toulouse

Abstract. This paper investigates the potential of a Wasserstein Generative Adversarial Networks to produce realistic weather situations when trained from the climate of a general circulation model (GCM). To do so, a convolutional neural network architecture is proposed for the generator and trained on a synthetic climate database, computed using a simple 3 dimensional climate model: PLASIM.

The generator transforms a latent space, defined by a 64 dimensional Gaussian distribution, into spatially defined anomalies on the same output grid as PLASIM. The analysis of the statistics in the leading empirical orthogonal functions shows that the generator is able to reproduce many aspects of the multivariate distribution of the synthetic climate. Moreover, generated states reproduce the leading geostrophic balance present in the atmosphere.

The ability to represent the climate state in a compact, dense and potentially nonlinear latent space opens new perspectives in the analysis and the handling of the climate. This contribution discusses the exploration of the extremes close to a given state and how to connect two realistic weather situations with this approach.

Camille Besombes et al.

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

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

Camille Besombes et al.

Model code and software

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

Camille Besombes et al.

Viewed

Total article views: 827 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
689 125 13 827 7 5
  • HTML: 689
  • PDF: 125
  • XML: 13
  • Total: 827
  • BibTeX: 7
  • EndNote: 5
Views and downloads (calculated since 16 Feb 2021)
Cumulative views and downloads (calculated since 16 Feb 2021)

Viewed (geographical distribution)

Total article views: 814 (including HTML, PDF, and XML) Thereof 814 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 23 Jul 2021
Download
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 aspect 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.