Producing realistic climate data with generative adversarial networks - INSU - Institut national des sciences de l'Univers Accéder directement au contenu
Article Dans Une Revue Nonlinear Processes in Geophysics Année : 2021

Producing realistic climate data with generative adversarial networks

Résumé

This paper investigates the potential of a Wasserstein generative adversarial network 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 three 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 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.

Fichier principal
Vignette du fichier
npg-28-347-2021.pdf (31.69 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

insu-03668378 , version 1 (15-05-2022)

Licence

Paternité

Identifiants

Citer

Camille Besombes, Olivier Pannekoucke, Corentin Lapeyre, Benjamin Sanderson, Olivier Thual. Producing realistic climate data with generative adversarial networks. Nonlinear Processes in Geophysics, 2021, 28, pp.347-370. ⟨10.5194/npg-28-347-2021⟩. ⟨insu-03668378⟩
53 Consultations
11 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More