Articles | Volume 28, issue 3
Nonlin. Processes Geophys., 28, 347–370, 2021
Nonlin. Processes Geophys., 28, 347–370, 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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Cited articles

Arjovsky, M., Chintala, S., and Bottou, L.: Wasserstein gan, arXiv [preprint], arXiv:1701.07875, 26 January 2017. a, b, c, d, e, f
Besombes, C.: Producing realistic climate data with GANs, Zenodo [data set],, 2021 (data available at:, last access: January 2021). a
Beusch, L., Gudmundsson, L., and Seneviratne, S. I.: Emulating Earth system model temperatures with MESMER: from global mean temperature trajectories to grid-point-level realizations on land, Earth Syst. Dynam., 11, 139–159,, 2020. a
Boukabara, S.-A., Krasnopolsky, V., Stewart, J. Q., Maddy, E. S., Shahroudi, N., and Hoffman, R. N.: Leveraging modern artificial intelligence for remote sensing and NWP: Benefits and challenges, B. Am. Meteorol. Soc., 100, ES473–ES491, 2019. a
Chan, S. and Elsheikh, A. H.: Parametric generation of conditional geological realizations using generative neural networks, Computat. Geosci., 23, 925–952,, 2019. a
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.