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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Jul 2021
Research article |
| 30 Jul 2021
| 30 Jul 2021
Producing realistic climate data with generative adversarial networks
CERFACS, Toulouse, France
Institut National Polytechnique de Toulouse, Toulouse, France
Olivier Pannekoucke
CORRESPONDING AUTHOR
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Corentin Lapeyre
CORRESPONDING AUTHOR
CERFACS, Toulouse, France
CERFACS, Toulouse, France
Olivier Thual
CORRESPONDING AUTHOR
CERFACS, Toulouse, France
Institut de Mécanique des Fluides de Toulouse (IMFT), Université de Toulouse, CNRS, Toulouse, France
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- TemperatureGAN: generative modeling of regional atmospheric temperatures E. Balogun et al. 10.1017/eds.2024.21
- Generative deep learning for data generation in natural hazard analysis: motivations, advances, challenges, and opportunities Z. Ma et al. 10.1007/s10462-024-10764-9
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15 citations as recorded by crossref.
- Synthetic data generation with hybrid quantum-classical models for the financial sector O. Pires et al. 10.1140/epjb/s10051-024-00786-1
- Climate-informed flood risk mapping using a GAN-based approach (ExGAN) R. Belhajjam et al. 10.1016/j.jhydrol.2024.131487
- TemperatureGAN: generative modeling of regional atmospheric temperatures E. Balogun et al. 10.1017/eds.2024.21
- Generative deep learning for data generation in natural hazard analysis: motivations, advances, challenges, and opportunities Z. Ma et al. 10.1007/s10462-024-10764-9
- Extreme heat wave sampling and prediction with analog Markov chain and comparisons with deep learning G. Miloshevich et al. 10.1017/eds.2024.7
- Scalable Bayesian Transport Maps for High-Dimensional Non-Gaussian Spatial Fields M. Katzfuss & F. Schäfer 10.1080/01621459.2023.2197158
- Bayesian Nonparametric Generative Modeling of Large Multivariate Non-Gaussian Spatial Fields P. Wiemann & M. Katzfuss 10.1007/s13253-023-00580-z
- Current progress in subseasonal-to-decadal prediction based on machine learning Z. Shen et al. 10.1016/j.acags.2024.100201
- Weather-Adaptive Synthetic Data Generation for Enhanced Power Line Inspection Using StarGAN B. Kyem et al. 10.1109/ACCESS.2024.3520120
- A comprehensive review of applications and feedback impact of microclimate on building operation and energy L. Pasandi et al. 10.1016/j.buildenv.2024.111855
- Moving beyond post hoc explainable artificial intelligence: a perspective paper on lessons learned from dynamical climate modeling R. O'Loughlin et al. 10.5194/gmd-18-787-2025
- Perceptual loss function for generating high-resolution climate data Y. Wang & H. Karimi 10.3934/aci.2022009
- Technical note: Emulation of a large-eddy simulator for stratocumulus clouds in a general circulation model K. Nordling et al. 10.5194/acp-24-869-2024
- How to stop being surprised by unprecedented weather T. Kelder et al. 10.1038/s41467-025-57450-0
- A Survey of Recent Advances in Quantum Generative Adversarial Networks T. Ngo et al. 10.3390/electronics12040856
1 citations as recorded by crossref.
Latest update: 22 May 2025
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.
This paper investigates the potential of a type of deep generative neural network to produce...
