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.

中文翻译：

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利用GAN生成逼真的气候数据

摘要。本文研究了Wasserstein生成对抗网络在根据一般循环模型（GCM）的气候进行训练时产生现实天气情况的潜力。为此，为发电机提出了卷积神经网络架构，并在合成气候数据库上进行了训练，该数据库使用简单的三维气候模型：PLASIM计算。生成器转换潜在空间由64维高斯分布定义，并在与PLASIM相同的输出网格上分解为空间定义的异常。对领先的经验正交函数中的统计数据进行的分析表明，生成器能够再现人工气候多元分布的许多方面。此外，生成状态再现了大气中存在的主要地转平衡。在紧凑，密集且可能是非线性的潜在空间中表示气候状态的能力为气候的分析和处理开辟了新的视角。这篇文章讨论了在给定状态附近的极端情况的探索，以及如何用这种方法将两种现实的天气情况联系起来。