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.

中文翻译：

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使用生成对抗网络生成真实的气候数据

本文研究了 Wasserstein 生成对抗网络在根据一般环流模型 (GCM) 的气候进行训练时产生真实天气情况的潜力。为此，为生成器提出了卷积神经网络架构，并在合成气候数据库上进行训练，使用简单的三维气候模型计算：PLASIM。生成器转换由 64 维高斯分布定义的“潜在空间” ，进入与 PLASIM 相同的输出网格上的空间定义异常。主要经验正交函数中的统计分析表明，生成器能够重现合成气候多元分布的许多方面。此外，生成的状态再现了大气中存在的主要地转平衡。在紧凑、密集和潜在的非线性潜在空间中表示气候状态的能力为气候分析和处理开辟了新的视角。这篇文章讨论了对接近给定状态的极端情况的探索，以及如何用这种方法将两种现实的天气情况联系起来。