This paper presents a novel hybrid approach for the probabilistic reconstruction of meteorological fields based on the combined use of the analogue method (AM) and deep autoencoders (AEs). The AE–AM algorithm trains a deep AE in the predictor fields, which the encoder filters towards a compressed space of reduced dimensionality. The AM is then applied in this latent space to find similar situations (analogues) in the historical record, from which the target field can be reconstructed. The AE–AM is compared to the classical AM, in which flow analogues are explicitly searched in the fully resolved field of the predictor, which may contain useless information for the reconstruction. We evaluate the performance of these two approaches in reconstructing the daily maximum temperature (target) from sea‐level pressure fields (predictor) recorded during eight major European heat waves of the 1950–2010 period. We show that the proposed AE–AM approach outperforms the standard AM algorithm in reconstructing the magnitude and spatial pattern of the considered heat wave events. The improvement ranges from 7% to 22% in skill score, depending on the heat wave analyzed, demonstrating the potential added value of the hybrid method.

中文翻译：

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基于自动编码器的流动模拟压力场热波的概率重建


本文提出了一种基于模拟法 （AM） 和深度自动编码器 （AEs） 组合使用的新型混合方法，用于气象场的概率重建。AE-AM 算法在预测器字段中训练深度 AE，编码器将其过滤到降维的压缩空间。然后将 AM 应用于这个潜在空间，以在历史记录中找到类似的情况（类似物），从中可以重建目标字段。AE-AM 与传统 AM 进行了比较，在经典 AM 中，在预测器的完全解析字段中显式搜索流模拟，其中可能包含对重建无用的信息。我们评估了这两种方法在 1950-2010 年期间记录的海平面压力场（预测器）重建日最高温度（目标）的性能。我们表明，所提出的 AE-AM 方法在重建所考虑的热浪事件的幅度和空间模式方面优于标准 AM 算法。技能得分的提高范围为 7% 到 22%，具体取决于分析的热浪，这表明混合方法的潜在附加值。