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基于自动编码器的压力场热波流模拟概率重建。

Autoencoder-based flow-analogue probabilistic reconstruction of heat waves from pressure fields.

机构信息

Department of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain.

Helmholtz-Zentrum Geesthacht, Hamburg, Germany.

出版信息

Ann N Y Acad Sci. 2024 Nov;1541(1):230-242. doi: 10.1111/nyas.15243. Epub 2024 Oct 30.

DOI:10.1111/nyas.15243
PMID:39476218
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11580787/
Abstract

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.

摘要

本文提出了一种基于模拟方法 (AM) 和深度自动编码器 (AE) 联合使用的气象场概率重建的混合方法。AE-AM 算法在预测字段中训练深度 AE，编码器将其过滤到降维的压缩空间中。然后，在这个潜在空间中应用 AM 来找到历史记录中的相似情况（模拟），从中可以重建目标字段。AE-AM 与经典 AM 进行了比较，在经典 AM 中，在预测的完全解析字段中显式搜索流模拟，这可能包含对重建无用的信息。我们评估了这两种方法在从 1950-2010 年期间记录的海平面气压场（预测器）中重建日最高温度（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。根据分析的热浪，技能得分的提高范围为 7%至 22%，证明了混合方法的潜在附加值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/067d/11580787/80ae7aedec50/NYAS-1541-230-g005.jpg
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