Abstract

Various methods of cluster analysis are used for identifying large-scale atmospheric circulation regimes (weather regimes (WRs)). In this paper we compare the four most commonly used clustering methods: k-means (KM), Ward’s hierarchical clustering (HW), Gaussian mixture model (GM), and self-organizing maps (SOMs) to analyze WRs in the Euro-Atlantic (EAT) region. The data used for identifying WRs are 500 hPa geopotential height fields (z500) from the ERA5 reanalysis for 1940–2022. Four classical wintertime WRs are identified by the KM method—two regimes associated with positive and negative phases of the North Atlantic Oscillation (NAO+ and NAO–), a regime associated with the Scandinavian blocking (SB), and a regime characterized by elevated pressure over the Northern Atlantic. For summer months, the KM method gets WRs that are similar in spatial structure to the classical winter ones. The SOM method yields results that are almost identical to the results of the KM method. Unlike KM and SOM methods, HW and GM do not fully catch the spatial structure of all of the four classical winter EAT WRs and their summer analogues. Compared to WRs of the KM and SOM methods, WRs obtained by HW and GM methods explain less z500 variance; they have different occurrences, persistence, and transition features. Summer and winter WRs obtained by HW and GM methods are less similar to each other compared to WRs provided by the KM method. Average spatial correlation coefficients between mean z500 fields of WRs obtained by KM and HW methods are 0.76 in winter and 0.83 in summer; 0.70 in winter and 0.72 in summer for KM and GM methods; and 0.41 in winter and 0.44 in summer for the regimes compared between HW and GM methods, respectively. There are statistically significant trends of the seasonal occurrence of WRs found by some of the studied clustering methods—a positive trend for the occurrence of the NAO+ regime and a negative trend for the occurrence of the NAO-regime.

中文翻译：

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识别欧洲-大西洋地区冬夏季天气状况的聚类分析方法比较

摘要

各种聚类分析方法用于识别大范围大气环流状况（天气状况（WR））。在本文中，我们比较了四种最常用的聚类方法：k-means (KM)、Ward 层次聚类 (HW)、高斯混合模型 (GM) 和自组织映射 (SOM)，以分析欧洲-大西洋地区的 WR （吃）区域。用于识别 WR 的数据是来自 1940-2022 年 ERA5 再分析的 500 hPa 位势高度场 (z500)。KM 方法识别了四种经典的冬季 WR：两种与北大西洋涛动的正相和负相相关的区域（NAO+ 和 NAO-）、与斯堪的纳维亚阻塞 (SB) 相关的区域以及以高于北大西洋涛动的高压为特征的区域。北大西洋。对于夏季月份，KM 方法得到的 WR 在空间结构上与经典冬季的 WR 相似。SOM 方法产生的结果与 KM 方法的结果几乎相同。与 KM 和 SOM 方法不同，HW 和 GM 并不能完全捕捉所有四种经典冬季 EAT WR 及其夏季类似物的空间结构。与KM和SOM方法的WR相比，HW和GM方法获得的WR解释的z500方差较小；它们具有不同的发生、持续和过渡特征。与 KM 方法提供的 WR 相比，HW 和 GM 方法获得的夏季和冬季 WR 彼此不太相似。KM和HW方法获得的WR平均z500场之间的平均空间相关系数冬季为0.76，夏季为0.83；KM 和 GM 方法冬季为 0.70，夏季为 0.72；与 HW 和 GM 方法相比，冬季和夏季分别为 0.41 和 0.41。通过一些研究的聚类方法发现，WR 的季节性发生具有统计上显着的趋势——NAO+ 体系的发生呈正趋势，NAO-体系的发生呈负趋势。