识别欧洲-大西洋地区冬夏两季天气状况的聚类分析方法比较

Pub Date : 2023-12-25 DOI:10.1134/s0001433823060026
B. A. Babanov, V. A. Semenov, I. I. Mokhov
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引用次数: 0

摘要

摘要 各种聚类分析方法被用于识别大尺度大气环流机制(天气机制(WR))。本文比较了四种最常用的聚类方法:K-均值聚类(KM)、沃德分层聚类(HW)、高斯混合模型(GM)和自组织地图(SOM),以分析欧洲-大西洋(EAT)地区的 WRs。用于识别 WR 的数据是 1940-2022 年 ERA5 再分析的 500 hPa 位势高度场(z500)。KM 方法识别出了四种典型的冬季 WR--两种与北大西洋涛动的正负相位(NAO+ 和 NAO-)相关的 WR,一种与斯堪的纳维亚阻塞(SB)相关的 WR,以及一种以北大西洋气压升高为特征的 WR。对于夏季月份,KM 方法得到的 WR 在空间结构上与经典的冬季 WR 相似。SOM 方法得到的结果与 KM 方法几乎相同。与 KM 和 SOM 方法不同,HW 和 GM 未能完全捕捉到所有四种经典冬季 EAT WR 及其夏季类似 WR 的空间结构。与 KM 和 SOM 方法得到的 WR 相比,HW 和 GM 方法得到的 WR 对 z500 方差的解释较少;它们具有不同的出现、持续和过渡特征。与 KM 方法得到的 WR 相比,HW 和 GM 方法得到的夏季和冬季 WR 的相似度较低。KM 和 HW 方法得到的 WR 平均 z500 场的平均空间相关系数分别为:冬季 0.76,夏季 0.83;KM 和 GM 方法得到的 WR 平均 z500 场的平均空间相关系数分别为:冬季 0.70,夏季 0.72;HW 和 GM 方法得到的 WR 平均 z500 场的平均空间相关系数分别为:冬季 0.41,夏季 0.44。所研究的一些聚类方法发现,WRs 的季节性发生趋势在统计学上具有重要意义--NAO+ 机制的发生趋势为正,NAO-机制的发生趋势为负。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Comparison of Cluster Analysis Methods for Identifying Weather Regimes in the Euro-Atlantic Region for Winter and Summer Seasons

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Comparison of Cluster Analysis Methods for Identifying Weather Regimes in the Euro-Atlantic Region for Winter and Summer Seasons

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.

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