Analyzing Wintertime Extreme Winds over Türkiye and Their Relationships with Synoptic Patterns Using Cluster Analysis

Abstract

This study investigates the patterns of extreme winds and the correlation between synoptic patterns in Türkiye throughout the winter season, using the cluster analysis technique. We utilized the k-means algorithm to detect the surface patterns of extreme winds. Additionally, we deployed the Self-Organizing Map (SOM) technique to identify clusters of geopotential height at the 500 hPa level, average temperature at the 850 hPa level, and mean sea level pressure. We adopted the dataset from the New European Wind Atlas (NEWA) project for analyzing surface-level weather conditions and the ERA5 datasets for studying upper-level weather conditions. The k-means algorithm identifies six distinct clusters when applied to the ground-level data in Türkiye. These clusters are predominantly located around the Taurus Mountain ranges, which stretch in an east-west and northeastern direction along the Black Sea coast. The formation of these clusters is controlled by the characteristics of the land and its physical features. The higher-level clusters, consisting of nine SOM nodes, are unaffected by terrain and weather systems, which are characteristic of the macro-Mediterranean climate. These clusters are detected in the Eastern Mediterranean, Black Sea, and inner Aegean areas, emphasizing the impact of topography on surface patterns.