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Abstract <p>This study establishes the first climatological framework for hail events in the Democratic People’s Republic of Korea (DPR Korea), analyzing their spatiotemporal distribution (2010–2018) and governing synoptic drivers using surface observations and NCEP/NCAR reanalysis, and multivariate statistical methods. Principal Component Analysis and k-means clustering. Hail frequency peaks in April–June (75% of annual events), with regional maxima in the northwest linked to four distinct synoptic clusters. Cluster 1 (60.6% of hail days, May–July) features a weak 500&#xa0;hPa trough over northern regions, a ridge over eastern China, and a sea-level pressure contrast between an anticyclone over the West Sea of Korea and a low over Northeast China, driving warm advection and steep lapse rates (26&#xa0;K). Clusters 3 (16.6%) and 4 (15.1%) dominate April–May, characterized by vertically stacked lows and shortwave troughs, respectively, while Cluster 2 (7.7%, October) involves deep upper troughs with no major hail activity. Discriminant analysis validated clusters using 500&#xa0;hPa geopotential height, 850&#xa0;hPa temperature, and SLP loadings, achieving 93.4% classification accuracy (74.2% cross-validated). The methodology provides a transferable framework for hail prediction in regions with analogous topography and synoptic environments, emphasizing cold upper troughs, low-level moisture convergence, and moderate instability (CAPE 1000–2000&#xa0;J&#xa0;kg⁻<sup>1</sup>). This regional analysis offers actionable insights for hail forecasting and risk mitigation in the DPR Korea. The methodology and findings are transferable to regions with similar topographic and synoptic environments.</p>

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Large-scale atmospheric circulation patterns of hail events across the Democratic People's Republic of Korea

  • 期刊论文

  • 发表日期：

    2025年5月30日

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  • Kum-Ryong Jo,
  • Su-Song Kim,
  • Chung-Hyok Pak,
  • Yong-Sik Ham,
  • Won-Uk Kang

摘要

Abstract

This study establishes the first climatological framework for hail events in the Democratic People’s Republic of Korea (DPR Korea), analyzing their spatiotemporal distribution (2010–2018) and governing synoptic drivers using surface observations and NCEP/NCAR reanalysis, and multivariate statistical methods. Principal Component Analysis and k-means clustering. Hail frequency peaks in April–June (75% of annual events), with regional maxima in the northwest linked to four distinct synoptic clusters. Cluster 1 (60.6% of hail days, May–July) features a weak 500 hPa trough over northern regions, a ridge over eastern China, and a sea-level pressure contrast between an anticyclone over the West Sea of Korea and a low over Northeast China, driving warm advection and steep lapse rates (26 K). Clusters 3 (16.6%) and 4 (15.1%) dominate April–May, characterized by vertically stacked lows and shortwave troughs, respectively, while Cluster 2 (7.7%, October) involves deep upper troughs with no major hail activity. Discriminant analysis validated clusters using 500 hPa geopotential height, 850 hPa temperature, and SLP loadings, achieving 93.4% classification accuracy (74.2% cross-validated). The methodology provides a transferable framework for hail prediction in regions with analogous topography and synoptic environments, emphasizing cold upper troughs, low-level moisture convergence, and moderate instability (CAPE 1000–2000 J kg⁻1). This regional analysis offers actionable insights for hail forecasting and risk mitigation in the DPR Korea. The methodology and findings are transferable to regions with similar topographic and synoptic environments.