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<p>This study evaluates the diagnostic utility of two moisture-dynamic parameters—moist vorticity (MV) and moist divergence (MD)—in identifying heavy rainfall that occurred in central Democratic People’s Republic of Korea (DPR Korea) from August 28 to 29, 2018. High-resolution hourly precipitation data from the Hydro-Meteorological Service of DPR Korea (HMSK) and the NCEP Final Analysis (FNL) dataset were employed for analysis. Results demonstrate that both MV and MD enhance the spatial localization and temporal tracking of heavy rainfall, though their performance varies. MV exhibits superior precision in pinpointing rainfall locations during peak intensity, with its temporal evolution closely aligning with observed precipitation maxima. In contrast, MD identifies broader regions of heavy rainfall but generates inferred precipitation centers that frequently deviate from observational data, limiting its spatial accuracy. Quantitative assessment using the threat score (TS) reveals that MV outperforms traditional vorticity metrics by 27% (average TS = 0.24), while MD shows a 39% improvement over conventional divergence (average TS = 0.17). Notably, MD shows a higher percentage improvement than baseline diagnostics based on conventional divergence. This suggests its relative advancement. However, MD’s absolute TS remains lower than MV, due to inherent limitations in spatial correspondence. MV’s strong alignment with rainfall movement and concentration underscores its reliability for real-time forecasting, whereas MD’s utility lies in complementary large-scale moisture convergence analysis. These findings highlight the value of integrating both parameters into operational forecasting systems in DPR Korea. MV’s precision in tracking rainfall cores, combined with MD’s diagnostic insights into moisture dynamics, could improve early warning systems and mitigate risks associated with extreme precipitation events. Further research is recommended to explore region-specific calibration and synergistic applications of MV and MD.</p>

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Performance evaluation of moisture-dynamic diagnostic parameters for heavy rainfall event in DPR korea: a case study

  • 期刊论文

  • 发表日期：

    2025年10月13日

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  • Kum-Ryong Jo,
  • Kang-Sa Yun,
  • Yong-Sik Ham,
  • Hyok-Chol Kim,
  • Won-Uk Kang

摘要

This study evaluates the diagnostic utility of two moisture-dynamic parameters—moist vorticity (MV) and moist divergence (MD)—in identifying heavy rainfall that occurred in central Democratic People’s Republic of Korea (DPR Korea) from August 28 to 29, 2018. High-resolution hourly precipitation data from the Hydro-Meteorological Service of DPR Korea (HMSK) and the NCEP Final Analysis (FNL) dataset were employed for analysis. Results demonstrate that both MV and MD enhance the spatial localization and temporal tracking of heavy rainfall, though their performance varies. MV exhibits superior precision in pinpointing rainfall locations during peak intensity, with its temporal evolution closely aligning with observed precipitation maxima. In contrast, MD identifies broader regions of heavy rainfall but generates inferred precipitation centers that frequently deviate from observational data, limiting its spatial accuracy. Quantitative assessment using the threat score (TS) reveals that MV outperforms traditional vorticity metrics by 27% (average TS = 0.24), while MD shows a 39% improvement over conventional divergence (average TS = 0.17). Notably, MD shows a higher percentage improvement than baseline diagnostics based on conventional divergence. This suggests its relative advancement. However, MD’s absolute TS remains lower than MV, due to inherent limitations in spatial correspondence. MV’s strong alignment with rainfall movement and concentration underscores its reliability for real-time forecasting, whereas MD’s utility lies in complementary large-scale moisture convergence analysis. These findings highlight the value of integrating both parameters into operational forecasting systems in DPR Korea. MV’s precision in tracking rainfall cores, combined with MD’s diagnostic insights into moisture dynamics, could improve early warning systems and mitigate risks associated with extreme precipitation events. Further research is recommended to explore region-specific calibration and synergistic applications of MV and MD.