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<p>Topographical forcing plays a fundamental role in extreme precipitation events. This study investigates the influence of topographical factors on the 3-km hourly precipitation forecast from the China Meteorological Administration’s Mesoscale Weather Numerical Forecast System (CMA-MESO) during the 2021 Zhengzhou flood by employing spatio-temporal geographically weighted regression (GTWR). The results show that CMA-MESO overestimates the topographical impact on the spatial distribution of precipitation while underestimating its influence on the temporal variation of precipitation during the extreme event. The model’s underestimation of heavy precipitation in Zhengzhou can be largely attributed to the dominant influence of the near-surface temperature (ST)-precipitation relationship, which exhibits greater temporal stability in the model than in the observation. This study expands the application of GTWR to quantify the temporally varying topographical influence on extreme precipitation event, filling a gap in the understanding of non-stationary topographical forcing. Furthermore, it provides insights for improving the planet boundary layer (PBL) and orographic parameterization schemes in kilometer-scale predictions.</p>

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Topographical influence on kilometer-scale hourly precipitation prediction during the 2021 Zhengzhou flood

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月26日

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  • Linyun Yang,
  • Haoming Chen

摘要

Topographical forcing plays a fundamental role in extreme precipitation events. This study investigates the influence of topographical factors on the 3-km hourly precipitation forecast from the China Meteorological Administration’s Mesoscale Weather Numerical Forecast System (CMA-MESO) during the 2021 Zhengzhou flood by employing spatio-temporal geographically weighted regression (GTWR). The results show that CMA-MESO overestimates the topographical impact on the spatial distribution of precipitation while underestimating its influence on the temporal variation of precipitation during the extreme event. The model’s underestimation of heavy precipitation in Zhengzhou can be largely attributed to the dominant influence of the near-surface temperature (ST)-precipitation relationship, which exhibits greater temporal stability in the model than in the observation. This study expands the application of GTWR to quantify the temporally varying topographical influence on extreme precipitation event, filling a gap in the understanding of non-stationary topographical forcing. Furthermore, it provides insights for improving the planet boundary layer (PBL) and orographic parameterization schemes in kilometer-scale predictions.