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Accurate heavy rainfall location prediction is crucial for meteorological services. This study introduces the Precipitation Attribution Distance (PAD) method for evaluating heavy rainfall location prediction in Guizhou Province, China. PAD, based on precipitation attribution theory and distance weighting functions, eliminates the need for considering the resolution of prediction and observation fields and setting precipitation intensity thresholds, effectively reflecting the spatial matching between prediction and observation. The method is applied to evaluate the prediction of an extreme heavy rainfall event in Guizhou’s complex mountainous region on July 8, 2023, considering different initialization times, forecast lead times, and forecast models. Results demonstrate that the 12 and 24-h precipitation location predictions from the MESO model initialized at 00 UTC on July 8th closely match the observations, with corresponding PAD values also being small. The two-dimensional probability distribution of PAD further reveals the displacement bias of the precipitation band, indicating that PAD can effectively reflect the displacement direction and distance of the predicted and observed precipitation fields in Guizhou Province. Applying PAD to evaluate the impact of different microphysics schemes on regional heavy rainfall location prediction effectively reflects the spatial distribution characteristics and displacement directions of precipitation location predictions from different physical schemes.

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Evaluating Location Errors in Heavy Rainfall Forecasts Over Complex Terrain: Applying Precipitation Attribution Distance (PAD) to Guizhou’s 2023 Extreme Event

  • 会议论文

  • 发表日期：

    2026年

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  • Fang Peng,
  • Guhui Wu,
  • Xiaodong Yan,
  • Dapeng Qi

摘要

Accurate heavy rainfall location prediction is crucial for meteorological services. This study introduces the Precipitation Attribution Distance (PAD) method for evaluating heavy rainfall location prediction in Guizhou Province, China. PAD, based on precipitation attribution theory and distance weighting functions, eliminates the need for considering the resolution of prediction and observation fields and setting precipitation intensity thresholds, effectively reflecting the spatial matching between prediction and observation. The method is applied to evaluate the prediction of an extreme heavy rainfall event in Guizhou’s complex mountainous region on July 8, 2023, considering different initialization times, forecast lead times, and forecast models. Results demonstrate that the 12 and 24-h precipitation location predictions from the MESO model initialized at 00 UTC on July 8th closely match the observations, with corresponding PAD values also being small. The two-dimensional probability distribution of PAD further reveals the displacement bias of the precipitation band, indicating that PAD can effectively reflect the displacement direction and distance of the predicted and observed precipitation fields in Guizhou Province. Applying PAD to evaluate the impact of different microphysics schemes on regional heavy rainfall location prediction effectively reflects the spatial distribution characteristics and displacement directions of precipitation location predictions from different physical schemes.