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We are developing a Stochastic Tropical cyclone Rain Model (STRM), which probabilistically generates rainfall bands around tropical cyclones based on analysis of the Mesoscale Spectral Model (MSM) simulation results. In this symposium, we present an evaluation of the reproduction bias on the stochastic tropical cyclone rainfall model by comparing model predictions—generated from the track data of 10 historical tropical cyclones—with the MSM data associated with those tropical cyclones. The model reproduces rainfall well generally concerning observed significant rainfall. However, verification of the histogram of MSM quantiles relative to the frequency distribution of total rainfall for 100 cases predicted by the stochastic model showed that there is a possibility of overestimation or underestimation depending on the distance from the center of the tropical cyclone.

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On the Reproducibility Bias of Stochastic Rain Model for Tropical Cyclones

  • 会议论文

  • 发表日期：

    2026年

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  • Maya Takeda,
  • Sota Nakajo

摘要

We are developing a Stochastic Tropical cyclone Rain Model (STRM), which probabilistically generates rainfall bands around tropical cyclones based on analysis of the Mesoscale Spectral Model (MSM) simulation results. In this symposium, we present an evaluation of the reproduction bias on the stochastic tropical cyclone rainfall model by comparing model predictions—generated from the track data of 10 historical tropical cyclones—with the MSM data associated with those tropical cyclones. The model reproduces rainfall well generally concerning observed significant rainfall. However, verification of the histogram of MSM quantiles relative to the frequency distribution of total rainfall for 100 cases predicted by the stochastic model showed that there is a possibility of overestimation or underestimation depending on the distance from the center of the tropical cyclone.