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<p>The occurrence of tropical cyclones (TCs) in rare-event zones, such as mid-latitudes and inland regions, poses a significant challenge to disaster preparedness due to the limitations of traditional probabilistic forecasting methods based on historical events or random events set. This study addresses this challenge by developing a novel approach for estimating TC track probabilities in data-sparse areas. Focusing on the Northwest Pacific, we leverage the mechanistic link between TC genesis and movement and the subtropical high-pressure system. We find that the distance between the TC track and the edge of subtropical high exhibits a distinct spatial pattern. By quantifying the spatial correlation between historical TC tracks and subtropical highs distribution, we construct a probabilistic model for TC track prediction. Model simulations accurately reproduce the characteristics of historical high-frequency TC tracks and effectively estimate TC probabilities in rare-occurrence zones, surpassing the limitations of historical data reliance and random event set approaches. This methodology offers a promising framework for enhancing TC risk assessment and preparedness in understudied regions.</p>

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Tropical cyclone probability estimation in data-sparse regions: a subtropical high based approach

  • 期刊论文

  • 发表日期：

    2025年6月19日

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  • Jingyi Lu,
  • Jiazi Li,
  • Zhenguo Wang,
  • Xiaochao Li,
  • Chenlu Wang,
  • Xiaopeng Yang,
  • Zhiguo Gao,
  • Shaohua Wang,
  • Hua Zhang

摘要

The occurrence of tropical cyclones (TCs) in rare-event zones, such as mid-latitudes and inland regions, poses a significant challenge to disaster preparedness due to the limitations of traditional probabilistic forecasting methods based on historical events or random events set. This study addresses this challenge by developing a novel approach for estimating TC track probabilities in data-sparse areas. Focusing on the Northwest Pacific, we leverage the mechanistic link between TC genesis and movement and the subtropical high-pressure system. We find that the distance between the TC track and the edge of subtropical high exhibits a distinct spatial pattern. By quantifying the spatial correlation between historical TC tracks and subtropical highs distribution, we construct a probabilistic model for TC track prediction. Model simulations accurately reproduce the characteristics of historical high-frequency TC tracks and effectively estimate TC probabilities in rare-occurrence zones, surpassing the limitations of historical data reliance and random event set approaches. This methodology offers a promising framework for enhancing TC risk assessment and preparedness in understudied regions.