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<p>Tropical cyclones are among the most destructive weather phenomena, posing severe threats through extreme precipitation events that substantially influence global rainfall patterns. The intensification of extreme precipitation under climate change highlights their critical need to understand tropical cyclone-induced rainfall. In this context, this systematic review focuses on estimating cyclonic rainfall in observed datasets using the distance-based approach. This paper reviews 91 studies selected from Scopus, ScienceDirect and Springer databases based on their relevance to the present study. Results from this review indicate that 60% of the literature is concentrated in China and the USA, with nearly 45% focusing on the Western North Pacific Basin. The International Best Track Archive for Climate Stewardship dataset is used in 25% of studies and nearly 40% of articles utilize observed rain gauge data for rainfall estimation. Satellite-based precipitation estimates such as TRMM and IMERG offer high spatial and temporal resolution, but their accuracy crucially depends on how well they align with in situ gauge observations. Around 54 studies that applied the distance approach employed a 500&#xa0;km radius and some studies used additional techniques such as the objective synoptic analysis technique and the moving ROCI buffer technique. Given the profound influence of tropical cyclones on regional precipitation, this review reaffirms their importance in understanding inter-annual variability and trends in tropical cyclone-induced rainfall patterns. Historical data derived from such studies can enhance the development of machine learning models and weather prediction systems, thereby improving tropical cyclone rainfall forecasts.</p>

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Tropical cyclone-induced rainfall estimation using the distance approach: a systematic review

  • 期刊论文

  • 发表日期：

    2025年9月27日

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  • M. Selva Kumar,
  • V. Geethalakshmi,
  • S. Pazhanivelan,
  • K. Subrahmaniyan,
  • Ga Dheebakaran,
  • V. Saravanakumar,
  • K. Bhuvaneswari,
  • K. Pugazenthi

摘要

Tropical cyclones are among the most destructive weather phenomena, posing severe threats through extreme precipitation events that substantially influence global rainfall patterns. The intensification of extreme precipitation under climate change highlights their critical need to understand tropical cyclone-induced rainfall. In this context, this systematic review focuses on estimating cyclonic rainfall in observed datasets using the distance-based approach. This paper reviews 91 studies selected from Scopus, ScienceDirect and Springer databases based on their relevance to the present study. Results from this review indicate that 60% of the literature is concentrated in China and the USA, with nearly 45% focusing on the Western North Pacific Basin. The International Best Track Archive for Climate Stewardship dataset is used in 25% of studies and nearly 40% of articles utilize observed rain gauge data for rainfall estimation. Satellite-based precipitation estimates such as TRMM and IMERG offer high spatial and temporal resolution, but their accuracy crucially depends on how well they align with in situ gauge observations. Around 54 studies that applied the distance approach employed a 500 km radius and some studies used additional techniques such as the objective synoptic analysis technique and the moving ROCI buffer technique. Given the profound influence of tropical cyclones on regional precipitation, this review reaffirms their importance in understanding inter-annual variability and trends in tropical cyclone-induced rainfall patterns. Historical data derived from such studies can enhance the development of machine learning models and weather prediction systems, thereby improving tropical cyclone rainfall forecasts.