A quantitative method for the similarity assessment of typhoon tracks

Yangchen Di, Mingyue Lu (), Min Chen, Zhangjian Chen, Zaiyang Ma and Manzhu Yu
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Yangchen Di: Nanjing University of Information Science & Technology
Mingyue Lu: Nanjing University of Information Science & Technology
Min Chen: Nanjing Normal University
Zhangjian Chen: Zhejiang Academy of Surveying and Mapping
Zaiyang Ma: Nanjing Normal University
Manzhu Yu: The Pennsylvania State University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 112, issue 1, No 25, 587-602

Abstract: Abstract Typhoons are one of the most dangerous types of natural hazards; they are always developed in the western and southwestern Pacific Ocean and pose economic and human security threats to the Pacific Rim annually. Therefore, many scholars in related fields devote themselves to finding an effective way to analyze and forecast typhoon tracks to prevent disasters. Similarity analysis of typhoon tracks can provide great help for typhoon prediction. In this paper, a model for typhoon similarity analysis is proposed to effectively measure and quantify the similarity between two historical typhoon tracks based on the dynamic time warping algorithm, in which five typhoon elements—namely, longitude, latitude, central pressure, expanded Beaufort scale, and movement speed—are integrated to derive a final similarity percentage indicating the similarity level. At the end of this paper, case studies concerning historical typhoons and the ongoing Typhoon 202,106 In-Fa are also conducted to verify the validity and effectiveness of the proposed model. The results show that the proposed model can effectively provide a quantitative similarity of two typhoon tracks when functioning well on ongoing typhoons with a cutoff rule and supplying promising support for typhoon prediction simultaneously.

Keywords: Typhoon tracks; Similarity assessment; Time series; Dynamic time warping (DTW) (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11069-021-05195-6

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