Assessment and application of tropical cyclone clustering in the South China Sea
Abstract Accurate classification of tropical cyclone (TC) tracks is essential for evaluating and mitigating the potential disaster risks associated with TCs. In this study, three commonly used methods (K-means, Fuzzy C-Means, and Self-Organizing Maps) are assessed for clustering historical TC tracks...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-83872-9 |
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| author | Yan Yan Nergui Nanding Xiaomeng Li Yifan Shi Bing Chen |
| author_facet | Yan Yan Nergui Nanding Xiaomeng Li Yifan Shi Bing Chen |
| author_sort | Yan Yan |
| collection | DOAJ |
| description | Abstract Accurate classification of tropical cyclone (TC) tracks is essential for evaluating and mitigating the potential disaster risks associated with TCs. In this study, three commonly used methods (K-means, Fuzzy C-Means, and Self-Organizing Maps) are assessed for clustering historical TC tracks that originated in the South China Sea from 1949 to 2023. The results show that the K-means method performs the best, while the Fuzzy C-Means and Self-Organizing Maps methods are also viable alternatives. By applying the K-means method, the distinct characteristics of the four cluster types are investigated. Each type has different characteristics in terms of lifespan, wind speed, frequency of occurrence, Power Dissipation Index, and the spatial distribution of accumulated rainfall. The influence of El Niño-Southern Oscillation (ENSO) is evident in the patterns of TC activity. Specifically, there is a higher frequency of TC activity during La Niña years, whereas during El Niño years, the activity is reduced. This observation highlights the important role that ENSO plays in shaping the behavior of TCs and provides valuable information for predicting and preparing for these events. Understanding the unique characteristics of each cluster can help authorities and communities in the region better prepare for and respond to the potential impacts of TCs. |
| format | Article |
| id | doaj-art-649805ef7dbb4c258c0af4689a925c4b |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-649805ef7dbb4c258c0af4689a925c4b2025-01-19T12:18:48ZengNature PortfolioScientific Reports2045-23222025-01-0115111310.1038/s41598-024-83872-9Assessment and application of tropical cyclone clustering in the South China SeaYan Yan0Nergui Nanding1Xiaomeng Li2Yifan Shi3Bing Chen4College of Ocean and Meteorology & South China Sea Institute of Marine Meteorology, Guangdong Ocean UniversitySchool of Earth Sciences, Yunnan UniversityNational Meteorological Centre, China Meteorological AdministrationCollege of Ocean and Meteorology & South China Sea Institute of Marine Meteorology, Guangdong Ocean UniversityCollege of Ocean and Meteorology & South China Sea Institute of Marine Meteorology, Guangdong Ocean UniversityAbstract Accurate classification of tropical cyclone (TC) tracks is essential for evaluating and mitigating the potential disaster risks associated with TCs. In this study, three commonly used methods (K-means, Fuzzy C-Means, and Self-Organizing Maps) are assessed for clustering historical TC tracks that originated in the South China Sea from 1949 to 2023. The results show that the K-means method performs the best, while the Fuzzy C-Means and Self-Organizing Maps methods are also viable alternatives. By applying the K-means method, the distinct characteristics of the four cluster types are investigated. Each type has different characteristics in terms of lifespan, wind speed, frequency of occurrence, Power Dissipation Index, and the spatial distribution of accumulated rainfall. The influence of El Niño-Southern Oscillation (ENSO) is evident in the patterns of TC activity. Specifically, there is a higher frequency of TC activity during La Niña years, whereas during El Niño years, the activity is reduced. This observation highlights the important role that ENSO plays in shaping the behavior of TCs and provides valuable information for predicting and preparing for these events. Understanding the unique characteristics of each cluster can help authorities and communities in the region better prepare for and respond to the potential impacts of TCs.https://doi.org/10.1038/s41598-024-83872-9Tropical cycloneCluster analysisSouth China SeaPrecipitation |
| spellingShingle | Yan Yan Nergui Nanding Xiaomeng Li Yifan Shi Bing Chen Assessment and application of tropical cyclone clustering in the South China Sea Scientific Reports Tropical cyclone Cluster analysis South China Sea Precipitation |
| title | Assessment and application of tropical cyclone clustering in the South China Sea |
| title_full | Assessment and application of tropical cyclone clustering in the South China Sea |
| title_fullStr | Assessment and application of tropical cyclone clustering in the South China Sea |
| title_full_unstemmed | Assessment and application of tropical cyclone clustering in the South China Sea |
| title_short | Assessment and application of tropical cyclone clustering in the South China Sea |
| title_sort | assessment and application of tropical cyclone clustering in the south china sea |
| topic | Tropical cyclone Cluster analysis South China Sea Precipitation |
| url | https://doi.org/10.1038/s41598-024-83872-9 |
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