Influence of inner-core symmetry on tropical cyclone rapid intensification and its forecasting by a machine learning ensemble model
This study proposed a novel quantitative index, the Symmetric Ratio, derived from satellite observations to depict Tropical Cyclone (TC) inner-core symmetry. This index is found to be significantly influential in TC Rapid Intensification (RI). We applied four machine learning (ML) models—Decision Tr...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Weather and Climate Extremes |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2212094725000283 |
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| author | Jiali Zhang Qinglan Li Liguang Wu Qifeng Qian Xuyang Ge Sam Tak Wu Kwong Yun Zhang Xinyan Lyu Guanbo Zhou Gaozhen Nie Pak Wai Chan Wai Kin Wong Linwei Zhu |
| author_facet | Jiali Zhang Qinglan Li Liguang Wu Qifeng Qian Xuyang Ge Sam Tak Wu Kwong Yun Zhang Xinyan Lyu Guanbo Zhou Gaozhen Nie Pak Wai Chan Wai Kin Wong Linwei Zhu |
| author_sort | Jiali Zhang |
| collection | DOAJ |
| description | This study proposed a novel quantitative index, the Symmetric Ratio, derived from satellite observations to depict Tropical Cyclone (TC) inner-core symmetry. This index is found to be significantly influential in TC Rapid Intensification (RI). We applied four machine learning (ML) models—Decision Tree, Random Forest, Light Gradient Boosting Machine, and Adaptive Boosting to forecast TC RI in the Northwestern Pacific (WNP) and North Atlantic (NA) basins from 2005 to 2023, with lead times of 12 and 24 hours. An ensemble model integrated these ML models to further enhance prediction accuracy. Model training used TC best track and reanalysis data from 2005 to 2020, with validation from 2021 to 2022. Independent forecasting tests from 2016 to 2023 applied real-time TC track data from the Automated Tropical Cyclone Forecasting system and environmental data from the Global Forecast System. Compared with the best deterministic model with the detection probability (POD) of 21 % and false alarm rate (FAR) of 50 % for 24-h RI forecasts in the NA basin during 2016–2020, our ensemble model demonstrated significant improvements, achieving a POD of 0.27 and an FAR of 0.18 for the same period. For 2021–2023, the ensemble model obtained POD values of 0.24 and 0.41, and FAR values of 0.33 and 0.45 for 24-h predictions in the NA and WNP basins, respectively. Key predictors identified include maximum wind speed tendency, vertical wind shear, potential intensity, and Symmetric Ratio. These findings advance our understanding of TC RI mechanisms and improve prediction accuracy. |
| format | Article |
| id | doaj-art-84c0fef679934082878f3e72abe6e7d0 |
| institution | DOAJ |
| issn | 2212-0947 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Weather and Climate Extremes |
| spelling | doaj-art-84c0fef679934082878f3e72abe6e7d02025-08-20T03:18:39ZengElsevierWeather and Climate Extremes2212-09472025-06-014810077010.1016/j.wace.2025.100770Influence of inner-core symmetry on tropical cyclone rapid intensification and its forecasting by a machine learning ensemble modelJiali Zhang0Qinglan Li1Liguang Wu2Qifeng Qian3Xuyang Ge4Sam Tak Wu Kwong5Yun Zhang6Xinyan Lyu7Guanbo Zhou8Gaozhen Nie9Pak Wai Chan10Wai Kin Wong11Linwei Zhu12Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; University of Chinese Academy of Sciences, Beijing, 101408, ChinaShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; Corresponding author.Fudan University, Shanghai, 200438, ChinaNational Meteorological Center, Beijing, 100081, ChinaNanjing University of Information Science and Technology, Nanjing, 210044, ChinaLingnan University, Hong Kong, ChinaSun Yat-Sen University, Shenzhen Campus, Shenzhen, 518107, ChinaNational Meteorological Center, Beijing, 100081, ChinaNational Meteorological Center, Beijing, 100081, ChinaNational Meteorological Center, Beijing, 100081, ChinaHong Kong Observatory, Hong Kong, ChinaHong Kong Observatory, Hong Kong, ChinaShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, ChinaThis study proposed a novel quantitative index, the Symmetric Ratio, derived from satellite observations to depict Tropical Cyclone (TC) inner-core symmetry. This index is found to be significantly influential in TC Rapid Intensification (RI). We applied four machine learning (ML) models—Decision Tree, Random Forest, Light Gradient Boosting Machine, and Adaptive Boosting to forecast TC RI in the Northwestern Pacific (WNP) and North Atlantic (NA) basins from 2005 to 2023, with lead times of 12 and 24 hours. An ensemble model integrated these ML models to further enhance prediction accuracy. Model training used TC best track and reanalysis data from 2005 to 2020, with validation from 2021 to 2022. Independent forecasting tests from 2016 to 2023 applied real-time TC track data from the Automated Tropical Cyclone Forecasting system and environmental data from the Global Forecast System. Compared with the best deterministic model with the detection probability (POD) of 21 % and false alarm rate (FAR) of 50 % for 24-h RI forecasts in the NA basin during 2016–2020, our ensemble model demonstrated significant improvements, achieving a POD of 0.27 and an FAR of 0.18 for the same period. For 2021–2023, the ensemble model obtained POD values of 0.24 and 0.41, and FAR values of 0.33 and 0.45 for 24-h predictions in the NA and WNP basins, respectively. Key predictors identified include maximum wind speed tendency, vertical wind shear, potential intensity, and Symmetric Ratio. These findings advance our understanding of TC RI mechanisms and improve prediction accuracy.http://www.sciencedirect.com/science/article/pii/S2212094725000283Tropical cycloneRapid IntensificationMachine learningSatellite dataBrightness temperature |
| spellingShingle | Jiali Zhang Qinglan Li Liguang Wu Qifeng Qian Xuyang Ge Sam Tak Wu Kwong Yun Zhang Xinyan Lyu Guanbo Zhou Gaozhen Nie Pak Wai Chan Wai Kin Wong Linwei Zhu Influence of inner-core symmetry on tropical cyclone rapid intensification and its forecasting by a machine learning ensemble model Weather and Climate Extremes Tropical cyclone Rapid Intensification Machine learning Satellite data Brightness temperature |
| title | Influence of inner-core symmetry on tropical cyclone rapid intensification and its forecasting by a machine learning ensemble model |
| title_full | Influence of inner-core symmetry on tropical cyclone rapid intensification and its forecasting by a machine learning ensemble model |
| title_fullStr | Influence of inner-core symmetry on tropical cyclone rapid intensification and its forecasting by a machine learning ensemble model |
| title_full_unstemmed | Influence of inner-core symmetry on tropical cyclone rapid intensification and its forecasting by a machine learning ensemble model |
| title_short | Influence of inner-core symmetry on tropical cyclone rapid intensification and its forecasting by a machine learning ensemble model |
| title_sort | influence of inner core symmetry on tropical cyclone rapid intensification and its forecasting by a machine learning ensemble model |
| topic | Tropical cyclone Rapid Intensification Machine learning Satellite data Brightness temperature |
| url | http://www.sciencedirect.com/science/article/pii/S2212094725000283 |
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