A Novel Tornado Detection Algorithm Based on XGBoost
Tornadoes are severe convective weather exhibiting localized and sudden occurrences. Weather radar is widely regarded as the most effective tool for monitoring tornadoes and issuing early warnings. Dual-polarization updating has significantly improved tornado prediction and forecasting abilities. Th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/1/167 |
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| author | Qiangyu Zeng Guoxiu Zhang Shangdan Huang Wenwen Song Jianxin He Hao Wang Yin Liu |
| author_facet | Qiangyu Zeng Guoxiu Zhang Shangdan Huang Wenwen Song Jianxin He Hao Wang Yin Liu |
| author_sort | Qiangyu Zeng |
| collection | DOAJ |
| description | Tornadoes are severe convective weather exhibiting localized and sudden occurrences. Weather radar is widely regarded as the most effective tool for monitoring tornadoes and issuing early warnings. Dual-polarization updating has significantly improved tornado prediction and forecasting abilities. This article proposes an innovative tornado detection algorithm based on XGBoost which is suitable for dual-polarization radar data, was upgraded and has been used in China since 2019, and has been applied in the Tornado Key Open Laboratory of the China Meteorological Administration. The characteristics associated with the velocity attributes, reflectivity, velocity spectrum width, differential reflectivity, and correlation coefficient are used in the algorithm to achieve real-time tornado detection. Experimental evaluations have demonstrated that the proposed algorithm significantly improves tornado detection rates and leading times. Compared with the traditional TDA-RF algorithm based on Doppler weather radar data, the TDA-XGB algorithm introduces dual polarization parameters (such as differential reflectivity and the correlation coefficient), which effectively improves tornado identification performance. In addition, the TDA-XGB algorithm combines artificial intelligence-assisted learning to optimize the traditional algorithm based on the tornado vortex signature (TVS) and tornado debris signature (TDS), further improving the detection effect. Furthermore, the algorithm provides classification probabilities in the genesis and evolution of tornadoes, thereby supporting forecasters in their efforts to anticipate and issue timely tornado warnings. |
| format | Article |
| id | doaj-art-de01745469b548d0bda5ba65da1ded73 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-de01745469b548d0bda5ba65da1ded732025-01-10T13:20:27ZengMDPI AGRemote Sensing2072-42922025-01-0117116710.3390/rs17010167A Novel Tornado Detection Algorithm Based on XGBoostQiangyu Zeng0Guoxiu Zhang1Shangdan Huang2Wenwen Song3Jianxin He4Hao Wang5Yin Liu6College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225, ChinaCollege of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225, ChinaCollege of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225, ChinaSichuan Meteorological Service Center, Chengdu 610072, ChinaCollege of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225, ChinaCollege of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225, ChinaJiangsu Meteorological Observation Center, Key Laboratory of Atmosphere Sounding, China Meteorological Administration, Nanjing 210041, ChinaTornadoes are severe convective weather exhibiting localized and sudden occurrences. Weather radar is widely regarded as the most effective tool for monitoring tornadoes and issuing early warnings. Dual-polarization updating has significantly improved tornado prediction and forecasting abilities. This article proposes an innovative tornado detection algorithm based on XGBoost which is suitable for dual-polarization radar data, was upgraded and has been used in China since 2019, and has been applied in the Tornado Key Open Laboratory of the China Meteorological Administration. The characteristics associated with the velocity attributes, reflectivity, velocity spectrum width, differential reflectivity, and correlation coefficient are used in the algorithm to achieve real-time tornado detection. Experimental evaluations have demonstrated that the proposed algorithm significantly improves tornado detection rates and leading times. Compared with the traditional TDA-RF algorithm based on Doppler weather radar data, the TDA-XGB algorithm introduces dual polarization parameters (such as differential reflectivity and the correlation coefficient), which effectively improves tornado identification performance. In addition, the TDA-XGB algorithm combines artificial intelligence-assisted learning to optimize the traditional algorithm based on the tornado vortex signature (TVS) and tornado debris signature (TDS), further improving the detection effect. Furthermore, the algorithm provides classification probabilities in the genesis and evolution of tornadoes, thereby supporting forecasters in their efforts to anticipate and issue timely tornado warnings.https://www.mdpi.com/2072-4292/17/1/167dual-polarization radartornadodetectionXGBoost |
| spellingShingle | Qiangyu Zeng Guoxiu Zhang Shangdan Huang Wenwen Song Jianxin He Hao Wang Yin Liu A Novel Tornado Detection Algorithm Based on XGBoost Remote Sensing dual-polarization radar tornado detection XGBoost |
| title | A Novel Tornado Detection Algorithm Based on XGBoost |
| title_full | A Novel Tornado Detection Algorithm Based on XGBoost |
| title_fullStr | A Novel Tornado Detection Algorithm Based on XGBoost |
| title_full_unstemmed | A Novel Tornado Detection Algorithm Based on XGBoost |
| title_short | A Novel Tornado Detection Algorithm Based on XGBoost |
| title_sort | novel tornado detection algorithm based on xgboost |
| topic | dual-polarization radar tornado detection XGBoost |
| url | https://www.mdpi.com/2072-4292/17/1/167 |
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