| A PRELIMINARY STUDY ON THUNDERSTORM FORECAST WITH LS-SVM METHOD |
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| 作者姓名： | 王振会 张祎 朱佳 |
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| 作者单位： | Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044 China;School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044 China;Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044 China;School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044 China;Li;Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044 China;School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044 China |
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| 基金项目： | China Social Welfare Research Project (GYHY200806014) |
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| 摘 要： | The LS-SVM(Least squares support vector machine) method is presented to set up a model to forecast the occurrence of thunderstorms in the Nanjing area by combining NCEP FNL Operational Global Analysis data on 1.0°×1.0° grids and cloud-to-ground lightning data observed with a lightning location system in Jiangsu province during 2007-2008.A dataset with 642 samples,including 195 thunderstorm samples and 447 non-thunderstorm samples,are randomly divided into two groups,one(having 386 samples) for modeling and the rest for independent verification.The predictors are atmospheric instability parameters which can be obtained from the NCEP data and the predictand is the occurrence of thunderstorms observed by the lightning location system.Preliminary applications to the independent samples for a 6-hour forecast of thunderstorm events show that the prediction correction rate of this model is 78.26%,false alarm rate is 21.74%,and forecasting technical score is 0.61,all better than those from either linear regression or artificial neural network.
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| 关 键 词： | thunderstorm forecast LS-SVM Nanjing area cloud-to-ground lightning NCEP |
| 收稿时间： | 6/7/2011 12:00:00 AM |
| 修稿时间： | 2012/11/20 0:00:00 |
A PRELIMINARY STUDY ON THUNDERSTORM FORECAST WITH LS-SVM METHOD |
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| WANG Zhen-hui,ZHANG Yi and ZHU Jia.A PRELIMINARY STUDY ON THUNDERSTORM FORECAST WITH LS-SVM METHOD[J].Journal of Tropical Meteorology,2013,19(1):104-108. |
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| Authors: | WANG Zhen-hui ZHANG Yi and ZHU Jia |
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| Institution: | Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044 China;School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044 China;Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044 China;School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044 China;Li;Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044 China;School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044 China |
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| Abstract: | The LS-SVM (Least squares support vector machine) method is presented to set up a model to forecast the occurrence of thunderstorms in the Nanjing area by combining NCEP FNL Operational Global Analysis data on 1.0°×1.0° grids and cloud-to-ground lightning data observed with a lightning location system in Jiangsu province during 2007-2008. A dataset with 642 samples, including 195 thunderstorm samples and 447 non-thunderstorm samples, are randomly divided into two groups, one (having 386 samples) for modeling and the rest for independent verification. The predictors are atmospheric instability parameters which can be obtained from the NCEP data and the predictand is the occurrence of thunderstorms observed by the lightning location system. Preliminary applications to the independent samples for a 6-hour forecast of thunderstorm events show that the prediction correction rate of this model is 78.26%, false alarm rate is 21.74%, and forecasting technical score is 0.61, all better than those from either linear regression or artificial neural network. |
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| Keywords: | thunderstorm forecast LS-SVM Nanjing area cloud-to-ground lightning NCEP |
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