A PRELIMINARY STUDY ON THUNDERSTORM FORECAST WITH LS-SVM METHOD  

作　　者：王振会 张祎 朱佳 

机构地区：[1]Key Laboratory of Meteorological Disaster of Ministry of Education,Nanjing University of Information Science & Technology [2]School of Atmospheric Physics,Nanjing University of Information Science & Technology [3]Lightning Protection Centre of Zhejiang Province

出　　处：《Journal of Tropical Meteorology》2013年第1期104-108,共5页热带气象学报（英文版）

基　　金：China Social Welfare Research Project (GYHY200806014)

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.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.

关 键 词：THUNDERSTORM FORECAST LS-SVM NANJING area cloud-to-ground LIGHTNING NCEP 

分 类 号：P457.9[天文地球—大气科学及气象学]