基于支持向量机的雷暴潜势预报初探
投稿时间: 2016-03-02  最后修改时间: 2016-06-25  点此下载全文
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周明薇 南京信息工程大学 大气物理学院, 江苏 南京 210044;南京信息工程大学 气象灾害教育部重点实验室, 江苏 南京 210044;湖南省气象灾害防御技术中心, 湖南 长沙 410007 zhoumw2005@163.com 
肖稳安 南京信息工程大学 大气物理学院, 江苏 南京 210044;南京信息工程大学 气象灾害教育部重点实验室, 江苏 南京 210044  
张其林 南京信息工程大学 大气物理学院, 江苏 南京 210044;南京信息工程大学 气象灾害教育部重点实验室, 江苏 南京 210044  
周四清 洞口县气象局, 湖南 邵阳 422000  
彭双姿 邵阳市气象局, 湖南 邵阳 422000  
基金项目:中国气象局2011年气象关键技术集成与应用项目(CMAG2011M36);公益性行业科研专项(GYHY2008060147)
中文摘要:根据2008—2010年夏季邵阳地区的NCEP全球再分析资料(分辨率为1°×1°)和闪电定位资料,利用支持向量机(SVM)分类方法建立该地区雷暴潜势预报模型,并用测试样本检验了该模型的预报能力,同时与Logistic回归模型和Bayes判别法的预报效果进行了比较。结果表明,SVM模型的预报准确率为86.21%,虚警率为15.25%,漏报率为13.79%。对比三种模型的TSS技术评分,发现使用SVM方法建立的模型对邵阳地区雷暴预报的效果最好,评分值为0.79。因此,SVM方法所建立的模型可以为邵阳地区6 h的雷暴潜势预报提供一定的参考价值。
中文关键词:雷暴预报  支持向量机  Logistic回归  Bayes判别法
 
A preliminary study on thunderstorm forecast based on SVM
Abstract:Based on the data of the NCEP global reanalysis,with a resolution of 1°×1° and cloud-to-ground lightning in Shaoyang from June to August 2008 to 2010,an SVM classification model of thunderstorm forecast was set up.First,the ground flashes data for 6 hours corresponding to the NCEP data were counted.The flashes were greater than or equal to 3,which is defined as a thunderstorm.A non-thunderstorm is defined as when the flash is zero.If the number was greater than 0 or less than 3,it was not included in the statistical samples.In addition,if the flashes were detected by the lightning location system,but the thunderstorm days were not recorded at the meteorological observation station,then the data were not included on the model samples.According to these criteria,1 007 samples were counted,286 of which were thunderstorm samples,and 721 were non-thunderstorm samples.80% of the samples were used as the training samples,and the remaining 20% were used as the test samples.Next,27 parameters were extracted from the NCEP global reanalysis data on 1°×1° degree grids,and 21 convection parameters were calculated,of which a total of 48 physical quantities were used to set up a thunderstorm-forecasting model according to the method of SVM classification.The correlation between 48 physical quantities and the occurrence of thunderstorm of training samples was calculated,then 11 significant predictors greater than 0.3 were selected.Before the forecasting model was established,all parameters were normalized.By repeatedly changing the parameter values,the highest classification accuracy of the parameters c and g were finally selected as the model parameters.When the two parameters were determined,the potential thunderstorm forecast model was set up.The test results show that the prediction accuracy of the SVM model was 86.21%,false alarm rate was 15.25%,and missing rate was 13.79%.The scope and step length were manually set during the parameter optimization,yet after several repetitions some modeling errors were still present,which led to the test results differing.In order to validate the stability of the model,70% (90%) of the modeling samples were re-selected and verified with optimal parameters.It was found that there was no significant difference in accuracy among the three kinds of model samples,which illustrates the stability and reliability of the forecast model,and the model can forecast the thunderstorm in 6 hours.Logistic regression discrimination and Bayesian discrimination are the two other forecast methods used for thunderstorms.Through comparison of the three forecast methods,it was found that the forecast model based on SVM gave the best prediction performance with TSS,and the true skill statistic was 0.79.The rates of the false alarm and pseudo fault report were lower than when using the other two methods.Therefore,it can be seen that the forecast model built using the SVM method is able to provide a certain reference value for potential thunderstorm prediction in the Shaoyang area,with a higher accuracy rate of distinguishing thunderstorms and non-thunderstorm.In other words,the prediction model can provide a reference value for potential thunderstorm forecast in the Shaoyang area.
keywords:thunderstorm forecast  SVM  Logistic regression  Bayesian discrimination
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