南京地区雷暴活动强度潜势预报

Potential Forecast of Thunderstorm Strength in Nanjing

利用江苏省2008、2009和2010年6—8月闪电定位资料对雷暴活动的强度进行了分级,并使用同一时段的探空资料计算了47个代表南京地区环境层结特征的对流参数,利用其与雷暴活动强度的相关性选取预报因子。在此基础上,采用Bayes分类法和Logistic回归分析法,结合逐步选择法进一步筛选预报因子,分别建立了两种雷暴强度的预报模型。通过检验独立样本对比分析两种模型的预报效果,结果表明,Logistic回归分析法的Hedike技巧评分为0.396,并能识别出30%的强雷暴,而Bayes分类法的Hedike技巧评分为0.370,只能识别出5%的强雷暴。Logistic回归分析法对雷暴强度的潜势预报具有较好的指示意义。进一步分析进入预报模型的9个对流参数,表明雷暴活动越强时,低层空气越暖湿,中层空气越干冷,高低层的风切变越大。

In order to improve the validity of thunderstorm strength forecast, the data of lightning location are used to classify the thunderstorm strength from June to August, 2008 to 2010, and the radiosonde data are used to calculate 47 convective parameters to represent the characteristics of the Nanjing thunderstorm environment. The relationships of thunderstorm strength with 47 convective parameters are analyzed respectively. The convection parameters closely related to thunderstorm strength are selected as the forecasting predictors of thunderstorm strength. The Bayesian classification method and Logistic regression analysis are adopted to establish two thunderstorm forecasting models. By use of the testing samples, the forecasting models are tested and compared. The results indicate that the Hedike skill score of the Logistic regression analysis is 0. 396, can identify 30% of the severe thunderstorm, but that of the Bayesian classification＇s is 0.37, can identify only 5G of the severe thunderstorms. It is obvious that Logistic regression has better indicative significance to thunderstorm potential strength forecast. The analysis of the nine convection parameters used to build the forecasting model, indicates that the stronger the thunderstorm activity, the warmer and moister the air at lower levels, the colder and drier the air at middle levels, the stronger the wind shear between lower and upper levels.