银川河东机场小样本雷暴分类客观预报方法研究  

The variation characteristics of summer precipitation in Jiangsu Province during the last 50 years and its relationship with ENSO

作　　者：谷思雨 胡文东[1,2] 彭维耿 朱冬梅 胡亮帆 丁禹钦 GU Siyu;HU Wendong;PENG Weigeng;ZHU Dongmei;HU Liangfan;DING Yuqin(School of Atmospheric Science,Chengdu University of Information Technology,Chengdu 610225,China;Plateau Atmosphere and Environment Key Laboratory of Sichuan Province,Chengdu 610225,China;Aviation Meteorological Center,Air Traffic Management Bureau of CAAC,Beijing 100122,China;Air Traffic Management Bureau of Yinchuan,Yinchuan 750009,China)

机构地区：[1]成都信息工程大学大气科学学院,成都610225 [2]高原大气与环境四川省重点实验室,成都610225 [3]中国民用航空局空中交通管理局航空气象中心,北京100122 [4]民航银川空中交通管理局,银川750002

出　　处：《气象科学》2019年第6期763-774,共12页Journal of the Meteorological Sciences

基　　金：四川省基础应用研究计划重点项目(2018JY0056);中国气象局预报员专项(CMAYBY2018-083);西北空管局《银川河东机场雷暴生消规律及其个例的数值模拟研究》资助

摘　　要：利用2000—2016年欧洲中心再分析资料、探空及地面自动气象站观测资料,根据天气过程的强度和对应物理量,分别对银川河东机场雷暴伴随大风、降水等不同天气现象类别进行定量化转换,采用峰度偏度系数、χ2以及Q-Q图3种方法对定量转换的数据进行正态性检验,结果表明:按天气现象分类的样本服从正态分布,未分类样本基本服从。利用逐步回归、多元回归、非线性回归、BP人工神经元网络以及支持向量机5种方法,分别建立了雷暴现象与强度预报模型。结果表明:BP网络以及SVM对天气现象的预报能力较强;分类逐步、多元以及非线性回归模型分别对弱雨、强雨以及大风和降雨同时发生的天气强度预报效果较好。并在此基础上通过最优分析设计了河东机场不同种类雷暴天气定性和定量预报相结合的业务系统。Based on the ECMWF reanalysis data and AWS data from 2000 to 2016,the intensity of the weather process and the corresponding physical field,and quantitative transformation of thunderstorm cases with different weather phenomena such as gale and precipitation in Yinchuan Hedong International Airport,the normality tests were conducted on the quantitatively transformed data by using the coefficients of Kurtosis and Skewness,Chi-square and Q-Q plot.The results showed that the samples classified by weather phenomena passed normality,and the unclassified samples basically obeyed normality distribution.Thunderstorms phenomenon and intensity prediction models were developed in five ways of stepwise regression,multiple regression,nonlinear regression,BP artificial neural network and Support Vector Machine(SVM).The forecast results showed that the BP artificial neural network and SVM had strong forecasting ability for weather phenomena,and the classified stepwise,multivariate and nonlinear regression models showed better effects on the intensity of weak weather,heavy rain,simultaneous occurrence with gale and rainfall forecasting.Based on the analysis and research above,an operational platform system combining qualitative and quantitative prediction of different types of thunderstorms in Yinchuan Hedong International Airport was established.

关 键 词：航空气象 雷暴 定量化 正态分布检验 预报模型 业务系统 

分 类 号：P457.9[天文地球—大气科学及气象学]