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雷暴探测研究的进展
引用本文:涂朝勇, 倪长健, 朱育雷, 郑云华. 2018: 基于投影寻踪动态聚类模型的川东北雷暴预警的分析. 暴雨灾害, 37(6): 582-586. DOI: 10.3969/j.issn.1004-9045.2018.06.012
作者姓名:涂朝勇  倪长健  朱育雷  郑云华
作者单位:1.成都信息程大学大气科学学院 高原大气与环境四川省重点实验室, 成都 610225;2.巴中市气象局, 巴中 636000
基金项目:国家重点研发计划项目(2018YFC0214004);四川省科技厅重点研发项目(2018SZ0287)
摘    要:

基于达州市2015年10月-2016年9月的闪电定位和探空观测资料,以850 hPa与500 hPa温差、大气可降水量、K指数、对流有效位能、对流抑制和抬升指数作为雷暴预警因子,利用投影寻踪动态聚类方法对该时段内的雷暴个例构建了预警模型。结果表明:(1)模型预报结果定量评估的临界成功指数为72.00%,该模型对个例达到了识别和预警效果;(2)该预警模型与常规预警方法相比,具有识别率高,计算简便,客观性强等特点,可为雷暴预警提供了一种新的客观预报方法。



关 键 词:雷暴预警  预报因子  投影寻踪动态聚类  模型
收稿时间:2017-04-24

A Projection Pursuit Algorithm for Exploratory Data Analysis
TU Chaoyong, NI Changjian, ZHU Yulei, ZHENG Yunhua. 2018: Analysis of thunderstorm warning in northeastern Sichuan based on projection pursuit dynamic cluster model. Torrential Rain and Disasters, 37(6): 582-586. DOI: 10.3969/j.issn.1004-9045.2018.06.012
Authors:TU Chaoyong  NI Changjian  ZHU Yulei  ZHENG Yunhua
Affiliation:1.College of Atmospheric Sciences, Chengdu University of Information Technology, Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, Chengdu 610225;2.Bazhong Meteorological Bureau, Bazhong 636000
Abstract:Based on data of lightning locations and sounding observation data in Dazhou from October 2015 to September 2016, taking temperature difference between 850 hPa and 500 hPa, precipitable water, K Index, convective available potential energy, convective inhibition energy and lifting index as thunderstorm warning factors, the projection pursuit dynamic cluster method is used to construct the warning model for thunderstorms in the period. The results are as follows. (1) The critical success index for quantitative evaluation of model prediction results was 72%, and the case mentioned above was identified by this method. (2) Compared to conventional warning methods, this model has advantages of high recognition rate, simple calculation and objectivity. The model provides a new objective method for thunderstorm warning.
Keywords:thunderstorm warning  forecast factor  PPDC  model
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