基于地面电场资料的雷暴临近预报研究
投稿时间: 2014-08-07  最后修改时间: 2014-09-03  点此下载全文
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作者单位E-mail
张强 南京信息工程大学电子与信息工程学院 q_zhang1990@163.com 
行鸿彦 南京信息工程大学 xinghy@nuist.edu.cn 
季鑫源 南京信息工程大学  
基金项目:国家自然科学基金(61072133)、江苏普通高校研究生实践创新计划项目(SJZZ_0112)、江苏省产学研联合创新资金计划(BY2013007-02,BY2011112)、江苏省高校科研成果产业化推进项目(HB2011-15) 、江苏省“信息与通信工程”优势学科平台、江苏省优势江苏省“六大人才高峰” 计划
中文摘要:本文利用总体平均经验模态分解算法(EEMD),对南京地区2010-2011年6-8月近地面大气电场资料进行分析,研究了晴天、弱雷暴和强雷暴天气条件下大气电场的振荡特征。单站电场仪观测范围内,以晴天大气为背景场,根据固有模态函数(IMF)方差最大值对应层数的动态特性,建立并验证了两种强度的雷暴临近预报模型。结果表明:弱雷暴发生前IMF方差最大值对应层数跳变幅度较平稳,而强雷暴跳变幅度逐渐加剧。对IMF方差最大值对应层数进行三次样条插值,可直观地表征雷暴发生发展过程,延长预报时间至1小时。利用这些特征对92个独立样本进行预报效果检验,预报的准确率为73.3%,虚警率为14.5%。
中文关键词:总体平均经验模态分解  大气电场  方差动态特性  雷电预警
 
Experimental Research on Thunderstorm Nowcasting Based on Atmospheric Electric Field Data
Abstract:This paper analyzes the atmospheric electric field data in Nanjing area form June to August during 2010 to 2011,by using the Ensemble Empirical Mode Decomposition and discusses the oscillation characteristics of the atmospheric electric field in fair, weak and severe tunderstorm weather. Taking fair weather as the background with only one station. Establishing and verifying a two-level thunderstorm nowcasting modle based on the dynamic characteristic between the maximum variance corresponding layers and the total number of decomposition layers of intrinsic mode function (IMF). and select the independent samples to forecast and verify. The results show that energy is concentrated in the low-frequency component in fair weather, and high-frequency component in thunderstorm. The maximum variance corresponding layers of IMFs changes stablely before weak thunderstorm weather, and fiercely before severe thunderstorm. According to these characteristics, 92 independent samples are tested and the results show that the detection probability of warning is 73.3% and the false alarm rate is 14.5%.
keywords:Ensemble empirical mode decomposition  Atmospheric electric field  Dynamic characteristics of variance  Lightning warning
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