基于贝叶斯推断的风云四号闪电成像仪中虚假信号滤除  

Filtering of False Signals in Fengyun-4A Lightning Mapping Imager Based on Bayesian Inference

作　　者：张晓黄 张其林[1] 张仙玲[1] 陈亚芳 廉纯皓 王磊[2] ZHANG Xiao-huang;ZHANG Qi-lin;ZHANG Xian-ling;CHEN Ya-fang;LIAN Chun-hao;WANG Lei(Key Laboratory of Meteorological Disaster, Ministry of Education, Joint International Research Laboratory of Climate and Environment Change, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disaster, Key Laboratory for Aerosol-cloud-precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044, China;Yunnan Electric Power Test & Research Institute (Group) Co., Ltd, Kunming 650217, China)

机构地区：[1]南京信息工程大学气象灾害教育部重点实验室气候与环境变化国际合作联合实验室气象灾害预报预警与评估协同创新中心中国气象局气溶胶与云降水重点开放实验室,南京210044 [2]云南电力试验研究院(集团)有限公司,昆明650217

出　　处：《科学技术与工程》2019年第12期23-32,共10页Science Technology and Engineering

基　　金：上海航天科技创新基金(SAST2016032);国家自然科学基金(41575004;41775006);配电网综合防雷体系研究与工程示范项目(YNKJQQ00000274)资助

摘　　要：多种原因使得风云四号A星(Fengyun-4A,FY-4A)闪电成像仪(lightning mapping imager,LMI)探测到的发光事件中包含大量虚假信号,且目前针对不同的噪声来源采用不同的滤除方法。因此,为寻求一种较通用的、能同时滤除不同来源虚假信号的方法,尝试采用贝叶斯概率推断对2017年8月8日京津冀地区一次雷暴过程中的LMI实测数据进行虚假信号滤除。首先,采用国际上惯用的卫星闪电聚类参数化方案,将时间间隔小于330 ms且空间间隔小于16. 5 km的事件认为是闪电引起的连续信号,不满足此条件的则视为孤立的虚假信号而被滤除。然后,根据连续信号的辐射强度和该位置处的背景亮度与孤立虚假信号之间的差异性,采用贝叶斯概率推断来滤除连续信号中的虚假信号。考虑到贝叶斯推断可能存在误差,进一步根据闪电时空连续性特征设计了信号再判断方法,尽可能地滤除虚假信号且保留真实闪电信号。最后,利用云顶亮温资料及全球闪电定位网(worldwide lightning location network,WWLLN)数据对滤除结果进行初步检验。结果表明:虚假信号约占所有信号的50%;滤除虚假信号后,90%以上的信号分布在云顶低于240 K的区域内,且与WWLLN闪击点具有较好的对应。这种同时考虑信号时空连续性及不同信号间特征量差异性的虚假信号滤除算法,为设计更通用高效的虚假信号滤除算法做出了尝试。Variety of reasons may contribute to the fact that there are many different kinds of false signals in the luminous events detected by the Fengyun-4A(FY-4A) lightning mapping imager (LMI). In addition,different kinds of filtering algorithms had been established particularly to filter out corresponding false signals originating from different sources. In order to find a more general method that can filter out different kinds of false signals at the same time,an algorithm using Bayesian inference was proposed to filter out false signals detected by LMI during a thunderstorm in the Beijing-Tianjin-Hebei region on August 8,2017. First,events that occurred within 330 ms and 16. 5 km of each other were considered as consecutive signals caused by lightning based on lightning clustering parameterization method. If the condition was not satisfied,the other events were regarded as isolated false signals and were filtered out. Then,there were differences between the intensity,the background brightness of consecutive signals and these of isolated false signals. Based on these differences,the Bayesian inference was proposed for the first time to filter out the false signals in the consecutive signals. Considering the possible error of Bayesian inference,a signal re-judgment method based on the characteristics of lightning space-time continuity was designed to filter out false signals as much as possible and to preserve real lightning signals. Finally,temperature of brightness blackbody(TBB) data and worldwide lightning location network(WWLLN) data were used to verify the algorithm preliminarily. The results show that the false signals account for about 50% of all signals. After filtering out the false signals,more than 90% of the signals are in the region with cloud-top temperature lower than 240 K. The signals’ locations show great correspondence with these of WWLLN strokes. This false signal filtering algorithm,which took into account both the spatiotemporal continuity of the signals and the differences of signal character

关 键 词：闪电成像仪 虚假信号 贝叶斯推断 滤除算法 

分 类 号：P427[天文地球—大气科学及气象学]