摘要
电离层最高可观测频率(MOF)是电离层的一个重要的特征参数,它表征了电离层的实时状态信息。文中基于新乡市的返回散射探测系统,对2014—2016年采集的数据进行预处理分析,获得电离层F层模式对应的MOF在24 h内的月中值分布,讨论分析了MOF月中值参数的分布特征,并与适应于中国区域的亚大模型对应的结果进行对比。结果表明,在相同的群距离下,常规探测下的电离层MOF值在不同时刻下的分布趋势与理论结果一致,日出时刻MOF会逐渐增加,白天时刻MOF相对稳定,日落时刻MOF值逐渐降低,夜晚MOF值持续降低,凌晨时刻到达最低点,实测值与理论结果相差范围为0.8 MHz^5.4 MHz;电离层突发E层发生时,1000 km群距离对应的MOF值达到最大,主要是因为突发E层产生了遮蔽,通信距离受限所致。
The maximum observed frequency(MOF)is an important characteristic parameter of the ionosphere,which characterizes the real-time status information of the ionosphere.Based on the backscatter detection system in Xinxiang City,this paper first obtains the monthly median distribution about MOF in F layer mode of the ionosphere within 24 hours,through the pretreatment analysis on the data collected from 2014 to 2016;and then discusses and analyzes the distribution characteristics of monthly median parameters about MOF;finally,the theoretical results using the sub-large model adapted to China region are compared with analysis results based on the measured data in this thesis.The results show that the distribution trend of ionospheric MOF values under conventional detection at different moments is consistent with the theoretical results at the same group distance.The MOF gradually increases at sunrise,relatively stable at daytime,gradually decreases at sunset,continues to decrease during the night,and reach the lowest point in the early morning.The difference between the measured value and the theoretical value is 0.8 MHz^5.4 MHz;When the ionospheric sporadic E-layer occurs,the MOF corresponding to the1000 km group distance reaches the maximum,because communication distance is limited cause by masking of the sporadic E-layer.
作者
宋建梅
华彩成
杨东升
郭晓彤
SONG Jian-mei;HUA Cai-cheng;YANG Dong-sheng;GUO Xiao-tong(China Research Institute of Radiowave Propagation,Qindao 266107,China)
出处
《中国电子科学研究院学报》
北大核心
2020年第11期1050-1056,共7页
Journal of China Academy of Electronics and Information Technology
基金
国防技术基础资助项目(JSHS2016210C009)。
关键词
MOF
月中值
突发E层
时变特性
统计分析
MOF
monthly median
sporadic E-layer
time-varying characteristics
statistical analysis