闪电(连接器)
波形
遥感
雷电探测
雷雨
环境科学
气象学
计算机科学
物理
实时计算
模式识别(心理学)
地质学
电信
人工智能
功率(物理)
雷达
量子力学
作者
Qingliu Yang,Jiaquan Wang,Xiao Zhou,Shangbo Yuan,Xiaoyang Meng,Fang Xiao,Hongling Jin,Weiqing Xue,Qiming Ma
摘要
Abstract Hai‐Nan Lightning Detection Network (HNLDN) is composed of seven sites that accept very low frequency/low frequency (VLF/LF) signals. A total of seven sites are evenly distributed on Hai‐Nan Island. The baseline distance is between 50 and 150 km, which is about five times longer than in typical short‐baseline VLF/LF imaging networks. Each detection station uses zero‐phase digital filtering to avoid the phase deviation caused by filtering. HNLDN uses an improved peak search method to increase the number of detected of lightning waveforms, applies a One‐dimensional convolutional neural network (1D‐CNN) to identify and classify the waveforms, and to the increase the detection efficiency of intracloud lightning. This article analyzes three typical thunderstorm processes. From inside to outside the network, the detection efficiency of HNLDN relative to the advanced direction‐time lightning detection system (ADTD) is 239%, 181%, and 19%, respectively. It can be seen that the detection efficiency is negatively correlated with the distance from the thunderstorm to HNLDN. The average location error of HNLDN inside the network is 190 m.
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