Abstract：Thunderstorm is a kind of convective weather phenomenon, which is one of the frequent natural disasters and has great destructive power. Due to its short duration and small range, precise thunderstorm warning in the weather forecast is difficult. At present, there are many kinds of weather monitoring equipment, but the data utilization is low, and the research which use the atmospheric electric field data to judge the occurrence of lightning is relatively less. Thus, it is significant to establish a more effective thunderstorm forecasting model by exploring the correlation between the atmospheric electric field data and lightning. In this paper, we use the convolution neural network to analyze and verify the relationship between them. The classification results of the model reflect the correlation between those two kinds of data more directly.
郭橙,毋立芳,杜建苹,包坤,李庆申. 大气电场数据与雷电相关性的深度学习算法[J]. 信号处理, 2017, 33(4): 607-612.
GUO Cheng,WU Li-fang,DU Jian-ping,BAO Kun,LI Qing-shen. Deep Learning Algorithm for the Correlation between Atmospheric Electric Field Data and Thunderstorm. Journal of Signal Processing, 2017, 33(4): 607-612.
2017, Vol. 33 
