A Thunderstorm Cloud Point Charge Localization Method Based on CEEMDAN and SG Filtering

The current thunderstorm monitoring methods ignore the nonlinear and non-stationary characteristics of atmospheric electric field signals, which has a negative effect on the monitoring results. Based on complementary ensemble empirical mode decomposition with adaptive noise and Savitzky-Golay filtering (CEEMDAN-SG), a point charge localization method for the thunderstorm cloud is proposed. After CEEMDAN is used to decompose the electric field signal into a series of intrinsic mode function (IMF) components, the signal is reconstructed after SG filtering of those noise-dominant components. Then, the reconstructed signal is used for point charge localization correction. By changing the signal samples, the decomposition order and SNR of CEEMDAN-SG, CEEMDAN, etc. are compared, and the performance of the method is analyzed. Experiments show that compared to the SNR before reconstruction, the SNR after reconstruction is improved by about 3%. At the same time, the results can match the radar chart well on the time scale. After using the CNN-LSTM network model, it is found that compared with the original signal, the amplitude of the absolute error of the reconstructed signal is larger, so that the signal characteristics can be more displayed. This once again proves the localization effects.