Entropy-Based Thunderstorm Imaging System With Real-Time Prediction and Early Warning

Abstract

A combination of the sample entropy and the artificial intelligence (AI) would hopefully bring progress to the smart thunderstorm detection. In this paper, we establish an entropy-based thunderstorm point charge imaging system with the data clustering, wherein data are ultra-reliable and have low-latency three-dimensional atmospheric electric field (3DAEF) values. In particular, a high resolution 3DAEF sensor with the single-axis rotary vane is developed to measure the time sequence signal of the 3DAEF. The signal is first denoised, and then decomposed into multiple groups of branch data with the same number of samples. In order to break through the limitations of existing clustering rules, we propose a 3DAEF signal reconstruction method based on entropy intervals. By reconstructing the branch data, multiple time-scale data are formed. Finally, the curve fitting of the data is performed to realize the imaging after clustering the data. Experimental results suggest that the imaging error after the clustering is reduced by about 2.33%. Comparisons with radar charts show that the proposed system can effectively image the point charge moving paths.