As an important factor in fine thunderstorm detections, a multi-time scale thunderstorm monitoring, warning and imaging system is proposed in this paper. The first computing phase involves a decomposition, classification, denoising and reconstruction of the atmospheric electric field signals (AEFSs), collected by a self-made three-dimensional AEF apparatus, based on autocorrelation characteristics and Fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$C$</tex-math></inline-formula> -Means (FCM). Secondly, FCM classifies the equally divided AEFS components. A scale reconstruction rule is put forward and applied to obtain multi-time scale AEF branch data, according to the component temporal continuity in the same class. A corresponding scale correction strategy is then proposed. Thunderstorm point charge coordinate results are calculated by using branch data, and noise points contained in these results are removed. Finally, the curve fitting of denoised coordinate results is performed to image the point charge moving path. Empirical results confirm that the proposed system effectively warns and images thunderstorms, as well as provides a valid reference for multi-scale thunderstorm monitoring.
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