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A Lightning Warning Method Using Atmospheric Electric Field Based on EEWT-ASG and Morpho
Version 1
: Received: 28 April 2023 / Approved: 30 April 2023 / Online: 30 April 2023 (23:56:56 CEST)
A peer-reviewed article of this Preprint also exists.
Li, X.; Yang, L.; Yin, Q.; Yang, Z.; Zhou, F. Lightning Risk Warning Method Using Atmospheric Electric Field Based on EEWT-ASG and Morpho. Atmosphere 2023, 14, 1002. Li, X.; Yang, L.; Yin, Q.; Yang, Z.; Zhou, F. Lightning Risk Warning Method Using Atmospheric Electric Field Based on EEWT-ASG and Morpho. Atmosphere 2023, 14, 1002.
Abstract
The current methods for lightning risk warnings that are based on atmospheric electric field (AEF) data have a tendency to rely on single features, which results in low robustness and efficiency. Additionally, there is a lack of research on cancelling warning signals, contributing to the high false alarm rate (FAR) of these methods. To overcome these limitations, this study proposes a lightning risk warning method that incorporates enhanced empirical Wavelet transform-Adaptive Savitzky Gorey filter (EEWT-ASG) and one-dimensional morphology, using time-frequency domain features obtained through the Wavelet transform (WT). The proposed method achieved a probability of detection (POD) of 77.11%, miss alarm rate (MAR) of 22.89%, FAR of 40.19%, and critical success index (CSI) of 0.51, as evaluated on 83 lightning processes. This method can issue a warning signal up to 22 minutes in advance for lightning processes.
Keywords
Atmospheric electric field (AEF); lightning risk warning; enhanced empirical Wavelet transform-Adaptive Savitzky Golay filter (EEWT-ASG); one-dimensional morphology; Wavelet transform (WT)
Subject
Computer Science and Mathematics, Signal Processing
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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