Lightning Types

Adhikari, Pitri Bhakta; Adhikari, Aashutosh

doi:10.1007/978-981-19-1926-8_3

Cited by 1 publication

(1 citation statement)

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“…The pulse duration of NBE is short, ranging from 2 to 20 μs (Jacobson & Light, 2012; Wu et al., 2014). Intracloud lightning discharge (IC) occurs in a single storm cloud or between different storm clouds and can be divided into two stages, including the active stage and the final stage (Adhikari & Adhikari, 2022; Rakov & Uman, 2003; X. M. Shao & Krehbiel, 1996). During the active stage, repetitive pulses with low amplitude and unipolarity can be detected, which are related to the stepped growth of the leaders (Brunner, 2016).…”

Section: Methodsmentioning

confidence: 99%

Toward an Interpretable CNN Model for the Classification of Lightning‐Produced VLF/LF Signals

Xiao,

Chen,

Wang

et al. 2023

JGR Atmospheres

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An interpretable convolutional neural network model is proposed for the classification of very low frequency and low frequency lightning electric field waveforms. This model adopts multi‐scale convolutional kernels and shortcut connections to enhance the ability of lightning waveform classification. Based on the data recorded from five provinces in China, the proposed model achieves an accuracy of 98.56% for a four‐type classification task including return strokes, the intra‐cloud lightning, preliminary breakdown, and narrow bipolar events. The proposed model is validated with another open‐source data set from Argentina with an accuracy of 98.45%, which shows good robustness. To ensure the classification, the features learned by the model are visualized. The class activation mapping (CAM) method is adopted to visualize the class‐specific contribution of different waveform parts by using the feature maps of the final convolutional layer. It is highlighted by the CAM method that the proposed model focuses on waveform parts that align with those areas of interests identified by human experts. The high‐contribution waveform parts are furtherly analyzed, which indicate that the proposed model possesses the capability to associate waveform features with the corresponding lightning discharge processes.

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