Automated Negative Lightning Return Strokes Characterization Using Brute-Force Search Algorithm

Over the years, many studies have been conducted to measure, analyze, and characterize the lightning electric field waveform for a better conception of the lightning phenomenon. Moreover, the characterization mainly on the negative return strokes also significantly contributed to the development of the lightning detection system. Those studies mostly performed the characterization using a conventional method based on manual observations. Nevertheless, this method could compromise the accuracy of data analysis due to human error. Moreover, a longer processing time would be required to analyze data, especially for larger sample sizes. Hence, this study proposed the development of an automated negative lightning return strokes characterization using a brute-force search algorithm. A total of 170 lightning electric field waveforms were characterized automatically using the proposed algorithm. The manual and automated data were compared by evaluating their percentage difference, arithmetic mean (AM), and standard deviation (SD). The statistical analysis showed a good agreement between the manual and automated data with a percentage difference of 1.19% to 4.82%. The results showed that the proposed algorithm could provide an efficient structure and procedure by reducing the processing time and minimizing human error. Non-uniformity among users during negative lightning return strokes characterization can also be eliminated.

Automated summary

Over the years, numerous studies have been conducted to measure, analyze, and characterize lightning electric field waveforms, contributing to the development of lightning detection systems. However, traditional manual observation methods suffer from accuracy and processing time issues. This study proposes an automated algorithm for characterizing negative lightning return strokes, which improves processing efficiency and reduces human error.