Evaluating Strategies for Automatically Detecting the Long Continuing Current Signatures on Electric Field Waveforms of Lightning Events Occurring in the Metropolitan Area of Belo Horizonte

This paper discusses the results of a computational approach in Python for automatically detecting Long Continuing Currents (LCCs) in lightning data measured in the metropolitan region of Belo Horizonte, Minas Gerais. The authors developed a computational routine to reliably output LCC characteristics, such as initial and final time, duration and amplitude variation per time. This method enables fast, accurate identification of severe LCCs in large datasets to support statistical analysis. The results show positive responses from the code for the cases of negative and positive LCC analyzed but show challenges to be considered for a perfect evaluation.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-09-27
DOI
https://doi.org/10.5281/zenodo.13851549
Primary Topic
Lightning and Electromagnetic Phenomena
Type
article
Field-Weighted Citation Impact
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article

Evaluating Strategies for Automatically Detecting the Long Continuing Current Signatures on Electric Field Waveforms of Lightning Events Occurring in the Metropolitan Area of Belo Horizonte

Zenodo (CERN European Organization for Nuclear Research)
Lightning and Electromagnetic Phenomena
article

Evaluating Strategies for Automatically Detecting the Long Continuing Current Signatures on Electric Field Waveforms of Lightning Events Occurring in the Metropolitan Area of Belo Horizonte

article en

Abstract

This paper discusses the results of a computational approach in Python for automatically detecting Long Continuing Currents (LCCs) in lightning data measured in the metropolitan region of Belo Horizonte, Minas Gerais. The authors developed a computational routine to reliably output LCC characteristics, such as initial and final time, duration and amplitude variation per time. This method enables fast, accurate identification of severe LCCs in large datasets to support statistical analysis. The results show positive responses from the code for the cases of negative and positive LCC analyzed but show challenges to be considered for a perfect evaluation.

Zenodo (CERN European Organization for Nuclear Research)
New York City Fire Department (US), Federal Center for Technological Education of Minas Gerais (BR), Universitat Politècnica de Catalunya (ES)
Openalex Percentile: Top 64%
Lightning and Electromagnetic Phenomena
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