Comparison of ionospheric reflection heights for LEMPs produced by lightning return strokes of different polarity

Leal, Adonis F. R.; Rakov, Vladimir A.

doi:10.1016/j.jastp.2020.105426

Journal of Atmospheric and Solar-Terrestrial Physics

2020

DOI: 10.1016/j.jastp.2020.105426

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Comparison of ionospheric reflection heights for LEMPs produced by lightning return strokes of different polarity

Adonis F. R. Leal

Vladimir A. Rakov

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2024

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Cited by 4 publications

(1 citation statement)

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“…The same tre Figure 20 shows lightning electric field waveforms for −CG recorded at different distances highlighted with CAM. Note that we do not have distance information, but the distance can be estimated based on the skywaves (peak following the ground wave or first peak [46,47]). Even for lightning recorded from far distances in which the lightning electric field waveform has multiple skywaves (multiple peaks), the most important parts of the waveforms are the fast transitions located at the first peak (ground wave).…”

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confidence: 99%

Performance Analysis of Artificial Intelligence Approaches for LEMP Classification

Leal,

Ferreira,

Matos

2023

Remote Sensing

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Lightning Electromagnetic Pulses, or LEMPs, propagate in the Earth–ionosphere waveguide and can be detected remotely by ground-based lightning electric field sensors. LEMPs produced by different types of lightning processes have different signatures. A single thunderstorm can produce thousands of LEMPs, which makes their classification virtually impossible to carry out manually. The lightning classification is important to distinguish the types of thunderstorms and to know their severity. Lightning type is also related to aerosol concentration and can reveal wildfires. Artificial Intelligence (AI) is a good approach to recognizing patterns and dealing with huge datasets. AI is the general denomination for different Machine Learning Algorithms (MLAs) including deep learning and others. The constant improvements in the AI field show us that most of the Lightning Location Systems (LLS) will soon incorporate those techniques to improve their performance in the lightning-type classification task. In this study, we assess the performance of different MLAs, including a SVM (Support Vector Machine), MLP (Multi-Layer Perceptron), FCN (Fully Convolutional Network), and Residual Neural Network (ResNet) in the task of LEMP classification. We also address different aspects of the dataset that can interfere with the classification problem, including data balance, noise level, and LEMP recorded length.

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confidence: 99%

Performance Analysis of Artificial Intelligence Approaches for LEMP Classification

Leal,

Ferreira,

Matos

2023

Remote Sensing

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Virtual Reflection Height of Nighttime Equatorial Ionosphere Estimated With Low‐Frequency Magnetic Sferics Measured in Malacca

Wang,

Lu,

Cheng

et al. 2024

JGR Atmospheres

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The return stroke of cloud‐to‐ground (CG) lightning is an impulsive radiator of very low‐frequency/low‐frequency (VLF/LF) electromagnetic signals allowing for the remote sensing of lower ionosphere over large spatial coverage. In this study, we examined the LF magnetic fields measured in Malacca, Malaysia, to probe reflection heights of the lower ionosphere near the equator on three different nights in 2021. The results show that the virtual ionospheric height at nighttime typically ranged from 82.0 to 90.0 km, with a mean value of 85.3 km. Our measurements also revealed significant variations in the virtual ionospheric height across different regions over a spatial scale of about 800 km. The maximum height difference was about 5.0 km. Moreover, the fluctuation characteristics are observed in both estimated ionospheric height and calculated peak reflection ratio during similar periods. This fluctuation may be related to atmospheric gravity waves in the nighttime ionosphere. In addition, we compared the virtual ionospheric height estimated from CG strokes of different polarities, and the results showed that the virtual reflection height for positive CG strokes is lower than that for negative ones.

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Characterization of Lightning Electric Field Waveforms Using a Large Database: 1. Methodology

Leal

Rakov

2021

IEEE Trans. Electromagn. Compat.

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Comparison of ionospheric reflection heights for LEMPs produced by lightning return strokes of different polarity

Cited by 4 publications

References 13 publications

Performance Analysis of Artificial Intelligence Approaches for LEMP Classification

Performance Analysis of Artificial Intelligence Approaches for LEMP Classification

Virtual Reflection Height of Nighttime Equatorial Ionosphere Estimated With Low‐Frequency Magnetic Sferics Measured in Malacca

Characterization of Lightning Electric Field Waveforms Using a Large Database: 1. Methodology

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