Lightning a Fundamental of Atmospheric Electricity

Akinyemi, M. L.; Boyo, A. O.; Emetere, Moses; Usikalu, M.R.; Olawole, F.O.

doi:10.1016/j.ieri.2014.09.039

Cited by 12 publications

(9 citation statements)

References 9 publications

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“…Based on this, CG lightning is classified into two types: (a) +CG lightning (a flash consisting of several strokes) and (b) -CG lightning (a flash composed of a single stroke) (Rakov & Uman, 2003). According to Akinyemi et al (2014), CG lightning accounts for 75% of global lightning activity while CC lightning accounts for the remaining 25%. This lightning activity reflects the same pattern in seasonal variation concerning the southern and northern hemispheres (Amin, 2015).…”

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confidence: 99%

Statistical Study of Global Lightning Activity and Thunderstorm‐Induced Gravity Waves in the Ionosphere Using WWLLN and GNSS‐TEC

et al. 2023

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We present a statistical study of global lightning activity and correlate this activity with ionospheric fluctuations in the Total Electron Content (TEC). Thunderstorms play a significant role in modulating the ionosphere's plasma distribution. Using the well‐known World Wide Lightning Location Network (WWLLN) system, we investigate the diurnal, monthly, and seasonal variations in lightning stroke numbers across six continents. Typically, the monsoon causes a significant increase in lightning occurrences, as reflected in the WWLLN observations. We chose a time frame of July–December 2019, which usually covers the peak and post‐monsoon seasons on all the continents. The diurnal lightning activity is observed to be highest in the early afternoon for all the locations. In addition, we observe hemispherical changes in lightning activity that appear opposite in nature. Using the Global Navigation Satellite System (GNSS)‐International GNSS Service data, we use the Vertical TEC (VTEC) and Rate of change Of TEC Index as proxies for the amplitude scintillation S4 index. Using the wavelet analysis of the small‐scale fluctuations in the VTEC profile, we provide a time series analysis of wave‐like TEC fluctuations alongside lightning stroke count as a proxy for thunderstorm activity. It is found that, in most cases, Traveling Ionospheric Disturbances (TIDs) or a local maximum of small‐scale fluctuation in VTEC (dVTEC) that could be coupled to atmospheric gravity waves are observed during periods of peak thunderstorm activity. Most of the locations show that the spectral power of TIDs and the stroke energy density are highly correlated.

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confidence: 99%

Statistical Study of Global Lightning Activity and Thunderstorm‐Induced Gravity Waves in the Ionosphere Using WWLLN and GNSS‐TEC

et al. 2023

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“…A small positive charge is also usually found below the main negative charge center (Figure 1). These electrostatically-charged regions form due to the collision of graupel (soft hail) pellets and lighter ice crystals [12], [3], [13]. The positively charged ice-crystals are lighter and are therefore more easily carried to the upper portion of the thundercloud, typically 10 km above sea level [3].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Prediction of Thunderstorm Severity Using Remote Sensing Weather Data

Essa

Hunt

Gijben

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Lightning is one of the leading causes of electrical outages in South Africa, and the most severe weather-related killer in the country. Unfortunately for risk management, quantitative lightning prediction remains challenging. In this study, we evaluate the accuracy of LSTM neural network model variants on thunderstorm severity using remote sensing weather data. These LSTM model variants are LSTM-FC, CNN-LSTM and ConvLSTM variants. The CNN-LSTM and ConvLSTM models recognize spatio-temporal features which assist processing. The data used consists of lightning detection network data from the SALDN and weather-feature information from the network of weather stations operated by the SAWS. We forecast thunderstorm severity every hour, as quantified by lightning flash

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“…This discharge is the most harmful to human production and life, and can directly lead to human death. In general, about 90% of cloud-to-ground flashes are negative cloud-to-ground flashes (−CG), and the remaining 10% are positive cloud-to-ground flashes (+CG) [4,5]. Narrow bipolar events (NBE) is usually generated during a thunderstorm [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Classification of VLF/LF Lightning Signals Using Sensors and Deep Learning Methods

Wang

Huang

et al. 2020

Sensors

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Lightning waveform plays an important role in lightning observation, location, and lightning disaster investigation. Based on a large amount of lightning waveform data provided by existing real-time very low frequency/low frequency (VLF/LF) lightning waveform acquisition equipment, an automatic and accurate lightning waveform classification method becomes extremely important. With the widespread application of deep learning in image and speech recognition, it becomes possible to use deep learning to classify lightning waveforms. In this study, 50,000 lightning waveform samples were collected. The data was divided into the following categories: positive cloud ground flash, negative cloud ground flash, cloud ground flash with ionosphere reflection signal, positive narrow bipolar event, negative narrow bipolar event, positive pre-breakdown process, negative pre-breakdown process, continuous multi-pulse cloud flash, bipolar pulse, skywave. A multi-layer one-dimensional convolutional neural network (1D-CNN) was designed to automatically extract VLF/LF lightning waveform features and distinguish lightning waveforms. The model achieved an overall accuracy of 99.11% in the lightning dataset and overall accuracy of 97.55% in a thunderstorm process. Considering its excellent performance, this model could be used in lightning sensors to assist in lightning monitoring and positioning.

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Lightning a Fundamental of Atmospheric Electricity

Cited by 12 publications

References 9 publications

Statistical Study of Global Lightning Activity and Thunderstorm‐Induced Gravity Waves in the Ionosphere Using WWLLN and GNSS‐TEC

Statistical Study of Global Lightning Activity and Thunderstorm‐Induced Gravity Waves in the Ionosphere Using WWLLN and GNSS‐TEC

Deep Learning Prediction of Thunderstorm Severity Using Remote Sensing Weather Data

Classification of VLF/LF Lightning Signals Using Sensors and Deep Learning Methods

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