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Wang, Jing; Tong, Xiaochong; Yang, Ling; Shang, Jian Ku; Liu, Chengbao; Bao, Shulong; Zhang, Zhiqing; Yang, Jun

doi:10.1109/jstars.2021.3124955

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Also, the smoother cumulative prediction may indicate an inability for the ConvLSTM to detect features. A possible solution would be to use satellite weather and lighting information instead, although there will be a trade-off of weather feature accuracy [42], [43]. An additional benefit of using satellite data is that the neural network models may detect Cumulonimbus cloud structure [44], [45].…”

Section: Discussionmentioning

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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