Lightning Strike Location Identification Based on 3D Weather Radar Data

Lu, Mingyue; Zhang, Yadong; Ma, Zaiyang; Yu, Manzhu; Chen, Min; Zheng, Jianqin; Wang, Menglong

doi:10.3389/fenvs.2021.714067

Cited by 8 publications

(7 citation statements)

References 42 publications

(41 reference statements)

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“…Compared with previous studies [45], the LM-ResNet constructed in this paper has a certain effect on the monitoring of lightning locations. However, the results of lightning location recognition for discrete distributions are not optimal.…”

Section: Conclusion and Discussionmentioning

confidence: 88%

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Monitoring Lightning Location Based on Deep Learning Combined with Multisource Spatial Data

Zhang

Chen

et al. 2022

Remote Sensing

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Lightning is an important cause of casualties, and of the interruption of power supply and distribution facilities. Monitoring lightning locations is essential in disaster prevention and mitigation. Although there are many ways to obtain lightning information, there are still substantial problems in intelligent lightning monitoring. Deep learning combined with weather radar data and land attribute data can lay the foundation for future monitoring of lightning locations. Therefore, based on the residual network, the Lightning Monitoring Residual Network (LM-ResNet) is proposed in this paper to monitor lightning location. Furthermore, comparisons with GoogLeNet and DenseNet were also conducted to evaluate the proposed model. The results show that the LM-ResNet model has significant potential in monitoring lightning locations. In this study, we converted the lightning monitoring problem into a binary classification problem and then obtained weather radar product data (including the plan position indicator (PPI), composite reflectance (CR), echo top (ET), vertical integral liquid water (VIL), and average radial velocity (V)) and land attribute data (including aspect, slope, land use, and NDVI) to establish a lightning feature dataset. During model training, the focal loss function was adopted as a loss function to address the constructed imbalanced lightning feature dataset. Moreover, we conducted stepwise sensitivity analysis and single factor sensitivity analysis. The results of stepwise sensitivity analysis show that the best performance can be achieved using all the data, followed by the combination of PPI, CR, ET, and VIL. The single factor sensitivity analysis results show that the ET radar product data are very important for the monitoring of lightning locations, and the NDVI land attribute data also make significant contributions.

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Section: Conclusion and Discussionmentioning

confidence: 88%

“…Under normal circumstances, lightning is not very stable, and there is some drift during the discharge process. According to previous studies [45], it is believed that the lightning location within 1 km around the monitor is efficient. POD, FPR, and FNR were utilized to analyze the model results.…”

Section: Case Studymentioning

confidence: 91%

Monitoring Lightning Location Based on Deep Learning Combined with Multisource Spatial Data

Zhang

Chen

et al. 2022

Remote Sensing

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“…Lu M et al used 3D weather radar data to predict the location of lightning strikes [31]. They used a M*N sliding window to obtain radar data samples, one of which contains nine layers.…”

Section: Convolutional Neural Network Methodsmentioning

confidence: 99%

A Survey of Deep Learning-Based Lightning Prediction

Wang,

Hu,

et al. 2023

Preprint

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The escalation of climate change and the increasing frequency of extreme weather events have amplified the importance of precise and timely lightning prediction. This predictive capability is pivotal for the preservation of life, protection of property, and maintenance of crucial infrastructure safety. Recently, the rapid advancement and successful application of data-driven deep learning across diverse sectors, particularly in computer vision and spatio-temporal data analysis, have opened up innovative avenues for enhancing both the accuracy and efficiency of lightning prediction. This article presents a comprehensive review of the broad spectrum of existing lightning prediction methodologies. Starting from traditional numerical forecasting techniques, we traverse the path to the most recent breakthroughs in deep learning research. We encapsulate these diverse methods, shedding light on their progression and summarizing their capabilities, while also predicting their future development trajectories. This exploration is designed to enhance our understanding of these methodologies, allowing us to better utilize their strengths, navigate their limitations, and potentially integrate these techniques to create novel and powerful lightning prediction tools. Through such endeavors, our aim is to bolster our preparedness against the growing unpredictability of our climate and ensure a proactive stance towards lightning prediction.

show abstract

“…Lu et al used 3D weather radar data to predict the location of lightning strikes [32]. They used a M × N sliding window to obtain radar data samples, one of which contains nine layers.…”

Section: Convolutional Neural Network Methodsmentioning

confidence: 99%

A Survey of Deep Learning-Based Lightning Prediction

Wang,

Hu,

et al. 2023

Atmosphere

View full text Add to dashboard Cite

The escalation of climate change and the increasing frequency of extreme weather events have amplified the importance of precise and timely lightning prediction. This predictive capability is pivotal for the preservation of life, protection of property, and maintenance of crucial infrastructure safety. Recently, the rapid advancement and successful application of data-driven deep learning across diverse sectors, particularly in computer vision and spatio-temporal data analysis, have opened up innovative avenues for enhancing both the accuracy and efficiency of lightning prediction. This article presents a comprehensive review of the broad spectrum of existing lightning prediction methodologies. Starting from traditional numerical forecasting techniques, the path to the most recent breakthroughs in deep learning research are traversed. For these diverse methods, we shed light on their progression and summarize their capabilities, while also predicting their future development trajectories. This exploration is designed to enhance understanding of these methodologies to better utilize their strengths, navigate their limitations, and potentially integrate these techniques to create novel and powerful lightning prediction tools. Through such endeavors, the aim is to bolster preparedness against the growing unpredictability of climate and ensure a proactive stance towards lightning prediction.

show abstract

Lightning Strike Location Identification Based on 3D Weather Radar Data

Cited by 8 publications

References 42 publications

Monitoring Lightning Location Based on Deep Learning Combined with Multisource Spatial Data

Monitoring Lightning Location Based on Deep Learning Combined with Multisource Spatial Data

A Survey of Deep Learning-Based Lightning Prediction

A Survey of Deep Learning-Based Lightning Prediction

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