Abstract
Lightning is an instantaneous, intense, and convective weather phenomenon that can produce great destructive power and easily cause serious economic losses and casualties. It always occurs in convective storms with small spatial scales and short life cycles. Weather radar is one of the best operational instruments that can monitor the detailed 3D structures of convective storms at high spatial and temporal resolutions. Thus, extracting the features related to lightning automatically from 3D weather radar data to identify lightning strike locations would significantly benefit future lightning predictions. This article makes a bold attempt to apply three-dimensional radar data to identify lightning strike locations, thereby laying the foundation for the subsequent accurate and real-time prediction of lightning locations. First, that issue is transformed into a binary classification problem. Then, a suitable dataset for the recognition of lightning strike locations based on 3D radar data is constructed for system training and evaluation purposes. Furthermore, the machine learning methods of a convolutional neural network, logistic regression, a random forest, and k-nearest neighbors are employed to carry out experiments. The results show that the convolutional neural network has the best performance in identifying lightning strike locations. This technique is followed by the random forest and k-nearest neighbors, and the logistic regression produces the worst manifestation.
Highlights
Lightning is a spark of electricity in the atmosphere between clouds, the air, or the ground (Maggio et al, 2009)
Are its area under the curve (AUC) and P-R the worst, but its false positive rate (FPR) is the highest among those of all the models, which shows that logistic regression (LR) has difficulty dealing with the identification of lightning strike locations
We convert the problem of identifying lightning strike locations into a binary classification problem, and a sliding window strategy is utilized to construct a dataset suitable for the identification of lightning strike locations based on 3D weather radar data
Summary
Lightning is a spark of electricity in the atmosphere between clouds, the air, or the ground (Maggio et al, 2009). Zhu et al (2021) presented a machinelearning approach (support vector machines) to classify cloudto-ground and intracloud lightning These methods use a limited number of data factors to analyze the relationships with lightning strike locations, and the recognition effects are often unsatisfactory. Based on the data of nine weather radar slices at different elevations, Wang et al (2018) used a convolutional neural network model to identify the spatial structures of threedimensional abnormal clouds when hail lands. Zhou et al (2020) proposed a new semantic segmentation-based deep learning network for cloud-to-ground lightning nowcasting named LightningNet. The recognition performances of the learning models on thunderstorms and gales were compared. Zhou et al (2020) proposed a new semantic segmentation-based deep learning network for cloud-to-ground lightning nowcasting named LightningNet This model conducts reliable lightning nowcasting by using multisource data
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