Utilizing high spatiotemporal resolution lightning observation data for thunderstorm identification

Thunderstorm weather has a significant impact on human society, and the precise identification of thunderstorm processes is crucial for thunderstorm weather prediction and the reduction of thunderstorm-related disasters. Existing research on thunderstorm identification methods is mainly divided into two categories: those based on radar detection data and those based on lightning location data. However, current studies suffer from large identification errors and an inability to effectively reflect the shape and boundaries of thunderstorms. To address these issues, this paper proposes a high spatiotemporal resolution thunderstorm identification method based on lightning location data. The method utilizes DBSCAN for identifying thunderstorm clusters and subsequently employs Alpha Shapes to extract boundaries. The proposed method is evaluated through optimal parameter selection and comparative experiments using a lightning location dataset from the Guangxi region. The results indicate that the performance of the new method in thunderstorm identification far surpasses that of existing methods.