標(biāo)題：A lightning cluster identification method considering multi-scale spatiotemporal neighborhood relationships

作者：Manxing Shi, Peng Fan, Hantao Tao, Qin Li, Ju Wang, Yujun Liu, Lai Wei

來源出版物：PLOS One

DOI：10.1371/journal.pone.0333207

出版年：2025

文獻(xiàn)類型：Journal

語種：英文

摘要：Rapid and accurate identification and tracking of lightning clusters from massive lightning detection data are crucial for real-time thunderstorm nowcasting and clima tological analyses of thunderstorm activity. Although density-based clustering algo rithms can identify clusters of arbitrary shapes at fine scales, their performance is often hindered by large data volumes and significant variations in lightning density. To address these challenges, we propose a multi-scale spatiotemporal lightning cluster ing framework, termed CC3D-CSCAP. It consists of two main components. First, the 3-D connected component algorithm (CC3D) performs coarse-scale segmentation by dividing the lightning dataset into spatiotemporally disconnected subsets using 26-connectivity. Then, the cylinder-based scan clustering algorithm with adaptive parameters (CSCAP) is applied to each subset for fine-scale identification of light ning clusters. Since the lightning subset may still contain multiple thunderstorms with varying lightning densities, CSCAP adaptively determines clustering parameters based on the statistical characteristics (time difference and spatial distance) of sub set. Compared with fixed-parameter methods, CC3D-CSCAP identifies more clusters (771,033) while retaining a high percentage of usable lightning strokes (98.988%). The clustering results align well with the theoretical criteria for optimal clustering and are promising for global applications in lightning data analysis, nowcasting, and climatological studies of convective systems.

關(guān)鍵詞：School of Geographic Sciences, Xinyang Normal University, Xinyang, China, School of New Energy and Electrical Engineering, Hubei University, Wuhan, China, China Electric Power Research Institute, Wuhan, China, Spatial Information Technology Application Department, Changjiang River Scientific Research Institute, Wuhan, China

影響因子：2.6

論文鏈接：https://doi.org/10.1371/journal.pone.0333207

（地理科學(xué)學(xué)院 劉媛心 崔向超/初審 閆軍輝/復(fù)審 韓勇/終審）