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A lightning cluster identification method considering multi-scale spatiotemporal neighborhood relationships.

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Area of Science:

  • Atmospheric Science
  • Geophysics
  • Computer Science

Background:

  • Accurate lightning cluster identification is vital for thunderstorm nowcasting and climatology.
  • Existing density-based clustering methods struggle with large datasets and variable lightning density.

Purpose of the Study:

  • To develop a multi-scale spatiotemporal framework for improved lightning cluster identification.
  • To address limitations of current algorithms in handling massive lightning data and density variations.

Main Methods:

  • Proposed a novel framework, CC3D-CSCAP, combining a 3-D connected component algorithm (CC3D) for coarse segmentation and a cylinder-based scan clustering algorithm with adaptive parameters (CSCAP) for fine-scale identification.
  • CC3D segments data into spatiotemporally disconnected subsets.
  • CSCAP adaptively determines parameters based on subset characteristics for robust clustering.

Main Results:

  • CC3D-CSCAP identified 771,033 lightning clusters, significantly more than fixed-parameter methods.
  • Maintained a high percentage (98.988%) of usable lightning strokes.
  • Clustering results align with optimal clustering criteria.

Conclusions:

  • The CC3D-CSCAP framework offers a robust solution for analyzing massive lightning detection data.
  • Demonstrates significant improvements in identifying and tracking lightning clusters.
  • Shows promise for global applications in lightning data analysis, nowcasting, and climatology of convective systems.