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Area of Science:

  • Atmospheric Science
  • Meteorology
  • Geophysics

Background:

  • The Geostationary Lightning Mapper (GLM) algorithm struggles with complex lightning flashes, leading to artificial splitting.
  • Existing algorithms require improvements for accurate characterization of extreme lightning events.

Purpose of the Study:

  • To develop and apply a technique for correcting anomalies in GLM lightning flash detection.
  • To produce comprehensive statistics on lightning and thunderstorm characteristics across the Americas using reprocessed GLM data.

Main Methods:

  • Developed a novel technique to correct GLM algorithm anomalies for complex flashes.
  • Applied the correction technique to 2018 GLM data, reclassifying lightning flashes and thunderstorm areas.
  • Generated statistical analyses of flash characteristics, rates, and thunderstorm features.

Main Results:

  • The reprocessed data revealed detailed characteristics of lightning flashes and thunderstorm areas across the Americas.
  • Identified extreme flash events, including the longest (673 km), largest (114,997 km²), and longest-lasting (13.496 s).
  • Documented high flash rate densities (e.g., 157 flashes/km²/year in Lake Maracaibo) and significant thunderstorm activity (e.g., 256 thunder days in Lloró, Colombia).

Conclusions:

  • The developed technique significantly improves the accuracy of GLM lightning data, especially for complex events.
  • Reprocessed GLM data provides valuable insights into lightning and thunderstorm climatology.
  • A reprocessed GLM science product offers substantial value for meteorological research and applications.