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An Algorithmic Approach for Detecting Bolides with the Geostationary Lightning Mapper.

Clemens M Rumpf1,2, Randolph S Longenbaugh3, Christopher E Henze4

  • 1NASA Advanced Supercomputing Division, NASA Ames Research Center, Moffett Field, CA 94035, USA. clemens.rumpf@nasa.gov.

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Summary

A new algorithm using Geostationary Lightning Mapper (GLM) data automatically detects bolides, enhancing the study of asteroids and meteors. This method provides continuous, semi-global coverage for discovering and tracking atmospheric bolide events.

Keywords:
CubaGLMGOESGeostationary Lightning Mapperasteroidbolidelight curvelightningmeteor

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Area of Science:

  • Atmospheric science
  • Planetary science
  • Remote sensing

Background:

  • The Geostationary Lightning Mapper (GLM) on GOES 16/17 satellites offers unique semi-global, continuous coverage for atmospheric event detection.
  • GLM data is publicly available, presenting an opportunity for novel scientific applications beyond lightning detection.

Purpose of the Study:

  • To develop and validate an automatic algorithm for extracting bolide signatures from GLM data.
  • To leverage GLM's capabilities for enhanced discovery and study of bolides in Earth's atmosphere.

Main Methods:

  • Development of six specialized filters to identify unique bolide characteristics.
  • Aggregation of filters into an automated algorithm to process GLM level 2 data.
  • Performance assessment using over 144,000 GLM files, equating to 34 days of data.

Main Results:

  • The algorithm successfully identified 2252 files with bolide-similar signatures, a 1.44% pass rate.
  • Demonstrated effectiveness in discovering confirmed and new bolide events, including seven likely bolides in November 2018.
  • Successfully generated a light curve and ground track for the Feb 1st, 2019 Cuban meteor within 8.5 hours.

Conclusions:

  • The automated GLM bolide extraction algorithm significantly enhances the study of asteroids and meteors.
  • GLM data provides a valuable, continuous, and semi-global dataset for bolide research.
  • Future applications can yield substantial new measurements of atmospheric bolides.