An Algorithmic Approach for Detecting Bolides with the Geostationary Lightning Mapper

Sensors
Clemens M RumpfDonovan L Mathias

Abstract

The Geostationary Lightning Mapper (GLM) instrument onboard the GOES 16 and 17 satellites can be used to detect bolides in the atmosphere. This capacity is unique because GLM provides semi-global, continuous coverage and releases its measurements publicly. Here, six filters are developed that are aggregated into an automatic algorithm to extract bolide signatures from the GLM level 2 data product. The filters exploit unique bolide characteristics to distinguish bolide signatures from lightning and other noise. Typical lightning and bolide signatures are introduced and the filter functions are presented. The filter performance is assessed on 144845 GLM L2 files (equivalent to 34 days-worth of data) and the algorithm selected 2252 filtered files (corresponding to a pass rate of 1.44%) with bolide-similar signatures. The challenge of identifying frequent but small, decimeter-sized bolide signatures is discussed as GLM reaches its resolution limit for these meteors. The effectiveness of the algorithm is demonstrated by its ability to extract confirmed and new bolide discoveries. We provide discovery numbers for November 2018 when seven likely bolides were discovered of which four are confirmed by secondary observations. The Cuban m...Continue Reading

References

Mar 28, 2009·Nature·P JenniskensS P Worden
Jun 9, 2012·Chemical Society Reviews·John M C Plane
Oct 23, 2013·Proceedings of the National Academy of Sciences of the United States of America·Steven D MillerYoo-Jeong Noh
Nov 10, 2013·Science·Olga P PopovaUNKNOWN Chelyabinsk Airburst Consortium

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Citations

May 23, 2020·Journal of Applied Remote Sensing·Michael Peterson
Jun 5, 2020·Journal of Geophysical Research. Atmospheres : JGR·Michael PetersonDaile Zhang
Dec 7, 2019·Journal of Geophysical Research. Atmospheres : JGR·Michael Peterson

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Software Mentioned

GLM
GOES Geostationary Lightning Mapper

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