An Algorithmic Approach for Detecting Bolides with the Geostationary Lightning Mapper

Autor: Joseph C. Chavez, Randolph S. Longenbaugh, Christopher E. Henze, Clemens Rumpf, Donovan Mathias
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Zdroj: Sensors
Volume 19
Issue 5
Sensors, Vol 19, Iss 5, p 1008 (2019)
Sensors (Basel, Switzerland)
ISSN: 1424-8220
DOI: 10.3390/s19051008
Popis: 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 meteor on Feb 1st 2019 serves as an additional example to demonstrate the algorithms capability and the first light curve as well as correct ground track was available within 8.5 hours based on GLM data for this event. The combination of the automatic bolide extraction algorithm with GLM can provide a wealth of new measurements of bolides in Earth&rsquo
s atmosphere to enhance the study of asteroids and meteors.
Databáze: OpenAIRE
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