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9 December 2020 Airborne radiometric validation of the geostationary lightning mapper using the Fly’s Eye GLM Simulator
Mason G. Quick, Hugh J. Christian, Katrina S. Virts, Richard J. Blakeslee
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Abstract

The Fly’s Eye GLM Simulator (FEGS) is a multi-spectral array of radiometers designed to provide a validation dataset for the geostationary lightning mapper (GLM). The main component of FEGS is a 5  ×  5 grid of radiometers, each with a square 18 deg field of view, that are sensitive to a 10-nm wide spectral band centered on 780 nm to observe a neutral atomic oxygen emission triplet at 777.4 nm. FEGS was flown as a primary payload on the high-altitude NASA ER-2 aircraft as part of the GOES-R validation flight campaign in the spring of 2017. More than 14,000 lightning flashes were recorded over a variety of thunderstorm phenomenologies and geographical regions in the continental United States and Atlantic Ocean. Following the application of a quality control process, a subset of 9160 of these flashes occurring during daylight hours was suitable for analysis. This analysis has provided an independent, lower bound estimate of the average daytime optical flash detection efficiency (FDE) of GLM at 64%. The FDE varied significantly between storms and location ranging between 44% and 88%. An upper estimate of the time-integrated cloud-top radiance detection threshold of GLM during daytime observations is calculated to be 9  μJ  /  m2 sr nm. Dependencies of GLM performance on the temporal width, energy, and cloud-top area of optical pulses, and the background solar radiance conditions are presented and discussed.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Mason G. Quick, Hugh J. Christian, Katrina S. Virts, and Richard J. Blakeslee "Airborne radiometric validation of the geostationary lightning mapper using the Fly’s Eye GLM Simulator," Journal of Applied Remote Sensing 14(4), 044518 (9 December 2020). https://doi.org/10.1117/1.JRS.14.044518
Received: 23 August 2020; Accepted: 16 November 2020; Published: 9 December 2020
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