2019
DOI: 10.1175/waf-d-19-0082.1
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Geostationary Lightning Mapper Flash Characteristics of Electrified Snowfall Events

Abstract: This study examines characteristics of lightning in snowfall events (i.e., thundersnow, TSSN) from the perspective of the Geostationary Lightning Mapper (GLM) and the National Environmental Satellite Data and Information Service (NESDIS) merged Snowfall Rate (mSFR) product. A thundersnow detection algorithm (TDA) was derived from the GLM and mSFR that resulted in a probability of detection (POD) of 66.7% when compared to the aviation routine weather report (METAR) reports of TSSN. However, using the TDA an add… Show more

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Cited by 12 publications
(22 citation statements)
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“…Thus, there is potential utility of this lightning nowcasting technique to the winter weather environment. However, developing applications requires a deeper look into when the use of these signatures is needed, given that each depolarization streak observed in this study did not produce lightning but was associated with cloud electrification, and in an initial look at the Harkema et al (2019) data set, not all lightning flashes in winter storms were accompanied by depolarization streaks in Z DR .…”
Section: Application Of Lip and Radar Depolarization In Z Dr For Lightning Safetymentioning
confidence: 96%
“…Thus, there is potential utility of this lightning nowcasting technique to the winter weather environment. However, developing applications requires a deeper look into when the use of these signatures is needed, given that each depolarization streak observed in this study did not produce lightning but was associated with cloud electrification, and in an initial look at the Harkema et al (2019) data set, not all lightning flashes in winter storms were accompanied by depolarization streaks in Z DR .…”
Section: Application Of Lip and Radar Depolarization In Z Dr For Lightning Safetymentioning
confidence: 96%
“…To document the incremental effects of increasing the time coincidence window, we started the time window of the DE and FAR analysis at the value used for the previous work of ± 1 s (2 s total) (Harkema et al, 2019;Bateman & Mach, 2020). We then repeated the DE and FAR analysis for the same dataset, increasing the time window for each run.…”
Section: Accepted Articlementioning
confidence: 99%
“…In that study, we used a time coincidence criteria of ± 1 s and a distance criteria of 50 km. Another previous study (Harkema et al, 2019) used this same time and distance criteria when comparing GLM data to NLDN.…”
Section: Introductionmentioning
confidence: 99%
“…In this section we will demonstrate how the geolocated video imagery of lightning can be used to derive accurate flash statistics. Flash area and length have been shown to be important to separate convective and stratiform regions individual in storms (e.g., Bruning & MacGorman, 2013; Calhoun et al., 2013; T. J. Lang et al., 2015; Schultz et al., 2015) and providing seasonal and climatological characteristics for lightning occurrence (e.g., Fuchs et al., 2016; Harkema et al., 2019; López et al., 2017; Rudlosky et al., 2019; Yoshida et al., 2019). In total, 7,105 unique METEOR frames with lightning were assigned to 289 flashes observed by GLM during 05:43:48–05:46:03 UTC (when METEOR was observing lightning).…”
Section: Discussionmentioning
confidence: 99%
“…This is not surprising because LMA flash area computations are not affected by light scattering; thus, LMA flash sizes will be smaller than optically detected flashes. Comparing to GLM observations of flash area and length places most of the flash lengths observed from the METEOR‐derived technique between the 90th and 99th percentile in observed flash area and radii (Harkema et al., 2019; Rudlosky et al., 2019). However, as noted earlier, the median difference in flash area between METEOR and GLM was 266 km 2 , and the matched GLM flashes had a median flash length of 51 km.…”
Section: Discussionmentioning
confidence: 99%