Premium
GLM and ABI Characteristics of Severe and Convective Storms
Author(s) -
Thiel Kevin C.,
Calhoun Kristin M.,
Reinhart Anthony E.,
MacGorman Donald R.
Publication year - 2020
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1029/2020jd032858
Subject(s) - meteorology , environmental science , thunderstorm , geostationary orbit , convective storm detection , brightness temperature , storm , severe weather , cloud top , lightning (connector) , flash (photography) , cloud computing , climatology , computer science , geography , geology , telecommunications , physics , power (physics) , satellite , quantum mechanics , astronomy , microwave , optics , operating system
The recent deployment of the Geostationary Lightning Mapper (GLM) on board GOES‐16 and GOES‐17 provides a new perspective of total lightning production for the severe convective storms research and operational communities. While the GLM has met its performance targets, further understanding flash characteristics and the physical limitations of the GLM are required to increase the applicability of the data. Derived cloud‐top height and infrared (IR) brightness temperature products from the Advanced Baseline Imager (ABI) are used to assess data quality and characteristics from gridded GLM imagery across 7 weeks of active severe weather: 13 April through 31 May 2019. Areas with cloud tops colder than 240 K typically produced lightning, though this becomes less certain near the edge of the field of view due to algorithm limitations. Increasing flash rates were observed to correlate with decreasing flash areas, increasing cloud‐top heights, and colder cloud‐top temperatures. However, flash rates and size were more strongly tied to convective intensity and proximity to convective hazards at the surface due to the ability to delineate between convective and stratiform precipitation. Results show that merging ABI and GLM data sets could add value to both machine learning and statistical‐based algorithms and also forecast applications with each providing unique details, although parameters such as GOES‐16 viewing angle should be considered. Lastly, two case studies (24 and 27 May 2019) are used to help interpret the results from the 7‐week sampling period and identify GLM and ABI trends related to thunderstorm evolution.