Thunderstorm Cloud-Type Classification from Space-Based Lightning Imagers
[1];
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- National Oceanic and Atmospheric Administration (NOAA), College Park, MD (United States). National Environmental Satellite, Data, and Information Service (NESDIS), Center for Satellite Applications and Research (STAR), Satellite Climate Studies Branch (SCSB)
- Univ. of Maryland, College Park, MD (United States). Cooperative Inst. for Satellite Earth System Studies (CISESS)
The organization and structure of thunderstorms determines the extent and severity of their hazards to the general public and their consequences for the Earth system. Distinguishing vigorous convective regions that produce heavy rain and hail from adjacent regions of stratiform clouds or overhanging anvil clouds that produce light to no rainfall is valuable in operations and physical research. Cloud-type algorithms that partition convection from stratiform regions have been developed for space-based radar, passive microwave, and now Geostationary Operational Environmental Satellites (GOES) Advanced Baseline Imager (ABI) multispectral products. However, there are limitations for each of these products including temporal availability, spatial coverage, and the degree to which they based on cloud microphysics. We report we have developed a cloud-type algorithm for GOES Geostationary Lightning Mapper (GLM) observations that identifies convective/nonconvective regions in thunderstorms based on signatures of interactions with nonconvective charge structures in the lightning flash data. The GLM sensor permits a rapid (20 s) update cycle over the combined GOES-16–GOES-17 domain across all hours of the day. Storm regions that do not produce lightning will not be classified by our algorithm, however. The GLM cloud-type product is intended to provide situational awareness of electrified nonconvective clouds and to complement other cloud-type retrievals by providing a contemporary assessment tied to lightning physics. We propose that a future combined ABI–GLM cloud-type algorithm would be a valuable product that could draw from the strengths of each instrument and approach.
- Research Organization:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); National Aeronautics and Space Administration (NASA)
- Grant/Contract Number:
- 89233218CNA000001; NNX17AH63G
- OSTI ID:
- 1688749
- Report Number(s):
- LA-UR-19-30048
- Journal Information:
- Monthly Weather Review, Vol. 148, Issue 5; ISSN 0027-0644
- Publisher:
- American Meteorological SocietyCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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