Date of Award
Master of Science (MS)
While severe convective weather might be the focus of lightning data, studies regarding the use of these data for winter weather purposes are sparse. Next-generation satellite sensors, such as the Geostationary Lightning Mapper (GLM), provide new capabilities to gain a better understanding of the microphysical processes within heavy-snowfall. The advent of GLM provides a new opportunity to study how lightning is related to snowfall from a geostationary satellite perspective. Observed overlap between observations from GLM and National Environmental Satellite Data and Information Services (NESDIS) merged Snowfall Rate (mSFR) product indicate the existence of thundersnow (TSSN) and offers valuable insight to the rapidly changing environment in and around heavy-snowfall. A thundersnow detection algorithm was developed to objectively identify this phenomena and statistically significant (P<0.05) differences were observed for GLM flash area and flash energy; where GLM flashes that did not correspond to National Lightning Detection Network (NLDN) data were weaker in energy output and smaller in size compared to those that had at least one NLDN correspondence. Nearly 14% of TSSN flashes observed by GLM corresponded with NLDN data near a tall human-made structure. Geographic locations that experience TSSN could expect to receive on average 9.81-in of accumulating snowfall. TSSN flashes occurred in snowfall rates less than 1.00-in hr-1 and were more likely to be associated with snow-to-liquid ratios between 8:1 and 9:1. With continuous high spatial and temporal resolution lighting observations, GLM improved the identification and characterization of TSSN events and situational awareness of significant snowfall events.
Harkema, Sebastian S., "Improving situational awareness of heavy-snowfall using the geostationary lightning mapper" (2019). Theses. 276.