Sarah Bang (ST11) co-authored a paper with lead author Benjamin Scarino (SSAI) and co-authors Kyle Itterly (SSAI), Kristopher Bedka (NASA LaRC), Cameron R. Homeyer (OU), John Allen (CMTU), and Daniel Cecil (ST11). The paper explores using a combination of satellite-observed cloud patterns with reanalysis environmental parameters, linked to Next Generation Weather Radar- (NEXRAD-) estimated Maximum Expected Size of Hail (MESH) using a deep neural network (DNN) to estimate the potentially severe hail likelihood of observed storm cells. These estimates are made where satellites observe cold clouds, indicative of convection, located in favorable storm environments. The paper discusses an approach that can be used to estimate climatological hailstorm frequency and risk throughout the historical satellite data record. These hail classifications are aggregated across an 11-year GOES-12/image database to derive a hail frequency and severity climatology.
Read the paper at: https://journals.ametsoc.org/view/journals/aies/aop/AIES-D-22-0042.1/AIES-D-22-0042.1.xml.
