Matthew Van Den Broeke
Date of this Version
Severe weather events in the United States including tornadoes, hail, and wind are often produced by supercell thunderstorms. These storms are characterized by complex hydrometeor distributions which can be influenced by environmental distributions of wind and moisture. Since the Weather Surveillance Radar-1988 Doppler (WSR-88D) network was fully upgraded to dual-polarimetric capabilities in 2013, dominant hydrometeor species such as hail have been inferable using fuzzy logic. In this study, time series of areal extent of the inferred hail signature at base scan level have been estimated for 145 supercell storms, including both tornadic and non-tornadic cases, across a variety of environments from February 2012-December 2014. Proximity soundings were gathered for environments representative of the supercells (e.g., on the same side of mesoscale boundaries, in a region representative of storm-relative inflow) using archived Rapid Update Cycle (RUC) and Rapid Refresh (RAP) model output from the National Operational Model Archive and Distribution System (NOMADS). Model sounding points were within ~80 km and the midpoint of the analysis period in order to spatiotemporally represent environments during the period in which storms were analyzed. Previous modeling and observational studies have shown that thermodynamic, moisture, and shear parameters influence the mean areal extent of hail at the base scan level and the temporal variability of inferred hail areal extent (HAE). Significant relationships were determined in this study between mean HAE/variability and several environmental parameters. Hail polarimetric radar signatures were also compared across environments; results showed that certain environments produce distinctive mean hail areal extent and hail variability. Correlations between HAE and environment variables are generally higher when the storm has a mean altitude greater than 1 km. An increase in some thermodynamic parameters is observed to produce an increase in mean HAE, while an increase in shear produces an increase in hail variability. Predictive equations for HAE and hail variability are also developed from the analyzed environmental variables.
Advisor: Matthew Van Den Broeke