Analysis of Convective Mode in Severe Thunderstorm Events across Eastern Parts of the Central and Southern Great Plains

Forecasting convective mode continues to present a considerable challenge, as its evolution can be associated with variability in anticipated severe thunderstorm hazards and their spatiotemporal sustainability.  This is particularly the case across portions of the Great Plains, where severe weather events often feature a mix of transitioning convective modes.  In this study, a regional analysis of significantly severe and tornadic thunderstorm events was conducted, with an area of focus centered on the NOAA NWS Tulsa, Oklahoma, County Warning Area (CWA) and surrounding locations.  Specifically, the domain considered for analysis was defined by latitude and longitude lines 32.5 and 38 degrees North, and 93 and 97 degrees West, respectively. Thunderstorm events in which significantly severe hail (≥2-in. diameter), significantly severe wind (≥65-kt thunderstorm gusts), or at least one tornado occurred were analyzed from March 1 through June 15 during the years 2003 to 2013 for convective mode evolutions, yielding roughly 200 analyzed events over the time period of interest. Convective mode classifications were assigned based on archived radar data: 1) discrete convection initiating west of the domain moving into the domain, 2) discrete convection developing within the domain, and 3) discrete convection west of the domain evolving into a linear mode before affecting the domain. Events were also analyzed using archived surface observations including Oklahoma Mesonet observations to identify and classify boundaries influencing the development and evolution of the severe convection. Using proximity soundings and reanalysis data, mean and normalized-anomaly plots for various fields are presented, as well as other datasets including the Storm Prediction Center Observed Sounding Climatology (http://www.spc.noaa.gov/exper/soundingclimo/).  These analyses are intended to illustrate differences amongst various meteorological parameters associated with the different regimes. Results could assist forecasters in anticipating convective mode evolutions in high-impact severe weather events based upon the development of a conceptual framework that can be applied to observational and numerical weather prediction datasets in assessing severe-weather potential.