Impacts of Physics Parameterization and Data Assimilation on Synoptic Feature Modeling in Severe Weather Outbreaks

The use of numerical weather prediction (NWP) has brought significant improvements to the forecasting of severe weather outbreaks; however, determination of severe weather outbreak mode (in particular tornadic and nontornadic outbreaks) continues to be a challenge.  It is useful to examine synoptic-scale atmospheric patterns to differentiate between patterns associated with tornadic and nontornadic outbreaks.  This research examines the effect of microphysics and planetary boundary layer (PBL) physics schemes on the modeling of such synoptic composite fields.  WRF-ARW simulations of forty United States tornadic and nontornadic forecasts are generated.  Each case is modeled with 15 different combinations of physics parameterizations:  5 microphysics and 3 planetary boundary layer (PBL), all of which were designed to perform well in convective weather situations.  Standard synoptic parameters are extracted from the WRF-ARW simulations and a k-means cluster analysis is performed on tornadic and nontornadic outbreak datasets to generate valid, synoptically distinct clusters representing atmospheric conditions found in each type of outbreak.  Variations among the synoptic features in each cluster are examined across physics parameterization runs.