The assimilation of Doppler radar reflectivity and velocity data into mesoscale numerical weather models has been shown to improve forecasts by decreasing spin-up time for precipitation fields and enhancing the resolution of boundary layer processes. The migration of National Weather Service (NWS) WSR-88D radars from 1km range gate and 1 degree azimuthal resolution to “super resolution” 250m range gate and 0.5 degree azimuthal resolution data distribution which began in spring 2008 is expected to improve warning lead times by detecting small scale features sooner with increased reliability; however, current operational high resolution model domains utilize grid spacing several times larger than the legacy 1km radar data resolution, and therefore the added resolution of radar data can not be fully exploited. The assimilation of “super resolution” reflectivity and velocity data into high resolution numerical weather model forecasts where grid spacing is comparable to the radar data resolution is investigated here to determine the impact of the improved data resolution on model predictions. As the increase in computational power and availability has made increasingly high resolution real-time model simulations possible, the ability to verify model output has become increasingly difficult as well. The role of “super resolution” observations in model verification is discussed.