Impact of UAS Data on Supercell Evolution in an Observing System Simulation Experiment

Unmanned Aircraft Systems (UAS) have the potential to characterize the thermodynamic and kinematic state in the vicinity of supercell thunderstorms.  Based on previous ensemble sensitivity analysis, knowledge of this state is hypothesized to be particularly useful for improving the accuracy of storm-scale numerical weather prediction in three distinct regions of a supercell: the inflow environment, forward flank gust front (FFGF) and rear flank gust front (RFGF).  The impact of UAS observations on simulated supercells is being assessed through Observing System Simulation Experiments (OSSEs), wherein synthetic observations are “collected” by a simulated UAS within a high-resolution idealized simulation (termed the nature run) that represents the true atmospheric state.  The nature run is developed using CM1 initialized in an environment consisting of realistic boundary layer convection.  The simulation also includes radiative parameterization, thermal and moisture surface fluxes, and utilizes a semi-slip surface.  The synthetic observations from the nature run are then assimilated into coarse-resolution idealized WRF simulations intended to represent the typical operational model.  Comparison of the nature run predictions (truth) to the coarse-run predictions with and without assimilation of new data will be discussed to evaluate the impact of the new data on the accuracy of forecast storm evolution and their potential impact on warn-on-forecast efforts.