Assimilation of GOES-R ABI Satellite and WSR-88D Radar Observations During a Convection-Resolving OSSE

For this study, data assimilation tests employing different combinations of simulated satellite and radar observations were performed for a severe weather event that occurred across the central U.S. during June 2005. The simulated data include GOES-R ABI brightness temperatures and WSR-88D Doppler radar reflectivity and radial velocity. Assimilating these datasets into convection permitting numerical weather prediction (NWP) models poses many challenges due to observation uncertainties and correlations to model state variables. This study used an Observing System Simulation Experiment (OSSE) framework that provides a useful means to investigate the impact of these observations in a controlled manner. A 2-km model simulation initialized with a blend of NAM and GMAO-GEOS analysis data was used to create synthetic observations. Real-data experiments assimilated those synthetic observations into a 4-km resolution grid using the Data Assimilation Research Testbed (DART) ensemble Kalman filter (EnKF) system combined with the Weather Research Forecast (WRF) model. The use of a simple bias correction and separate horizontal localizations for the clear and cloudy brightness temperatures are shown to improve the water vapor and cloud analyses. Results documenting the relative benefit of each observation type at convection-resolving scales will be shown, with the assimilation of radar improving the storm structure while the satellite observations increase the vertical shear and improve the moisture profiles and cloud extent. The most accurate storm forecast results from assimilating both observation types together.