Thinning Strategies for Combined Radar and Satellite Data Sets to Increase Storm-Scale Data Assimilation Efficiency

Assimilating radar reflectivity and radial velocity along with satellite cloud water path (CWP) retrievals has shown promise at improving forecasts of supercell thunderstorms and the near storm environment. One of the challenges limiting additional improvements from assimilating satellite observations is the nature in which both data sets are combined. Currently, no data thinning occurs to remove observations where radar and satellite data may provide conflicting information to the model, potentially leading to degraded results. This research will address this issue by creating a product in which radar reflectivity and satellite observations are placed on the same grid. Using this product, it becomes possible to remove satellite observations where high reflectivity values already exist and also remove clear-air reflectivity where the satellite indicates cloud-free conditions. Various combinations of clear-air reflectivity and satellite retrievals will be tested in non-precipitating cloudy areas. Testing of various data sets will employ the NSSL Experimental Warn-on-Forecast System ensemble adjustment Kalman filter data assimilation system (NEWSe). The goal is to determine the combination of radar and satellite observations that provides the highest forecast skill while using the fewest number of observations. By only assimilating observations that provide value added information to the model, the assimilation process itself speeds up, which is of critical importance for rapid cycling, real-time applications.