An examination of WRF 3DVAR radar data assimilation on its capability in retrieving unobserved variables and forecasting precipitation through observing system simulation experiments

An assimilation methodology based on the Weather Research and Forecasting (WRF) model three-dimensional variational data assimilation (3DVAR) and a cloud analysis scheme is described. Simulated data from 25 Weather Surveillance Radar-1988 Doppler (WSR-88D) radars are assimilated, and the potential benefits and limitations of the assimilation are quantitatively evaluated through observing system simulation experiments of a dryline that occurred over the southern Great Plains.

Results indicate that the 3DVAR system is able to analyze certain mesoscale and convective-scale features through the incorporation of radar observations. The assimilation of all possible data (radial velocity and reflectivity factor data) results in the best performance on short-range precipitation forecasting. The wind retrieval by assimilating radial velocities is of primary importance in the 3DVAR framework and the storm case applied, and the use of multiple-Doppler observations improves the retrieval of the tangential wind component.