The impact of surface data assimilation on the predictability of atmospheric boundary layer and near surface conditions over complex terrain

The impact of surface data assimilation on the predictability of atmospheric boundary layer and near surface conditions over complex terrain is examined. Our previous results have indicated the potentials of assimilating surface observations in improving the analysis and forecasts over complex terrain. In this study, we performed data assimilation and numerical prediction experiments for one-month period during September to October of 2011 with an advanced research version of Weather Research and Forecasting (WRF) model and its 3-dimensional variational data assimilation (3DVAR) systems. The hourly surface mesonet observations are assimilated into WRF model over the Intermountain West region with horizontal resolution at 10 km, 3.3km and 1.1km. The impact of surface observations on the predictability of atmospheric boundary layer and near surface conditions is assessed. Results are compared for both weak- and strong- synoptic forcing scenarios. The influence of model resolution, background error correlation length-scale, and representative errors on the data assimilation and predictability over complex terrain is discussed. In addition, recent progress and development in ensemble Kalman filter (EnKF) data assimilation will be presented.