Estimating 3-D atmospheric boundary layer structure with surface data assimilation using ensemble Kalman filter

The use of single level surface observations is a challenging problem in the numerical weather prediction. Recent studies (e.g., Hacker and Snyder 2005; Hacker and Rostkier-Edelstein 2007) proved the potential of the ensemble Kalman filter in assimilating surface observations. With a 1-D column model, it has been demonstrated that the ensemble Kalman filter is able to generate reliable vertical structure of planetary boundary layer (PBL).

In this study, we use a weather research and forecasting (WRF) model and an ensemble Kalman filter data assimilation system from NCAR Data Assimilation Research Testbed (DART) to further evaluate the performance of the ensemble Kalman filter in surface data assimilation. A 3D scenario is studied to examine the potential impact of surface observations on accurate representation of both PBL vertical structures and horizontal mesoscale features. The effectiveness of the ensemble Kalman filter in assimilating surface observation in both flat terrain and complex terrain is evaluated and compared. Sensitivity of data assimilation results to various surface variables and ensemble size is also investigated.Preliminary results and discussion will be presented.