In-situ surface-layer observations represent a robust data source that are not adequately utilized in NWP applications. If properly assimilated, data from existing mesonets could improve initial conditions near the earth's surface, leading to the possibility of improved short-range forecasts of slope flows, sea breezes, convective initiation, and other PBL circulations.

The efficacy of surface-observation assimilation strongly depends on the error-growth characteristics of parameterized PBL states in mesoscale models. Summer-season error growth is first described by examining PBL forecast error and covariance structure over the southern great plains, using 2002 real-time WRF forecasts. With an understanding of background error-growth properties, a perfect 1-D PBL model is used to determine the ability of the Ensemble Kalman Filter (EnKF) approach to assimilate near-surface observations and spread their effects vertically through the layer in the atmosphere that is coupled to the observations. Results are positive for certain regimes, but little or no improvement is observed in others. Shortcomings can be explained by recalling the analysis of the WRF forecasts. Those results will be presented, along with a preliminary investigation on addressing model error in parameterized PBL forecasts.