To evaluate the efficacy of the bias estimation and correction, OSSEs are constructed for the conventional observation network including radiosondes, aircraft observations, atmospheric motion vectors, and surface observations. Three experiments are created by adding different kinds of bias to temperature from synthetic METAR observations: (1) a spatially invariant bias, (2) a spatially changing bias proportional to height differences between the model and the observations, and (3) bias that is proportional to the temperature. The target region characterized by complex terrain is the western U.S. on a domain with 30-km grid spacing. Observations are assimilated every 3 hours using an 80-member ensemble during September 2012. Results demonstrate that the approach is able to estimate and correct the bias when it is spatially invariant (1); errors in estimating the known parameters are small, but similar to the systematic errors in innovations for unbiased observations. More complex bias structure in experiments (2) and (3) are more difficult to estimate, but still possible. The results demonstrate that parameter estimation for surface observation bias may be a viable approach for real-data problems.