A Technique for Assimilating Surface Data to Improve the Accuracy of Atmospheric Boundary Layer Simulations: A 1-D Model Study

amatic reductions in ABL modeling errors. Large prediction errors in atmospheric boundary layer (ABL) simulations can be caused by inaccuracies in the specification of surface characteristics, assumptions and simplifications made in boundary layer formulations, and other deficiencies in modeling aspects. To reduce these types of errors, a technique to continuously assimilate surface thermodynamic fields for the surface layer of the ABL combined with proper adjustment of ground temperature is developed. A one-dimensional soil-vegetation-boundary-layer model was used to study the improvements that result from applying this technique for ABL simulations. We performed several 1-D model simulations to delineate how various factors affect the evolution of the ABL. These factors included uncertainties in the soil moisture specification, different mixed-layer formulations, advection errors, and different methods for diagnosing mixed-layer depths. From this study we found that (1) it is feasible to perform continuous data assimilation for the surface and mixed layers along with the proper adjustment of ground temperature, and (2) application of this new technique can lead to dr