Assimilating Surface Data to Improve the Accuracy of Atmospheric Boundary Layer Simulations

Abstract

Large errors in atmospheric boundary layer (ABL) simulations can be caused by inaccuracies in the specification of surface characteristics in addition to assumptions and simplifications made in boundary layer formulations or other model deficiencies. For certain applications, such as air quality studies, these errors can have significant effects. To reduce such errors, a continuous surface data assimilation technique is developed. In this technique, surface-layer temperature and water vapor mixing ratio are directly assimilated by using the analyzed surface data. Then, the difference between the observations and model results is used to calculate adjustments to the surface fluxes of sensible and latent heat. These adjustments are then used to calculate a new estimate of the ground temperature, thereby affecting the simulated surface fluxes on the subsequent time step. This indirect data assimilation is applied simultaneously with the direct assimilation of surface data in the model's lowest layer, thereby maintaining greater consistency between the ground temperature and the surface-layer mass-field variables. A one-dimensional model was used to study the improvements that result from applying this technique for ABL simulations in two cases. It was found that application of the new technique led to significant reductions in ABL modeling errors.