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Description:
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This study developed a parameterization method to investigate the impacts of inhomogeneous land surfaces on mesoscale model simulations using a high -resolution 1 -d PBL model . Then , the 1 -d PBL model was used to investigate the inhomogeneity -caused model errors in applying the GOES satellite skin temperature assimilation technique into the MM5 over the Houston metropolitan area (HOU ) . In order to investigate the surface inhomogeneity impacts on the surface fluxes and PBL variables over HOU , homo - and inhomogeneous 1 -d PBL model simulations were performed over HOU and compared to each other . The 1 -d PBL model was constructed so that the surface inhomogeneities were able to be represented within model grid elements using a methodology similar to Avissar and Pielke (1989 ) . The surface inhomogeneities over HOU were defined using 30 -m resolution land cover data produced by Global Environment Management (GEM ) , Inc . The inhomogeneity parameterization method developed in the 1 -d model was applied to a standard MM5 simulation to test the applicability of the parameterization to 3 -d mesoscale model simulations . From the 1 -d simulations it was inferred that the surface inhomogeneities would enhance the sensible heat flux by about 36 % and reduce the latent heat flux by about 25 % , thereby inducing the warmer (0 .7 % ) and drier ( -1 .0 % ) PBL and the colder and moister PBL top induced by greater turbulent diffusivities . The 3 -d application of the inhomogeneity parameterization indicated consistent results with the 1 -d in general , with additional effects of advection and differential local circulation . The original GOES simulation was warmer compared to observations over HOU than over surrounding areas . The satellite data assimilation itself would lead to a warm bias due to erroneous estimation of gridpoint -mean skin temperature by the satellite , but 1 -d simulations indicate that the impact of this error should be much weaker than what was observed . It seems that , unless the already existing warm and dry bias of the MM5 is corrected , the inhomogeneity parameterization in the MM5 would adversely affect the MM5 performance . Therefore , consideration of the surface inhomogeneities in the urban area needs to be confined to the GOES skin temperature retrieval errors at the moment . |