Investigation of surface inhomogeneity and estimation of the GOES skin temperature assimilation errors of the MM5 implied by the inhomogeneity over Houston metropolitan area

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.