Abstract. The performance of different urban surface parameterizations in the WRF (Weather Research and Forecasting) in simulating urban boundary layer (UBL) was investigated using extensive measurements during the Texas Air Quality Study 2006 field campaign. The extensive field measurements collected on surface (meteorological, wind profiler, energy balance flux) sites, a research aircraft, and a research vessel characterized 3-dimensional atmospheric boundary layer structures over the Houston-Galveston Bay area, providing a unique opportunity for the evaluation of the physical parameterizations. The model simulations were performed over the Houston metropolitan area for a summertime period (12–17 August) using a bulk urban parameterization in the Noah land surface model (original LSM), a modified LSM, and a single-layer urban canopy model (UCM). The UCM simulation compared quite well with the observations over the Houston urban areas, reducing the systematic model biases in the original LSM simulation by 1–2 °C in near-surface air temperature and by 200–400 m in UBL height, on average. A more realistic turbulent (sensible and latent heat) energy partitioning contributed to the improvements in the UCM simulation. The original LSM significantly overestimated the sensible heat flux (~200 W m⁻²) over the urban areas, resulting in warmer and higher UBL. The modified LSM slightly reduced warm and high biases in near-surface air temperature (0.5–1 °C) and UBL height (~100 m) as a result of the effects of urban vegetation. The relatively strong thermal contrast between the Houston area and the water bodies (Galveston Bay and the Gulf of Mexico) in the LSM simulations enhanced the sea/bay breezes, but the model performance in predicting local wind fields was similar among the simulations in terms of statistical evaluations. These results suggest that a proper surface representation (e.g. urban vegetation, surface morphology) and explicit parameterizations of urban physical processes are required for accurate urban atmospheric numerical modeling.