Abstract

The sky view factor (SVF) is an important radiometric parameter for assessing the canopy energy budget of urban areas. There are several methods to determine the SVF observationally. The most common is taking a photo with a digital camera equipped with a fish-eye lens and then converting ratio of sky area to canopy area into SVF. However, most urban canopy models use this variable as derived from idealized canopy geometry. To evaluate the effect of inputting observed SVFs in numerical models, we evaluated a mesoscale model's performance in reproducing surface wind and surface temperature when subjected to different ways of SVF prescription. The studied area was the Metropolitan Area of Sao Paulo (MASP) in Brazil. Observed SVFs were obtained for 37 sites scattered all over the MASP. Three simulations, A, B, and C, with different SVF and aspect-ratio prescriptions, were performed to analyze the effect of SVF on the urban canopy parameterization: Simulation A (standard) used the original formulation of the Town Energy Budget (TEB) model, computing the SVFs from the aspect-ratios; Simulation B used the observed SVFs, but keeps aspect-ratios as original; and Simulation C used the aspect-ratios computed from observed SVFs. The results show that in general inputting observed SVFs improves the model capability of reproducing temperature at surface level. The comparison of model outputs with data of regular meteorological stations shows that the inclusion of observed values of SVFs enhances model performance, reducing the RMSE index by up to 3 degrees C. In this case, the model is able to better reproduce the expected effects in the wind field, and consequently the temperature advection, of the urban boundary layer to a large urban area. The result of Simulation C shows that the surface wind and temperature intensity for all urban types is higher than those of Simulation A, because of the lower values of the aspect ratio. The urban type with high density of tall buildings increase up to 1 m s(-1) in the wind speed, and approximately 1 degrees C in temperature, showing the importance of a better representation of the urban structure and the SVF database improvement.