Analysis of urban surface morphologic effects on diurnal thermal directional anisotropy

Abstract

Abstract Remote thermal radiative observations over metropolitan areas are subject to an angular-dependent variation, known as the directional thermal anisotropy. The 3D urban surface morphology is one key factor in determining the magnitude and temporal variation of thermal anisotropy. This study uses 3D building data and the Town Energy Balance model (TEB) to explore the impact of morphological variability on diurnal anisotropy patterns, and quantifies errors introduced by a simplification of urban morphology as an array of evenly distributed uniform cubes. Results from the comparison of two representative urban districts in Brooklyn and midtown Manhattan, New York City reveal distinct diurnal anisotropy patterns. Daytime anisotropy varies more time-sensitively over the compact high-rise district of Manhattan, although the maximum effective anisotropy (the maximum contrast of directional anisotropy) is smaller than Brooklyn, which is related to a reduced contrast among wall temperatures. A stronger angular effect at night is found as the aspect-ratio increases. The anisotropy is further simulated at the Moderate Resolution Imaging Spectroradiometer (MODIS) overpass time and sensor-surface relative geometry over these two morphologic samples. The sensitivity test unravels that the effective anisotropy monotonically increases with a greater aspect ratio for MODIS nighttime overpasses, while the daytime pattern is more complex with a single- or double-peak distribution depending on the solar angle (or time of day). Finally, the variation of building height and size is important in determining the anisotropy from comparing simulations of a realistic 3D building model and a simplified urban morphology as cube array. The morphological simplification can lead to a higher discrepancy in these cases with a high aspect ratio or small sky view factor for both daytime and nighttime. The proposed 3D-computer-graphics approach is computationally affordable for the seen surface estimation and can be applied to IFOVs across a relatively large urban area. Its flexibility in integrating various levels of 3D urban surface complexity makes it a promising tool for correcting the urban thermal anisotropy from satellite observations in the future.