Abstract

Surface temperature is a key variable in boundary-layer meteorology and is typically acquired by remote observation of emitted thermal radiation. However, the three-dimensional structure of cities complicates matters: uneven solar heating of urban facets produces an “effective anisotropy” of surface thermal emission at the neighbourhood scale. Remotely-sensed urban surface temperature varies with sensor view angle as a consequence. The authors combine a microscale urban surface temperature model with a thermal remote sensing model to predict the effective anisotropy of simplified neighbourhood configurations. The former model provides detailed surface temperature distributions for a range of “urban” forms, and the remote sensing model computes aggregate temperatures for multiple view angles. The combined model’s ability to reproduce observed anisotropy is evaluated against measurements from a neighbourhood in Vancouver, Canada. As in previous modeling studies, anisotropy is underestimated. Addition of moderate coverages of small (sub-facet scale) structure can account for much of the missing anisotropy. Subsequently, over 1900 sensitivity simulations are performed with the model combination, and the dependence of daytime effective thermal anisotropy on diurnal solar path (i.e., latitude and time of day) and blunt neighbourhood form is assessed. The range of effective anisotropy, as well as the maximum difference from nadir-observed brightness temperature, peak for moderate building-height-to-spacing ratios (H/W), and scale with canyon (between-building) area; dispersed high-rise urban forms generate maximum anisotropy. Maximum anisotropy increases with solar elevation and scales with shortwave irradiance. Moreover, it depends linearly on H/W for H/W < 1.25, with a slope that depends on maximum off-nadir sensor angle. Decreasing minimum brightness temperature is primarily responsible for this linear growth of maximum anisotropy. These results allow first order estimation of the minimum effective anisotropy magnitude of urban neighbourhoods as a function of building-height-to-spacing ratio, building plan area density, and shortwave irradiance. Finally, four “local climate zones” are simulated at two latitudes. Removal of neighbourhood street orientation regularity for these zones decreases maximum anisotropy by 3%–31%. Furthermore, thermal and radiative material properties are a weaker predictor of anisotropy than neighbourhood morphology. This study is the first systematic evaluation of effective anisotropy magnitude and causation for urban landscapes.

Highlights

  • Surface-atmosphere exchanges of heat and water are core drivers of the fair-weather meteorology and climatology of cities

  • Numerous factors influence urban surface temperature distributions and their viewing by remote sensors, and control the magnitude of effective anisotropy of brightness temperature

  • To avoid the difficult issue of temperature-emissivity separation, we investigate the effective anisotropy in terms of TB, the brightness temperature [26]

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Summary

Introduction

Surface-atmosphere exchanges of heat and water are core drivers of the fair-weather meteorology and climatology of cities. Assessment of these energy fluxes is undertaken within the context of the Remote Sens. 2016, 8, 108 surface energy balance [1], which determines surface temperature, drives atmospheric boundary layer processes and controls thermal climate. Better understanding and measurement of the urban energy balance is required to more effectively adapt our built environments for the purposes of, for example, urban heat mitigation, air pollution dispersal and water management. Surface temperature is a determining factor for each energy flux in the surface energy balance, with the exception of shortwave radiation. Passive remote sensing of upwelling thermal radiance is an efficient means of observing surface temperature. The protocols and corrections for doing so are well established over relatively flat, undeveloped landscapes [2], but complex three-dimensional (3-D)

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