An advanced geometric model to simulate thermal anisotropy time-series for simplified urban neighborhoods (GUTA-T)

Abstract

The anisotropy of directional radiative surface temperature measurements over urban building surfaces is expected to have strong temporal variation. However, a model with the capability to simulate the temporal variation of urban thermal anisotropy (UTA) and with inversion abilities has not yet been developed. In this paper, the relationship between the temporal variation of UTA and temperature contrasts among surface components is investigated. Based on a system of previously developed geometric models for simulation of thermal anisotropy in simplified urban neighborhoods of different densities, a system of advanced geometric models, GUTA-T, is proposed that incorporates the temporal variability of UTA through the dependence of temperature contrasts on solar zenith angle θs under clear skies. A 3-D microscale urban surface temperature model and a sensor view model are combined to generate a synthetic UTA dataset for a variety of simplified urban geometries and with which to evaluate GUTA-T. The results show that parameter inversions are sensitive to sun-surface-sensor geometries. The seasonal and hourly behavior of anisotropy is simulated by GUTA-T with mean absolute differences of 1.06 °C, 1.25 °C, 1.69 °C and 1.04 °C for the view zenith and azimuth angles (θv, φv) of (40°, 90°), (60°, 270°), (60°, 180°) and (40°, 130°), respectively. Evaluation using diurnal measurements over an urban scale model shows that the root mean square difference between simulated anisotropy and the reference anisotropy generated from observed directional temperatures and a high-accuracy computer model is 2.02 °C for θs ≤ 60°. If the proposed model is to be calibrated with satellite data, a second directional observation from the opposite side of the surface target area can improve model performance significantly. Calibrations using samples including only small (e.g., θs ≤ 30°) or large θs (e.g., θs ≥ 50°) tend to decrease the anisotropy time-series simulation accuracy. Due to its inversion abilities, the GUTA-T model has the potential to simulate and correct anisotropy in time-series thermal infrared remote sensing data, thereby improving the analysis either of spatial variation within an image, or of temporal variation of angular urban surface temperature.