This study will employ existing year-long, continuous datasets of hemispherical upwelling longwave irradiances and concurrent ambient conditions from the Basel Urban Boundary Layer Experiment in Switzerland (BUBBLE, 2001-02). Irradiances are used to calculate radiative hemispherical surface temperatures simultaneously for multiple urban and rural sites. At urban sites, tower-mounted, downward-facing pyrgeometers view a full range of vertical, sloped, and horizontal canyon facets – thus sampling temperatures of the complete urban form. These data represent radiometric surface temperature patterns over a continuous time series with dynamic climatic conditions. UHI magnitudes in this study are calculated as urban-rural differences for patches of urban terrain that are representative of a large portion of urban coverage at-large.
Measurement of radiative surface temperature is subject to atmospheric influence from atmospheric absorbers and the air-surface temperature differential. A downward facing pyrgeometer’s broad waveband and wide field-of-view increase the potential for significant atmospheric influence on the at-sensor thermal infrared signal. This systemic error is further compounded by differences in instrument height between urban (>30m) and rural sites (~2 m). Our results show that the magnitude of atmospheric influence varies significantly based on ambient conditions – and thus may be an important source of error relative to typical surface heat island magnitudes. Accordingly, measurements are corrected to both quantify the magnitude of, and remove atmospheric influence on the hemispherical thermal infrared signal. In addition, as is the case in most urban energy balance assessments, upwelling thermal irradiances were measured from below optimal heights. The upwelling thermal infrared signal from an urban patch only becomes spatially invariant at approximately two to three times mean rooftop height. As such, sensitivity analyses were performed to quantify the influence of microscale variations in sensor position relative to urban terrain.
Atmospherically corrected surface temperatures are retrieved in four steps: First, surface-to-sensor path length and view factor geometries are calculated with the surface-sensor-sun urban model (SUM). Using a digital building model and information about sensor position, SUM calculates average path lengths at 5° intervals and angular view factors for the projected pyrgeometer field-of-view. Second, the MODTRAN 4.1 radiative transfer code is used to model spectral at-sensor radiance for each calculated path length. Runs are initialized using profiles of air temperature and water vapor content collected concurrently with thermal infrared measurements. Spectral radiances are convolved by manufacturer supplied spectral sensor response curves to ensure that modeled radiances replicate the sensor signal. Third, radiances are integrated spectrally and weighted at 5° intervals based on their proportion of the total sensor field-of-view. Weighted and corrected angular radiances are integrated 3-dimensionally to yield hemispherical at-sensor irradiances as “seen” by the pyrgeometer for each target surface temperature. Finally, the process is repeated at 0.5 K surface temperature intervals and the results are aggregated into a lookup table relating modeled irradiances to corrected surface temperatures for the range of air temperatures and humidities observed at each site. Measured irradiances, air temperatures, and humidities from BUBBLE are then matched with modeled irradiances to return corresponding corrected surface temperatures. This method applied over the duration of the BUBBLE campaign yields a climatology of weighted and corrected surface temperatures for urban surface heat island assessment.
This study provides the first long-term, temporally continuous climatology of urban surface heat island patterns over varied climatic conditions. Results will show how surface heat islands develop through time and with changing atmospheric conditions. Additionally, surface heat island magnitudes will be compared with concurrent measurements of surface and canopy layer conditions (e.g. rural soil moisture, wind speed, and air temperature heat island magnitude) to elucidate possible driving and controlling factors.