James A. Voogt

Thermal remote sensing of urban climates

Thermal remote sensing has been used over urban areas to assess the urban heat island, to perform land cover classifications and as input for models of urban surface atmosphere exchange. Here, we review the use of thermal remote sensing in the study of urban climates, focusing primarily on the urban heat island effect and progress made towards answering the methodological questions posed by Roth et al. [International Journal of Remote Sensing 10 (1989) 1699]. The review demonstrates that while some progress has been made, the thermal remote sensing of urban areas has been slow to advance beyond qualitative description of thermal patterns and simple correlations. Part of the difficulty lies in the tendency to use qualitatively based land use data to describe the urban surface rather than the use of more fundamental surface descriptors. Advances in the application of thermal remote sensing to natural and agricultural surfaces suggest insight into possible methods to advance techniques and capabilities over urban areas. Improvements in the spatial and spectral resolution of current and next-generation satellite-based sensors, in more detailed surface representations of urban surfaces and in the availability of low cost, high resolution portable thermal scanners are expected to allow progress in the application of urban thermal remote sensing to the study of the climate of urban areas.

The International Urban Energy Balance Models Comparison Project: First Results from Phase 1

A large number of urban surface energy balance models now exist with different assumptions about the important features of the surface and exchange processes that need to be incorporated. To date, no comparison of these models has been conducted; in contrast, models for natural surfaces have been compared extensively as part of the Project for Intercomparison of Land-surface Parameterization Schemes. Here, the methods and first results from an extensive international comparison of 33 models are presented. The aim of the comparison overall is to understand the complexity required to model energy and water exchanges in urban areas. The degree of complexity included in the models is outlined and impacts on model performance are discussed. During the comparison there have been significant developments in the models with resulting improvements in performance (root-mean-square error falling by up to two-thirds). Evaluation is based on a dataset containing net all-wave radiation, sensible heat, and latent heat flux observations for an industrial area in Vancouver, British Columbia, Canada. The aim of the comparison is twofold: to identify those modeling approaches that minimize the errors in the simulated fluxes of the urban energy balance and to determine the degree of model complexity required for accurate simulations. There is evidence that some classes of models perform better for individual fluxes but no model performs best or worst for all fluxes. In general, the simpler models perform as well as the more complex models based on all statistical measures. Generally the schemes have best overall capability to model net all-wave radiation and least capability to model latent heat flux.

Complete urban surface temperatures

An observation program using ground and airborne thermal infrared radiometers is used to estimate the surface temperature of urban areas, taking into account the total active surface area. The authors call this the complete urban surface temperature. This temperature is not restricted by the viewing biases inherent in remote sensors used to estimate surface temperature over rough surfaces such as cities. Two methods to estimate the complete surface temperature are presented. Results for three different land-use areas in the city of Vancouver, British Columbia, Canada, show significant differences exist between the complete, nadir, and off-nadir airborne estimates of urban surface temperature during daytime. For the sites and times studied, the complete surface temperature is shown to agree with airborne off-nadir estimates of the apparent surface temperature of the most shaded walls. Some implications of using the complete surface temperature to estimate screen level air temperature and to calculate surface sensible heat flux are given.

Effects of urban surface geometry on remotely-sensed surface temperature

Direct observations of urban surface temperature in daytime are made using a thermal scanner mounted in a helicopter over three land-use areas in Vancouver, B.C. The results reveal strong directional variations in the observed apparent surface temperature. The variations arise due to the differential patterns of irradiated and shaded surfaces within the sensor field of view created by the three-dimensional urban surface structure and the position of the Sun. The directional variations may be considered to be a form of effective anisotropy due to the large scale roughness of the urban surface. Variations in excess of 9 C were measured over a downtown area. Urban residential and light industrial land-use areas also exhibited strong effective anisotropy. The directional temperature variations are of similar magnitude to atmospheric corrections applied to thermal imagery. This implies that effective anisotropy should receive serious consideration in the interpretation of thermal imagery obtained over urban ar...

Modeling Surface Sensible Heat Flux Using Surface Radiative Temperatures in a Simple Urban Area

Abstract Sensible heat fluxes over a light industrial area in Vancouver, British Columbia, Canada, are analyzed from observed tower fluxes and modeled using a bulk heat transfer approach. The bulk transfer models are initialized using remotely sensed surface temperatures from both airborne and ground-based observing platforms. The remotely sensed surface temperature, in conjunction with a surface database, is used to create area-weighted temperature estimates representative of the complete urban surface. Sensitivity analyses of the various surface temperature estimates are performed. Estimates of kB−1, the ratio of roughness length of momentum to heat, for this area are in general agreement with theoretical estimates for bluff-rough surfaces and are larger than those documented for vegetated and agricultural surfaces. Back-calculated values do vary depending on the method used to determine surface temperature but vary more with the time of day. Empirical relations derived previously for vegetated surfaces...

Comparison of Four Methods to Estimate Urban Heat Storage

The relative performance of four independent methods to estimate the magnitude and diurnal behavior of net heat storage fluxes (QS) in a city center is assessed. This heat flux is a significant but understudied component of the urban surface energy balance (SEB). Direct measurement of this SEB term at the local scale (horizontal length scale 10 2 –10 4 m) is practically unattainable primarily because of the complex array of materials and the three-dimensionality of urban systems. Results of an 8-day summertime observational study at a site in the center of Marseille, France, are presented. This locale is an ideal environment for such research because of the warm, dry climate (hence the SEB is dominated by sensible heat exchanges) and the high density of tall buildings with thick walls (hence it has a large thermal mass that favors heat storage as a component of the SEB). Estimates of QS derived as residuals in the SEB, after the remaining terms are measured directly, (termed RES) are compared with those calculated from a parameterization scheme [objective hysteresis model (OHM)], a local-scale numerical model [Town Energy Balance model (TEB)], and a bulk heat transfer method [thermal mass scheme (TMS)]. Inputs to the methods include observed meteorological data and morphometric properties of the urban site. All approaches yield a similar diurnal course. The OHM and TEB methods tend to slightly overestimate storage uptake by day when compared with the RES, whereas TMS slightly underestimates it. All methods underestimate heat storage release at night when compared with RES and show some sensitivity to wind speed, especially above about 5 m s 1 . OHM estimates perform satisfactorily in the mean but miss short-term variability and are poor at night. TEB simulations show the best agreement with RES results, particularly at night. TMS values are comparable to those from the other methods, but its extensive input requirements render it almost impractical. Overall, the convergence of results is reassuring but the lack of a standard for quantifying heat storage and the spread of results mean this term remains a source of imprecision in urban energy balance measurement and modeling.

Characterizing Urban Surface Cover and Structure with Airborne Lidar Technology

Urban and landscape planners are becoming increasingly aware of the potential of light detection and ranging (lidar) technology to produce height and structural information over large geographic areas in both an economic and time-efficient fashion. In urban environments where the structural complexity is high, for example, lidar is seen as a critical and innovative dataset to improve the characterization of both vegetation and building attributes. Using a small-footprint, first- and last-return lidar dataset of Vancouver, Canada, we demonstrate the potential to derive a suite of attributes important for describing the interactions of the urban surface and atmosphere in weather forecasting, air pollution, and urban dispersion modelling. Two levels of attributes were defined. First, primary attributes such as building shape, size, and location and tree classification were calculated. Building extent and size were computed using an object-based approach based on connectivity and height rules. The classification of tree crown areas was derived from the location of last-return data, filtered to remove the incidence of last returns caused by the interaction of the lidar beam with building edges, and height rules. Validation showed that building areas derived from lidar compared well with aerial photography estimates (r2 = 0.96, p < 0.001, n = 98). The percentage difference between estimates was equal to 16% (n = 83) when buildings were discriminated from the surrounding features. However, the percentage difference between estimates increased to 35% (n = 98) when commission errors were considered, as lidar often overestimated building areas due to closely spaced buildings (gaps less than 1–2 m) not being separated. Similarly, the height and area of lidar-extracted trees were highly correlated with field-based measurements (r2 = 0.84 and 0.76, respectively, p < 0.001, n = 50). Once these primary attributes were derived, we demonstrate the extraction of a number of secondary attributes including building mean height, normalized building volume, building wall surface area, and interelement spacing. Of significance, this research has shown that lidar can provide spatially detailed estimates of urban structure and cover which characterize the aerodynamic and energetic properties of urban areas.

Enhancing Hydrologic Modelling in the Coupled Weather Research and Forecasting-Urban Modelling System

Urbanization modifies surface energy and water budgets, and has significant impacts on local and regional hydroclimate. In recent decades, a number of urban canopy models have been developed and implemented into the Weather Research and Forecasting (WRF) model to capture urban land-surface processes. Most of these models are inadequate due to the lack of realistic representation of urban hydrological processes. Here, we implement physically-based parametrizations of urban hydrological processes into the single layer urban canopy model in the WRF model. The new single-layer urban canopy model features the integration of, (1) anthropogenic latent heat, (2) urban irrigation, (3) evaporation from paved surfaces, and (4) the urban oasis effect. The new WRF–urban modelling system is evaluated against field measurements for four different cities; results show that the model performance is substantially improved as compared to the current schemes, especially for latent heat flux. In particular, to evaluate the performance of green roofs as an urban heat island mitigation strategy, we integrate in the urban canopy model a multilayer green roof system, enabled by the physical urban hydrological schemes. Simulations show that green roofs are capable of reducing surface temperature and sensible heat flux as well as enhancing building energy efficiency.

Impacts of Urban Albedo Increase on Local Air Temperature at Daily–Annual Time Scales: Model Results and Synthesis of Previous Work

Abstract The authors combine urban and soil–vegetation surface parameterization schemes with one-dimensional (1D) boundary layer mixing and radiation parameterizations to estimate the maximum impact of increased surface albedo on urban air temperatures. The combined model is evaluated with measurements from an urban neighborhood in Basel, Switzerland, and the importance of surface–atmosphere model coupling is demonstrated. Impacts of extensive albedo increases in two Chicago, Illinois, neighborhoods are modeled. Clear-sky summertime reductions of diurnal maximum air temperature for the residential neighborhood (λp = 0.33) are −1.1°, −1.5°, and −3.6°C for uniform roof albedo increases of 0.19, 0.26, and 0.59, respectively; reductions are about 40% larger for the downtown core (λp = 0.53). Realistic impacts will be smaller because the 1D modeling approach ignores advection; a lake-breeze scenario is modeled and temperature reductions decline by 80%. Assuming no advection, the analysis is extended to seasona...

Research priorities in observing and modeling urban weather and climate

In 2007, the world reached the unprecedented milestone of half of its people living in cities, and that proportion is projected to be 60% in 2030. The combined effect of global climate change and rapid urban growth, accompanied by economic and industrial development, will likely make city residents more vulnerable to a number of urban environmental problems, including extreme weather and climate conditions, sea-level rise, poor public health and air quality, atmospheric transport of accidental or intentional releases of toxic material, and limited water resources. One fundamental aspect of predicting the future risks and defining mitigation strategies is to understand the weather and regional climate affected by cities. For this reason, dozens of researchers from many disciplines and nations attended the Urban Weather and Climate Workshop.1 Twenty-five students from Chinese universities and institutes also took part. The presentations by the workshops participants span a wide range of topics, from the interaction between the urban climate and energy consumption in climate-change environments to the impact of urban areas on storms and local circulations, and from the impact of urbanization on the hydrological cycle to air quality and weather prediction.