Direct numerical simulations of turbulent flow over regular arrays of urban-like, cubical obstacles are reported. Results are analysed in terms of a formal spatial averaging procedure to enable interpretation of the flow within the arrays as a canopy flow, and of the flow above as a rough wall boundary layer. Spatial averages of the mean velocity, turbulent stresses and pressure drag are computed. The statistics compare very well with data from wind-tunnel experiments. Within the arrays the time-averaged flow structure gives rise to significant 'dispersive stress' whereas above the Reynolds stress dominates. The mean flow structure and turbulence statistics depend significantly on the layout of the cubes. Unsteady effects are important, especially in the lower canopy layer where turbulent fluctuations dominate over the mean flow.

The structure of turbulent flow over large roughness consisting of regular arrays of cubical obstacles is investigated numerically under constant pressure gradient conditions. Results are analysed in terms of first-and second-order statistics, by visualization of instantaneous flow fields and by conditional averaging. The accuracy of the simulations is established by detailed comparisons of first-and second-order statistics with wind-tunnel measurements. Coherent structures in the log region are investigated. Structure angles are computed from two-point correlations, and quadrant analysis is performed to determine the relative importance of Q2 and Q4 events (ejections and sweeps) as a function of height above the roughness. Flow visualization shows the existence of low-momentum regions (LMRs) as well as vortical structures throughout the log layer. Filtering techniques are used to reveal instantaneous examples of the association of the vortices with the LMRs, and linear stochastic estimation and conditional averaging are employed to deduce their statistical properties. The conditional averaging results reveal the presence of LMRs and regions of Q2 and Q4 events that appear to be associated with hairpin-like vortices, but a quantitative correspondence between the sizes of the vortices and those of the LMRs is difficult to establish; a simple estimate of the ratio of the vortex width to the LMR width gives a value that is several times larger than the corresponding ratio over smooth walls. The shape and inclination of the vortices and their spatial organization are compared to recent findings over smooth walls. Characteristic length scales are shown to scale linearly with height in the log region. Whilst there are striking qualitative similarities with smooth walls, there are also important differences in detail regarding: (i) structure angles and sizes and their dependence on distance from the rough surface; (ii) the flow structure close to the roughness; (iii) the roles of inflows into and outflows from cavities within the roughness; (iv) larger vortices on the rough wall compared to the smooth wall; (v) the effect of the different generation mechanism at the wall in setting the scales of structures.

SUMMARYAn urban canopy model is developed for spatially averaged mean winds within and above urban areas. The urban roughness elements are represented as a canopy-element drag carefully formulated in terms of morphological parameters of the building arrays and a mean sectional drag coefficient for a single building. Turbulent stresses are represented using a mixing-length model, with a mixing length that depends upon the density of the canopy and distance from the ground, which captures processes known to occur in canopies. The urban canopy model is sufficiently simple that it can be implemented in numerical weather-prediction models.The urban canopy model compares well with wind tunnel measurements of the mean wind profile through a homogeneous canopy of cubical roughness elements and with measurements of the effective roughness length of cubical roughness elements. These comparisons give confidence that the basic approach of a canopy model can be extended from fine-scale vegetation canopies to the canopies of large-scale roughness elements that characterize urban areas.The urban canopy model is also used to investigate the adjustment to inhomogeneous canopies. The canonical case of adjustment of a rural boundary layer to a uniform urban canopy shows that the winds within the urban canopy adjust after a distance x 0 = 3L c ln K, where L c is the canopy drag length-scale, which characterizes the canopy-element drag, and ln K depends weakly on canopy parameters and varies between about 0.5 and 2. Thus the density and shape of buildings within a radius x 0 only determine the local canopy winds. In this sense x 0 gives a dynamical definition of the size of a neighbourhood.The urban canopy model compares well with observations of the deceleration of the wind associated with adjustment of a rural boundary layer to a canopy of cubical roughness elements, but only when the sectional drag coefficient is taken to be somewhat larger than expected. We attribute this discrepancy to displacement of streamlines around the large-scale urban roughness elements, which yields a stress that decelerates the wind. A challenge for future research is to incorporate this additional 'dispersive stress' into the urban canopy model.

Abstract. Further to our previous Large-Eddy Simulation (LES) of flow over a staggered array of uniform cubes (Xie & Castro, 2006), a simulation of flow over random urban-like obstacles is presented. To gain a deeper insight into the effects of randomness in the obstacle topology, the current results, e.g. spatially averaged mean velocity, Reynolds stresses, turbulence kinetic energy (TKE) and dispersive stresses, are compared with our previous LES data and Direct Numerical Simulation (DNS) data (Coceal et al., 2006) of flow over uniform cubes. Significantly different features in the turbulence statistics are observed within and immediately above the canopy, although there are some similarities in the spatially-averaged statistics. It is also found that the relatively high pressures on the tallest buildings generate contributions to the total surface drag which are far in excess of their proportionate frontal area within the array. Details of the turbulence characteristics (like the stress anisotropy) are compared with those in regular roughness arrays and attempts to find some generality in the turbulence statistics within the canopy region are discussed.

The Clean Air for London (ClearfLo) project provides integrated measurements of the meteorology, composition, and particulate loading of the urban atmosphere in London, United Kingdom, to improve predictive capability for air quality. METEOROLOGY, AIR QUALITY, AND HEALTH IN LONDONThe ClearfLo Project Economic and Social Affairs 2013). Urban populations are exposed to stressful environmental conditions, such as local and nonlocal pollutants, that cause poor air quality and microclimates that exacerbate heat stress during heat waves. These are projected to increase in a warming climate. Our cities are therefore nexus points for several environmental health stresses that we currently face (Rydin et al. 2012) and the interacting issues around sustainability and human health.The purpose of this paper is to introduce the Clean Air for London (ClearfLo) project, which investigates the atmospheric science that underpins these health stresses, with a particular focus on the urban increment in atmospheric drivers. We focused on three atmospheric drivers of environmental health stress in cities, namely, heat, gas-phase pollutants, and particulate matter (PM). Health stresses from the urban atmospheric environment.Heat waves have an impact on human health. Populations typically display an optimal temperature range at which the (daily or weekly) mortality rate is lowest. Mortality rates rise as temperatures exceed this optimal range (e.g., Rydin et al. 2012). The 2003 European heat wave (Stedman 2004) in combination with air pollution was responsible for more than 2000 excess deaths in the United Kingdom (Johnson et al. 2005). Under a warming climate, the risks posed by heat stress are predicted to increase (Hacker et al. 2005). People living in urban environments are exposed to higher temperatures than in nonurban regions. Thus, heat-related deaths could be higher within urban areas (Mavrogianni et al. 2011). Hence, ClearfLo is concerned with measuring the factors controlling the urban atmospheric boundary layer, that is, the surface energy balance.The World Health Organization (WHO) reported (WHO 2006) that the strongest effects of air quality 779MAY 2015 AMERICAN METEOROLOGICAL SOCIETY | on health are attributable to PM, followed by ozone (O 3 ) and nitrogen dioxide (NO 2 ). A recent report (Guerreiro et al. 2013) indicates that in 2011 up to 88% of the urban population in Europe was exposed to concentrations exceeding the WHO air quality guidelines for PM 10 (defined as particles that pass through a size-selective inlet with a 50% efficiency cutoff at 10-µm aerodynamic diameter, representative of the inhalable fraction). It is estimated that a reduction of PM 10 to the WHO annual-mean guideline of 20 µg m −3 would reduce attributable deaths per year in Europe by 22,000. Further, this would lead to a substantial improvement in the quality of life for millions with a preexisting respiratory or cardiovascular disease (COMEAP 2010).Epidemiological studies consistently demonstrate an association between the PM mass concentr...