We present results from laboratory and computational experiments on the

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The presence of tall buildings in cities affects momentum and scalar exchange within and above the urban canopy. As wake effects can be important over large distances, they are crucial for urban-flow modelling on and across different spatial scales. We explore the aerodynamic effects of tall buildings on the microscale to local scales with a focus on the interaction between the wake structure, canopy and roughness sublayer flow of the surroundings in a realistic urban setting in central London. Flow experiments in a boundary-layer wind tunnel use a 1:200 scale model with two tall buildings (81 m and 134.3 m) for two wind directions. Large changes in mean flow, turbulence statistics and instantaneous flow structure of the wake are evident when tall buildings are part of the complex urban canopy rather than isolated. In the near-wake, the presence of lower buildings displaces the core of the recirculation zone upwards, thereby reducing the vertical depth over which flow reversal occurs. This amplifies vertical shear at the rooftop and enhances turbulent momentum exchange. In the near part of the main wake, lateral velocity fluctuations and hence turbulence kinetic energy are reduced compared to the isolated building case as eddies generated in the urban canopy and roughness sublayer distribute energy down to smaller scales that dissipate more rapidly. Evaluation of a wake model for flow past isolated buildings suggests model refinements are needed to account for such flow-structure changes in tall-building canopies. Keywords Tall-building environments • Urban canopy • Urban flow • Wake model • Wind tunnel Nomenclature H Building (roof) height H NoTall Mean building height (No Tall) H ave Mean building height Electronic supplementary material The online version of this article (

Two urban schemes within the Joint UK Land Environment Simulator (JULES) are evaluated offline against multi-year flux observations in the densely built-up city centre of London and in suburban Swindon (UK): (i) the 1-tile slab model, used in climate simulations; (ii) the 2-tile canopy model MORUSES (Met Office–Reading Urban Surface Exchange Scheme), used for numerical weather prediction over the UK. Offline, both models perform better at the suburban site, where differences between the urban schemes are less pronounced due to larger vegetation fractions. At both sites, the outgoing short- and longwave radiation is more accurately represented than the turbulent heat fluxes. The seasonal variations of model skill are large in London, where the sensible heat flux in autumn and winter is strongly under-predicted if the large city centre magnitudes of anthropogenic heat emissions are not represented. The delayed timing of the sensible heat flux in the 1-tile model in London results in large negative bias in the morning. The partitioning of the urban surface into canyon and roof in MORUSES improves this as the roof tile is modelled with a very low thermal inertia, but phase and amplitude of the grid box-averaged flux critically depend on accurate knowledge of the plan-area fractions of streets and buildings. Not representing non-urban land cover (e.g. vegetation, inland water) in London results in severely under-predicted latent heat fluxes. Control runs demonstrate that the skill of both models can be greatly improved by providing accurate land cover and morphology information and using representative anthropogenic heat emissions, which is essential if the model output is intended to inform integrated urban services.

Transboundary smoke haze caused by biomass burning frequently causes extreme air pollution episodes in maritime and continental Southeast Asia. With millions of people being affected by this type of pollution every year, the task to introduce smoke haze related air quality forecasts is urgent. We investigate three severe haze episodes: June 2013 in Maritime SE Asia, induced by fires in central Sumatra, and March/April 2013 and 2014 on mainland SE Asia. Based on comparisons with surface measurements of PM10 we demonstrate that the combination of the Lagrangian dispersion model NAME with emissions derived from satellite‐based active‐fire detection provides reliable forecasts for the region. Contrasting two fire emission inventories shows that using algorithms to account for fire pixel obscuration by cloud or haze better captures the temporal variations and observed persistence of local pollution levels. Including up‐to‐date representations of fuel types in the area and using better conversion and emission factors is found to more accurately represent local concentration magnitudes, particularly for peat fires. With both emission inventories the overall spatial and temporal evolution of the haze events is captured qualitatively, with some error attributed to the resolution of the meteorological data driving the dispersion process. In order to arrive at a quantitative agreement with local PM10 levels, the simulation results need to be scaled. Considering the requirements of operational forecasts, we introduce a real‐time bias correction technique to the modeling system to address systematic and random modeling errors, which successfully improves the results in terms of reduced normalized mean biases and fractional gross errors.

di saBatino, JunXia dou, daniel r. dreW, John M. edWards, JoaChiM fallMann, krzysztof fortuniak, JeMMa gornall, toBias groneMeier, Christos h. halios, denise hertWig, kohin hirano, alBert a. M. holtslag, zhiWen luo, gerald Mills, Makoto nakayoshi, kathy Pain, k. heinke sChlünzen, stefan sMith, lionel soulhaC, gert-Jan steeneveld, ting sun, natalie e theeuWes, david thoMson, JaMes a. voogt, helen C. Ward, zheng-tong Xie, and Jian zhong W ith the majority of people experiencing weather in urban areas, it is critical to understand cities, weather, and climate impacts. Increasing climate extremes (e.g., heat stress, air pollution, flash flooding) combined with the density of people means it is essential that city infrastructure and operations can withstand high-impact weather. Thus, there is a huge opportunity to mitigate climate change effects and provide healthier environments through design and planning to reduce the background climate and urban effects. However, our understanding of the underlying urban atmospheric processes are primarily derived from studies of separate aspects, rather than the complete, human-environment system. Air quality modeling has not been widely integrated with aerosol feedbacks on local climate, while few city-greening scenarios have tested the impacts on boundary layer pollutant dispersion or the carbon cycle. Building design guidelines have been developed without incorporating the impact of waste heat on local temperatures, which, in turn, determines building performance. Integration of such feedbacks is imperative as they define, rather than just modify, urban climate.There is an urgent need to link processes that people experience at street level (human scale) to processes at neighborhood, city, and regional scales. As these scales have traditionally been the focus for specialists in different fields, few observation and model systems cross these scales. However, understanding the interactions between these scales is critical for the design of future parametrizations ES261OCTOBER 2017 AMERICAN METEOROLOGICAL SOCIETY | and observation networks. Although models and observational methods are emerging that permit research into scale interactions [e.g., high-resolution numerical weather prediction (NWP), large-domain computational fluid dynamic (CFD) models, remote sensing, extensive sensor networks, vertical remote sensing], an integrated approach across methodologies is currently lacking.To tackle these scale interactions requires diverse skills from a wide range of research communities. This is a daunting challenge. However, improved understanding of urban atmospheric processes such as clouds and precipitation, heat transfer, and convection would enable improvements in urban system models to provide seamless hazard prediction at all time scales. Hence, an initial focus on the meteorological aspects of the research challenge may be a more manageable problem, even though the scope is still large. As such, it was identified that within the United Kingdom there is an urgent need to devel...

Wind fields in the atmospheric surface layer (ASL) are highly three-dimensional and characterized by strong spatial and temporal variability. For various applications such as wind comfort assessments and structural design, an understanding of potentially hazardous wind extremes is important. Statistical models are designed to facilitate conclusions about the occurrence probability of wind speeds based on the knowledge of low-order flow statistics. Being particularly interested in the upper tail regions we show that the statistical behavior of near-surface wind speeds is adequately represented by the Beta distribution. By using the properties of the Beta probability density function in combination with a model for estimating extreme values based on readily available turbulence statistics, it is demonstrated that this novel modelling approach reliably predicts the upper margins of encountered wind speeds. The model's basic parameter is derived from three substantially different calibrating datasets of flow in the ASL originating from boundary-layer wind-tunnel measurements and direct numerical simulation. Evaluating the model based on independent field observations of nearsurface wind speeds showed a high level of agreement between the statistically modelled horizontal wind speeds and measurements. The results show that, based on the knowledge of only a few simple flow statistics (mean wind speed, wind speed fluctuations and integral time scales), the occurrence probability of velocity magnitudes at arbitrary flow locations in the ASL can be estimated with a high degree of confidence.

The need to balance computational speed and simulation accuracy is a key challenge in designing atmospheric dispersion models that can be used in scenarios where near real-time hazard predictions are needed. This challenge is aggravated in cities, where models need to have some degree of building-awareness, alongside the ability to capture effects of dominant urban flow processes. We use a combination of high-resolution large-eddy simulation (LES) and wind-tunnel data of flow and dispersion in an idealised, equal-height urban canopy to highlight important dispersion processes and evaluate how these are reproduced by representatives of the most prevalent modelling approaches: (i) a Gaussian plume model, (ii) a Lagrangian stochastic model and (iii) street-network dispersion models. Concentration data from the LES, validated against the wind-tunnel data, were averaged over the volumes of streets in order to provide a high-fidelity reference suitable for evaluating the different models on the same footing. For the particular combination of forcing wind