Variability of physical meteorology in urban areas at different scales: implications for air quality

Hertwig, Denise; Grimmond, Sue; Kotthaus, Simone; Vanderwel, Christina; Gough, Hannah; Haeffelin, Martial; Robins, Alan

doi:10.1039/d0fd00098a

Cited by 21 publications

(12 citation statements)

References 71 publications

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“…Instead of focusing on the actual drivers of air quality, e.g., pollutant dispersion, the idea is to focus on flow behaviour, which is then characterized using a model. Hertwig et al [76] employed the aforementioned approach to study the relation between meteorological variability and air quality in urban environments. They combined open-environment observations with wind-tunnel experiments to compare with the prediction of several models [76].…”

Section: Closed-environment Experimentsmentioning

confidence: 99%

“…Hertwig et al [76] employed the aforementioned approach to study the relation between meteorological variability and air quality in urban environments. They combined open-environment observations with wind-tunnel experiments to compare with the prediction of several models [76]. A 20 m × 3.5 m × 1.5 m open-return boundary-layer wind-tunnel was used to test a scaled model of a specific area in south London.…”

Section: Closed-environment Experimentsmentioning

confidence: 99%

“…A 20 m × 3.5 m × 1.5 m open-return boundary-layer wind-tunnel was used to test a scaled model of a specific area in south London. The full-scale domain covers 700 m of mixed residential and commercial neighbourhood [76]. Measurements were taken using a two-component laser-Doppler anemometry system.…”

Section: Closed-environment Experimentsmentioning

confidence: 99%

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On the Experimental, Numerical and Data-Driven Methods to Study Urban Flows

2021

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Understanding the flow in urban environments is an increasingly relevant problem due to its significant impact on air quality and thermal effects in cities worldwide. In this review we provide an overview of efforts based on experiments and simulations to gain insight into this complex physical phenomenon. We highlight the relevance of coherent structures in urban flows, which are responsible for the pollutant-dispersion and thermal fields in the city. We also suggest a more widespread use of data-driven methods to characterize flow structures as a way to further understand the dynamics of urban flows, with the aim of tackling the important sustainability challenges associated with them. Artificial intelligence and urban flows should be combined into a new research line, where classical data-driven tools and machine-learning algorithms can shed light on the physical mechanisms associated with urban pollution.

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Section: Closed-environment Experimentsmentioning

confidence: 99%

Section: Closed-environment Experimentsmentioning

confidence: 99%

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On the Experimental, Numerical and Data-Driven Methods to Study Urban Flows

2021

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“…At the urban site, a ceilometer gathered attenuated backscatter that were analysed with the CABAM algorithm 175 (Kotthaus and Grimmond, 2018) to provide the mixing layer height, the atmosphere's lowest layer in contact with earth's surface resulting from turbulent exchange Hertwig et al 2020). This was assumed to equate to the PBLH.…”

Section: Study Period and Meteorological Input Datamentioning

confidence: 99%

“…6), peaking at 84 % at 8 pm, associated with heat released from residential cooling systems and rush hour traffic (Fig. 5) accumulating in a stabilising evening PBL Hertwig et al 2020 and 3.07, respectively), whereas the temporal variations become more similar (R = 0.62 and 0.76 in winter and summer, respectively; Table 3), reflected by the increased linearity between observed and noAHE modelled UHIIs in Fig. 7.…”

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confidence: 98%

Modelling spatiotemporal variations of the canopy layer urban heat island in Beijing at the neighbourhood-scale

Biggart¹,

Stocker²,

Doherty³

et al. 2020

Preprint

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Abstract. Information on the spatiotemporal characteristics of Beijing's urban-rural near-surface air temperature difference, known as the canopy layer urban heat island (UHI), is important for future urban climate management strategies. This paper investigates the variation of near-surface air temperatures within Beijing at a neighbourhood-scale resolution (~ 100 m) during winter 2016 and summer 2017. We perform simulations using the urban climate component of the ADMS-Urban model with land surface parameters derived from both Local Climate Zone classifications and OpenStreetMap land use information. Through sensitivity simulations, the relative impacts of surface properties and anthropogenic heat emissions on the temporal variation of Beijing's UHI are quantified. Measured UHI intensities between central Beijing (Institute of Atmospheric Physics) and a rural site (Pinggu) during the Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-China) campaigns, peak during the evening at ~ 4.5 °C in both seasons. In winter, the nocturnal UHI is dominated by anthropogenic heat emissions but is underestimated by the model. Higher resolution anthropogenic heat emissions may capture the effects of local sources (e.g. residential buildings and adjacent major roads). In summer, evening UHI intensities are underestimated, especially during heatwaves. The inability to fully replicate the prolonged release of heat stored in the urban fabric may explain this. Observed negative daytime UHI intensities in summer are more successfully captured when surface moisture levels in central Beijing are increased. However, the spatial correlation between simulated air temperatures and satellite-derived land surface temperatures is stronger with a lower urban moisture scenario. This result suggests that near-surface air temperatures at the urban meteorological site are likely influenced by fine-scale green spaces that are unresolved by the available land cover data and demonstrates the expected differences between surface and air temperatures related to canopy layer advection. This study lays the foundations for future studies of heat-related health risks and UHI mitigation strategies across Beijing and other megacities.

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Distributed urban drag parametrization for sub‐kilometre scale numerical weather prediction

Sützl

Rooney

Finnenkoetter

et al. 2021

Quart J Royal Meteoro Soc

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A recently developed, height-distributed urban drag parametrization is tested with the London Model, a sub-kilometre resolution version of the Met Office Unified Model over Greater London. The distributed-drag parametrization requires vertical morphology profiles in the form of height-distributed frontal-area functions, which capture the full extent and variability of building heights. London's morphology profiles are calculated and parametrized by an exponential distribution with the ratio of maximum to mean building height as the parameter. A case study evaluates the differences between the new distributed-drag scheme and the current London Model setup using the MORUSES urban land-surface model. The new drag parametrization shows increased horizontal spatial variability in total surface stress, identifying densely built-up areas, high-rise building clusters, parks, and the river. Effects on the wind speed in the lower levels include a lesser gradient and more heterogeneous wind profiles, extended wakes downwind of the city centre, and vertically growing perturbations that suggest the formation of internal boundary layers. The surface sensible heat fluxes are underpredicted, which is attributed to difficulties coupling the distributed momentum exchange with the surface-based heat exchange.

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Variability of physical meteorology in urban areas at different scales: implications for air quality

Abstract: Air quality in cities is influenced not only by the emissions and chemical transformations but also by the physical state of the atmosphere which varies both temporally and spatially. Increasingly,...

Cited by 21 publications

References 71 publications

On the Experimental, Numerical and Data-Driven Methods to Study Urban Flows

On the Experimental, Numerical and Data-Driven Methods to Study Urban Flows

Modelling spatiotemporal variations of the canopy layer urban heat island in Beijing at the neighbourhood-scale

Distributed urban drag parametrization for sub‐kilometre scale numerical weather prediction

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