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Urban overheating and its ongoing exacerbation due to global warming and urban development lead to increased exposure to urban heat and increased thermal discomfort and heat stress. To quantify thermal stress, specific indices have been proposed that depend on air temperature, mean radiant temperature (MRT), wind speed, and relative humidity. While temperature and humidity vary on scales of hundreds of meters, MRT and wind speed are strongly affected by individual buildings and trees and vary on the meter scale. Therefore, most numerical thermal comfort studies apply microscale models to limited spatial domains (commonly representing urban neighborhoods with building blocks) with resolutions on the order of 1 m and a few hours of simulation. This prevents the analysis of the impact of city-scale adaptation and/or mitigation strategies on thermal stress and comfort. To solve this problem, we develop a methodology to estimate thermal stress indicators and their subgrid variability in mesoscale models – here applied to the multilayer urban canopy parameterization BEP-BEM within the Weather Research and Forecasting (WRF) model. The new scheme (consisting of three main steps) can readily assess intra-neighborhood-scale heat stress distributions across whole cities and for timescales of minutes to years. The first key component of the approach is the estimation of MRT in several locations within streets for different street orientations. Second, mean wind speed and its subgrid variability are downscaled as a function of the local urban morphology based on relations derived from a set of microscale LES and RANS simulations across a wide range of realistic and idealized urban morphologies. Lastly, we compute the distributions of two thermal stress indices for each grid square, combining all the subgrid values of MRT, wind speed, air temperature, and absolute humidity. From these distributions, we quantify the high and low tails of the heat stress distribution in each grid square across the city, representing the thermal diversity experienced in street canyons. In this contribution, we present the core methodology as well as simulation results for Madrid (Spain), which illustrate strong differences between heat stress indices and common heat metrics like air or surface temperature both across the city and over the diurnal cycle. [ABSTRACT FROM AUTHOR]

Radiative Coolers (RCs) represent an emerging technology that has the potential to significantly reduce urban heat and improve indoor/outdoor thermal comfort in the built environment. Broadband and Selective Radiative Coolers (BRCs and SRCs, respectively) are the two main types of RCs that have been proposed for application in the built environment. Being both typically characterized by high solar reflectance, the former emits thermal radiation within the overall infrared spectrum, whilst the latter emits thermal radiation mainly within the Atmospheric Window (AW) waverange. A variety of both types of RCs have been developed and tested in both in‐lab and in‐field experimental campaigns. Yet, all experiments comprise small scale specimens that do not represent real‐life scale components of the built environment. In addition, no meso scale assessments have been performed with respect to the RCs' performance at a city scale. Here, for the first time, the thermo‐optical performance of both BRCs and SRCs is introduced and investigated in a multilayer Urban Canopy Model (UCM) coupled with extended Weather Research and Forecasting model (WRF) and we simulated 20 different scenarios representing the vast majority of urban layouts. The outcomes show that both types of RCs maintain lower surface temperature and air temperature at 2 m height inside the canyon, compared to conventional roofs. In addition, a city scale application of RCs has been found capable of decreasing ambient temperature up to 1.6°C as potentially experienced by pedestrians. [ABSTRACT FROM AUTHOR]

In recent years, urban canopy models (UCMs) have been used as fully coupled components of mesoscale atmospheric models as well as offline tools to estimate temperature and surface fluxes using atmospheric forcings. Examples include multi-layer urban canopy models (MLUCMs), where the vertical variability of turbulent fluxes is calculated by solving prognostic momentum and turbulent kinetic energy (TKE, k) using mixing length scale (l) and drag parameterizations. These parameterizations are based on the well-established 1.5-order k-l turbulence closure theory and are often informed by microscale fluid dynamics simulations. However, this approach can include simplifications such as assuming the same diffusion coefficient for momentum, TKE, and scalars. In addition, the dispersive stresses arising from spatially averaged flow properties have been parameterized together with the turbulent fluxes despite being controlled by different mechanisms. Both of these assumptions impact the quantification of the turbulent exchange of flow properties and subsequent air temperature predictions in urban canopies. To assess these assumptions and improve corresponding parameterization, we analyzed 49 large-eddy simulations (LES) for idealized urban arrays, encompassing variable building height distributions and a comprehensive range of urban densities (λp∈[0.0625,0.64]) seen in global cities. We find that the efficiency of turbulent transport (numerically described via diffusion coefficients) is similar for scalars and momentum but is 3.5 times higher for TKE. Additionally, parameterizing the dispersive momentum flux using the k-l closure was a source of error, while scaling with the pressure gradient and urban morphological parameters appears more appropriate. In response to these findings, we propose two changes to the previous version of MLUCM: (a) separate characterization for turbulent diffusion coefficient for momentum and TKE and (b) introduction of an explicit physics-based "mass-flux" term to represent the fraction of the dispersive momentum transport directly induced from buildings as an amendment to the existing "eddy-diffusivity" framework. The updated one-dimensional model, after being tuned for building height variability, is further compared against the original LES results and demonstrates improved performance in predicting vertical turbulent exchange in urban canopies. [ABSTRACT FROM AUTHOR]

We conducted large‐eddy simulations over 98 urban arrays with varying building densities and height distributions. Compared with uniform‐height urban arrays, the influence of height variability on urban flow is pronounced and acts differently in two idealized urban configurations: the low buildings induce higher wind speed and stronger turbulence over staggered arrays but act inversely over aligned building configurations. The flow motions around tall buildings generate strong dispersive fluxes, which are sometimes of similar magnitude to the turbulent momentum flux and responsible for a persistent isolated roughness flow pattern in the upper canopy regardless of the urban density. Tall buildings further contribute disproportionately to the form drag of the urban surface, reaching up to 3.9 times the form drag induced by buildings of height equal to the average building height, in dense layouts. The flow inflection points—that is, the largest wind‐speed gradient that defines the aerodynamic interface between the urban canopy flow and the surface layer flow above—are found to be displaced to the maximum building height if less than 25% of buildings are below the mean building height. These findings provide critical insight for the development of urban canopy models, where the impacts of height variability on flow are often linked to the vertical variation in urban density alone. To address this deficiency, we provide a case study that considers the drag amplification due to the impact of vertical urban structures in the urban canopy model, enabling high‐resolution regional climate models to reproduce urban air flows better. [ABSTRACT FROM AUTHOR]

In recent years, urban canopy models (UCMs) have been used as fully coupled components of mesoscale atmospheric models as well as offline tools to estimate temperature and surface fluxes using atmospheric forcings. Examples include multi-layer urban canopy models (MLUCMs), where the vertical variability of turbulent fluxes is calculated by solving prognostic momentum and turbulent kinetic energy (TKE, k) equations using length scale (l) and drag parameterizations. These parameterizations are based on the well-established 1.5-order k − l turbulence closure theory and are often informed by microscale fluid dynamics simulations. However, this approach can include simplifications such as the assumption of the same diffusion coefficient for momentum, TKE, and scalars. In addition, the dispersive stresses arising from spatially-averaged flow properties have been parameterized together with the turbulent fluxes while being controlled by different mechanisms. Both of these assumptions impact the quantification of turbulent exchange of flow properties and subsequent air temperature prediction in urban canopies. To assess these assumptions and improve corresponding parameterization, we conducted 49 large-eddy simulations (LES) for idealized urban arrays, encompassing variable building height distributions and a comprehensive range of urban densities (λ p ∈ [0.0625,0.64]) seen in global cities. We find that the efficiency of turbulent transport (numerically described via diffusion coefficients) is similar for scalars and momentum but 3.5 times higher for TKE. Additionally, the parameterization of the dispersive momentum flux using the k − l closure was a source of error, while scaling with the pressure gradient and urban morphological parameters appears more appropriate. In response to these findings, we propose two changes to MLUCM v2.0: (a) separate characterization for turbulent diffusion coefficient for momentum and TKE; and (b) introduction of an explicit physics-based 'mass flux' term to represent the non-Gaussian component of the dispersive momentum transport as an amendment to the existing 'eddy diffusivity' framework. The updated one-dimensional model, after being tuned for building height variability, is further compared against the original LES results and demonstrates improved performance in predicting vertical turbulent exchange in urban canopies. [ABSTRACT FROM AUTHOR]

Urban climate model evaluation often remains limited by a lack of trusted urban weather observations. The increasing density of personal weather sensors (PWSs) make them a potential rich source of data for urban climate studies that address the lack of representative urban weather observations. In our study, we demonstrate that carefully quality-checked PWS data not only improve urban climate models' evaluation but can also serve for bias correcting their output prior to any urban climate impact studies. After simulating near-surface air temperatures over London and southeast England during the hot summer of 2018 with the Weather Research and Forecasting (WRF) Model and its building Effect parameterization with the building energy model (BEP–BEM) activated, we evaluated the modeled temperatures against 402 urban PWSs and showcased a heterogeneous spatial distribution of the model's cool bias that was not captured using official weather stations only. This finding indicated a need for spatially explicit urban bias corrections of air temperatures, which we performed using an innovative method using machine learning to predict the models' biases in each urban grid cell. This bias-correction technique is the first to consider that modeled urban temperatures follow a nonlinear spatially heterogeneous bias that is decorrelated from urban fraction. Our results showed that the bias correction was beneficial to bias correct daily minimum, daily mean, and daily maximum temperatures in the cities. We recommend that urban climate modelers further investigate the use of quality-checked PWSs for model evaluation and derive a framework for bias correction of urban climate simulations that can serve urban climate impact studies. Significance Statement: Urban climate simulations are subject to spatially heterogeneous biases in urban air temperatures. Common validation methods using official weather stations do not suffice for detecting these biases. Using a dense set of personal weather sensors in London, we detect these biases before proposing an innovative way to correct them with machine learning techniques. We argue that any urban climate impact study should use such a technique if possible and that urban climate scientists should continue investigating paths to improve our methods. [ABSTRACT FROM AUTHOR]

Urban overheating, and its ongoing exacerbation due to global warming and urban development, leads to increased exposure to urban heat and increased thermal discomfort and heat stress. To quantify thermal stress, specific indices have been proposed that depend on air temperature, mean radiant temperature (MRT), wind speed, and relative humidity. While temperature and humidity vary on scales of hundreds of meters, MRT and wind speed are strongly affected by individual buildings and trees, and vary at the meter scale. Therefore, most numerical thermal comfort studies apply micro-scale models to limited spatial domains (commonly representing urban neighborhoods with building blocks) with resolutions on the order of 1 m and a few hours of simulation. This prevents the analysis of the impact of city-scale adaptation/mitigation strategies on thermal stress and comfort. To solve this problem, we develop a methodology to estimate thermal stress indicators and their subgrid variability in mesoscale models – here applied to the multilayer urban canopy parametrization BEP-BEM within the WRF model. The new scheme (consisting of three main steps) can readily assess intra-neighborhood scale heat stress distributions across whole cities and for time scales of minutes to years. The first key component of the approach is the estimation of MRT in several locations within streets for different street orientations. Second, mean wind speed, and its subgrid variability, are parameterized as a function of the local urban morphology based on relations derived from a set of microscale LES and RANS simulations across a wide range of realistic and idealized urban morphologies. Lastly, we compute the distributions of two thermal stress indices for each grid square combining all the subgrid values of MRT, wind speed, air temperature, and absolute humidity. From these distributions, we quantify the high and low tails of the heat stress distribution in each grid square across the city, representing the thermal diversity experienced in street canyons. In this contribution, we present the core methodology as well as simulation results for Madrid (Spain), which illustrate strong differences between heat stress indices and common heat metrics like air or surface temperature, both across the city and over the diurnal cycle. [ABSTRACT FROM AUTHOR]