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Abstract Urbanization affects atmospheric boundary layer dynamics by altering cloud formation and precipitation patterns through the urban heat island (UHI) effect, perturbed wind flows, and urban aerosols, that overall contribute to the urban rainfall effect (URE). This study analyzes an ensemble of numerical simulations with the Weather Research and Forecasting (WRF) model and its version with coupled chemistry and aerosols (WRF-Chem) through a Functional ANalysis Of VAriance (FANOVA) approach to isolate the urban signature from the regional climatology and to investigate the relative contributions of various mechanisms and drivers to the URE. Different metropolitan areas across the United States are analyzed and their urban land cover and anthropogenic emissions are replaced with dominant land-use categories such as grasslands or croplands and biogenic only emissions, as in neighboring regions. Our findings indicate a significant role of the urban land cover in impacting surface temperature and turbulent kinetic energy over the city, and precipitation patterns, both within and downwind of the urban environment. Moreover, simulations of a deep convection event suggest that the aerosols impact dominates the sign and spatial extent of the changes in the simulated precipitation compared to the UHI effect, leading to a significant precipitation enhancement within the urban borders and suppression in downwind regions. 

Abstract Accurate microscale flow simulations are essential for assessing wind characteristics in complex terrain. This study evaluates a large ensemble of multiscale simulations, including large‐eddy simulations (LES), using the Weather Research and Forecasting model (WRF) over Perdigão, Portugal, driven by boundary conditions from multiple global data sets. Simulations are compared with data from the Perdigão field campaign, including radiosonde and flux tower measurements. Results show that LES, using high‐resolution topography and land use data, better replicate flow features and dynamics, providing valuable insights for wind resource quantification and mapping. We identify that model performance varies spatially. The RMSE of wind speed at 10 m at ridge towers is 5.65 m , while at valley towers, it is lower (2.28 m ), and variation across runs is greater for higher wind speeds. Temporally, surface winds show substantial variability throughout the day, posing greater modeling challenges during nighttime and synoptic transitions. This variability is not observed at 100 m, where topographic effects are less dominant and RMSE remains consistent across runs. Simulations driven by hourly boundary conditions perform best. However, drawing general conclusions about optimal turbulence modeling in the gray zone remains challenging due to microscale meteorology in complex terrain. Wind field characteristics are sensitive to turbulence scheme choice, particularly in the boundary layer, while above it, wind behavior is mainly influenced by boundary conditions. These results help identify key factors driving model variability and biases, which may guide future model developments to enhance wind flow simulation accuracy and reliability. 

Abstract A range of multi‐year observational data sets are used to characterize the hydroclimate of the Dallas Fort‐Worth area (DFW) and to investigate the impact of urban land cover on daily accumulated precipitation, RADAR composite reflectivity (cREF), and cloud top height (CTH) during the warm season. Analyses of observational data indicate rainfall rates (RR) in a 45° annulus sector 50–100 km downwind of the city are enhanced relative to an upwind area of comparable size. Enhancement of mean precipitation intensity in this annulus sector is not observed on days with spatially averaged RR > 6 mm/day. Under some flow directions, the probability of cREF >30 dBZ, occurrence of hail, and the probability of CTH >10,000 geopotential meters are also enhanced up to 200 km downwind of DFW. Two deep convection events that passed over DFW are simulated with the Weather Research and Forecasting model using a range of microphysical schemes and evaluated using RADAR observations. Model configurations that exhibit the highest fidelity in these control simulations are used in a series of perturbation experiments where the areal extent of the city is varied between zero (replacement with grassland) and eight times its current size. These perturbation experiments indicate a non‐linear response of Mesoscale Convective System properties to the urban areal extent and a very strong sensitivity to the microphysical scheme used. The impact on precipitation from the urban area, even when it is expanded to eight‐times the current extent, is much less marked for deep convection with stronger synoptic forcing. 

Abstract Recent studies have highlighted the importance of accurate meteorological conditions for urban transport and dispersion calculations. In this work, we present a novel scheme to compute the meteorological input in the Quick Urban & Industrial Complex () diagnostic urban wind solver to improve the characterization of upstream wind veer and shear in the Atmospheric Boundary Layer (ABL). The new formulation is based on a coupled set of Ordinary Differential Equations (ODEs) derived from the Reynolds Averaged Navier–Stokes (RANS) equations, and is fast to compute. Building upon recent progress in modeling the idealized ABL, we include effects from surface roughness, turbulent stress, Coriolis force, buoyancy and baroclinicity. We verify the performance of the new scheme with canonical Large Eddy Simulation (LES) tests with the GPU-accelerated FastEddyEquation missing<#comment/>solver in neutral, stable, unstable and baroclinic conditions with different surface roughness. Furthermore, we evaluate QUIC calculations with and without the new inflow scheme with real data from the Urban Threat Dispersion (UTD) field experiment, which includes Lidar-based wind measurements as well as concentration observations from multiple outdoor releases of a non-reactive tracer in downtown New York City. Compared to previous inflow capabilities that were limited to a constant wind direction with height, we show that the new scheme can model wind veer in the ABL and enhance the prediction of the surface cross-isobaric angle, improving evaluation statistics of simulated concentrations paired in time and space with UTD measurements. 

Abstract Two Sundowner events observed during the Sundowner Wind Experiment (SWEX) project are analyzed using a realistically forced large eddy simulation (LES) employing a multiscale Weather and Research Forecasting (WRF) model configuration with domain grid spacings ranging from 11,250 to 30 m centered over the Santa Barbara, CA region to examine their meso‐ to micro‐scale drivers. The main drivers of both events are increasing mountaintop stability and the mountain wave activity exhibiting a hydraulic jump and near‐surface critical layer. Another important finding is ascent of the downslope flows over the turbulent adiabatic layers at the coastal regions. In both events, the strong downslope flow warms and dries the air descending the southern slopes of the SYM adiabatically generating a deepening adiabatic layer that is 0.4 to as much as 1 km deep during peak Sundowner intensity over the coastal regions. This layer, exhibiting turbulence within and atop, is characterized with the strong downslope flow atop with much weaker, and at times, reversed flow beneath over the coastal regions. This flow structure, along with regions of turbulence within and atop the adiabatic layer, is indicative of a mountain lee‐wave rotor. Coastal locations in both events remain relatively unaffected. Further investigations are needed to determine whether or not this is consistent across all Sundowner events observed during the SWEX project and whether turbulence helps diffuse or accelerate the flows. 

Abstract We present a new ensemble of 36 numerical experiments aimed at comprehensively gauging the sensitivity of nested large-eddy simulations (LES) driven by large-scale dynamics. Specifically, we explore 36 multiscale configurations of the Weather Research and Forecasting (WRF) Model to simulate the boundary layer flow over the complex topography at the Perdigão field site, with five nested domains discretized at horizontal resolutions ranging from 11.25 km to 30 m. Each ensemble member has a unique combination of the following input factors: (i) large-scale initial and boundary conditions, (ii) subgrid turbulence modeling in thegray zoneof turbulence, (iii) subgrid-scale (SGS) models in LES, and (iv) topography and land-cover datasets. We probe their relative importance for LES calculations of velocity, temperature, and moisture fields. Variance decomposition analysis unravels large sensitivities to topography and land-use datasets and very weak sensitivity to the LES SGS model. Discrepancies within ensemble members can be as large as 2.5 m s⁻¹for the time-averaged near-surface wind speed on the ridge and as large as 10 m s⁻¹without time averaging. At specific time points, a large fraction of this sensitivity can be explained by the different turbulence models in the gray zone domains. We implement a horizontal momentum and moisture budget routine in WRF to further elucidate the mechanisms behind the observed sensitivity, paving the way for an increased understanding of the tangible effects of the gray zone of turbulence problem. Significance StatementSeveral science and engineering applications, including wind turbine siting and operations, weather prediction, and downscaling of climate projections, call for high-resolution numerical simulations of the lowest part of the atmosphere. Recent studies have highlighted that such high-resolution simulations, coupled with large-scale models, are challenging and require several important assumptions. With a new set of numerical experiments, we evaluate and compare the significance of different assumptions and outstanding challenges in multiscale modeling (i.e., coupling large-scale models and high-resolution atmospheric simulations). The ultimate goal of this analysis is to put each individual assumption into the wider perspective of a realistic problem and quantify its relative importance compared to other important modeling choices.