Urban Wind Field Research Articles

Drone Photogrammetry-based Wind Field Simulation for Climate Adaptation in Urban Environments

Addressing climate change issues is one of the most important tasks within the United Nations Sustainable Development Goals. Accurate and efficient simulation of wind fields within cities is essential for climate adaptation. Traditional simplified geometric model-based wind flow simulation can lead to significant errors, affecting the ability to develop effective urban climate strategies. This study addresses this limitation by introducing a novel workflow that leverages drone photogrammetry, deep learning, and geometric complexity quantification to create highly detailed 3D models of in-use building clusters within cities. These models are subsequently used for computational fluid dynamics simulations to accurately predict urban wind fields. The proposed method was validated on three real-world building clusters. Compared to traditional footprint extrusion models, the proposed method demonstrates an average error reduction of 29.2% in large eddy simulation cases and 17.6% in steady Reynolds-averaged Navier-Stokes equations cases. Meanwhile, the proposed model improved computational efficiency by an average of 33.7% in large eddy simulations compared to the flashy oblique photography model. The proposed method provides a balanced model of accuracy and efficiency for urban flow simulations. It has the potential to be incorporated into computational fluid dynamics best practice guidelines, thereby promoting the development of climate-resilient cities.

Evaluating Reduced-Order Urban Wind Models for Simulating Flight Dynamics of Advanced Aerial Mobility Aircraft

Advanced Aerial Mobility (AAM) platforms are poised to begin high-density operations in urban areas nationwide. This new category of aviation platforms spans a broad range of sizes, from small package delivery drones to passenger-carrying vehicles. Unlike traditional aircraft, AAM vehicles operate within the urban boundary layer, where large structures, such as buildings, interrupt the flow. This study examines the response of a package delivery drone, a general aviation aircraft, and a passenger-carrying urban air mobility aircraft through an urban wind field generated using Large Eddy Simulations (LES). Since it is burdensome to simulate flight dynamics in real-time using the full-order solution, reduced-order wind models are created. Comparing trajectories for each aircraft platform using full-order or reduced-order solutions reveals little difference; reduced-order wind representations appear sufficient to replicate trajectories as long as the spatiotemporal wind field is represented. However, examining control usage statistics and time histories creates a stark difference between the wind fields, especially for the lower wing-loading package delivery drone where control saturation was encountered. The control saturation occurrences were inconsistent across the full-order and reduced-order winds, advising caution when using reduced-order models for lightly wing-loaded aircraft. The results presented demonstrate the effectiveness of using a simulation environment to evaluate reduced-order models by directly comparing their trajectories and control activity metrics with the full-order model. This evaluation provides designers valuable insights for making informed decisions for disturbance rejection systems. Additionally, the results indicate that using Reynolds-averaged Navier–Stokes (RANS) solutions to represent urban wind fields is inappropriate. It was observed that the mean wind field trajectories fall outside the 95% confidence intervals, a finding consistent with the authors’ previous research.

Application of convolutional neural network for efficient turbulence modeling in urban wind field simulation

Accurate flow field estimation is crucial for the improvement of outdoor environmental quality, but computational fluid dynamics (CFD) based on the widely used Reynolds-averaged Navier–Stokes method has limitations in this regard. This study developed a turbulence modeling framework based on a convolutional neural network (CNN) to model turbulence in urban wind fields. The CNN model was trained by learning the Reynolds stress patterns and spatial correlations with the use of high-fidelity datasets. Next, the model was integrated into the CFD solver to generate accurate and continuous flow fields. The generalization capability of the proposed framework was initially demonstrated on the simplified benchmark configurations. The validated framework was then applied to case studies of urban wind environments to further assess its performance, and it was shown to be capable of delivering accurate predictions of the velocity field around an isolated building. For more complex geometries, the proposed framework performed well in regions where the flow properties were covered by the training dataset. Moreover, the present framework provided a continuous and smooth velocity field distribution in highly complicated applications, underscoring the robustness of the proposed turbulence modeling framework.

Reproducing vortex-induced vibrations of rooftop twin-mast by multi-scale coupled simulation of urban wind fields

The Shenzhen Electronics Group (SEG) Plaza in Shenzhen, China, experienced visible vibrations attributed to significant vortex-induced vibrations of the large spacing twin-mast atop the building. The vibration event occurred under complex urban wind environments. In order to reproduce wind fields around SEG Plaza, a multiscale simulation was carried out by integrating the Weather Research and Forecasting (WRF) and Computational Fluid Dynamics (CFD) models. The aerodynamic characteristics and wake modes of twin-mast are investigated by a downscale CFD simulation in detail. Finally, vortex-induced vibration (VIV) of twin-mast was analyzed considering fluid-structure interaction. This work reveals that wind conditions during the SEG Plaza vibration incident were appropriate for the VIV occurrence of twin-mast, and the results of VIV simulation agree well with the field monitoring results by computer vision identification. This study effectively reproduces the full pictures of VIVs of the rooftop twin-mast during this rare event, offering a critical view for the analysis of this event.

Multi-scale simulation of typhoon wind field at building scale utilizing mesoscale model with nested large eddy simulation

Based on the mesoscale WRF (Weather Research Forecast) and LES (Large Eddy Simulation), an integrated numerical simulation framework aiming to resolve typhoon wind fields around urban building blocks has been developed. The holistic multi-scale simulation spans four scales: macroscale (∼1000 km), mesoscale (∼100 km), microscale (∼1 km), and building scale (0.1–100m). By interactively sharing the meteorological data among different scales, this simulation framework addressed the challenge of downscaling typhoon effects in urban environments. Typhoon weather reanalysis and urban wind field simulation were conducted by the WRF model with nested computational domains incorporating high-resolution topography data. For the downscaled LES model with urban buildings, the wind profiles produced by the WRF model were used at the interface with provided turbulent fluctuations in the flow. K11 building is a 270-m-tall building located in a densely built-up area of Hong Kong. Wind velocity and pressure fields surrounding the K11 building in Hong Kong during Typhoon Kammuri were obtained by the proposed framework, and the simulation results were validated by comparing with the meteorological data and full-scale measurements at the top of the K11 building. The interactions of the wind fields and building clusters resulting in vortex shedding, separation, and channeling effects have been resolved at high resolutions. Probabilistic distributions and higher-order statistics of wind pressure, i.e., skewness and kurtosis, were presented to highlight non-Gaussian characteristics of the simulated local wind pressure on the K11 building. Based on the simulated wind pressure, wind-induced vibration of the K11 building during the typhoon was analyzed and compared to the measurements.

Towards urban wind utilization: The spatial characteristics of wind energy in urban areas

Urban wind energy is gaining increasing recognition for its environmentally friendly nature, particularly in the context of energy shortages. Predicting wind speed in urban environments is challenging due to the varying roughness and drag caused by obstacles on the ground. The complexity of urban morphology significantly disrupts the wind field and hampers the utilization of wind energy. This study aimed to investigate the diverse impacts of highly heterogeneous urban environment on the urban wind energy by innovatively establishing spatially varying power law wind profiles. Firstly, long-term LIDAR observations were conducted to measure the vertical wind profile in the study area. Then, a high-resolution LCZ map was created and integrated into the Weather Research and Forecasting (WRF) model to reproduce the urban wind field, which was validated using the observational data. Finally, the spatial characteristics of the urban wind field and wind energy were analyzed based on 3 representative points and the urban LCZ categories. The results show that the average power law exponents of each LCZ classification ranges from 0.29 to 0.75, with the average power law exponent of the high-volume building area being larger than 0.6. The highest wind power density in the study area can reach 343.3W/m2 at 200m height. Further, significant negative correlation coefficients were found between wind power density and building height, as well as building density, with values of approximately −0.6 and −0.35, respectively. Overall, the high-volume-ratio built-up areas with higher roughness had a notable impact on the wind speed by increasing the power law exponents. These spatially varying wind profiles are expected to provide technical support for the utilization of urban wind energy.

Chance-constrained UAM traffic flow optimization with fast disruption recovery under uncertain waypoint occupancy time

Trajectory-based operations offer a promising solution for effective urban air mobility (UAM) traffic management with conflict-free four-dimensional trajectories. However, these trajectories generated in strategic phases by existing methods could be significantly disrupted due to uncertainties such as flight delays and trajectory deviations. This paper develops a chance-constrained UAM traffic flow management (UTFM) optimization model with fast disruption recovery to solve observed disrupted trajectories and improve their resilience. Our model introduces a novel concept of waypoint occupancy time to cope with the probabilistic separation constraints induced by variables, notably urban wind field. We then convert the probabilistic constraint into a deterministic one, incorporating risk-bounded separation guarantees derived from flight experiment data. Furthermore, we develop a hierarchical stochastic search algorithm for solving the redefined deterministic optimization problem. Our comprehensive numerical studies demonstrate the model's effectiveness in restoring disrupted flights and resolving conflicts within seconds. Additionally, our reliability testing showcases the resilience of the model in managing disruptions, even at levels as high as 50%, and its adaptability in addressing varying levels of uncertainty risk. We further demonstrate the ability of the UTFM model to capture tail risks across Gaussian and non-Gaussian uncertainty distributions. Lastly, our scalability analysis highlights the potential capacity of the model to support up to 3,000 flights per hour in Singapore's urban airspace below 400 ft. This study introduces an adaptable framework to facilitate the modelling of robust traffic flow management and performance-based separation for a variety of eVTOL types under uncertainties.

Modeling advanced air mobility aircraft in data-driven reduced order realistic urban winds

The concept of Advanced Air Mobility involves utilizing cutting-edge transportation platforms to transport passengers and cargo efficiently over short distances in urban and suburban areas. However, using simplified atmospheric models for aircraft simulations can prove insufficient for modeling large disturbances impacting low-altitude flight regimes. Due to the complexities of operating in urban environments, realistic wind modeling is necessary to ensure trajectory planning and control design can maintain high levels of safety. In this study, we simulate the dynamic response of a representative advanced air mobility platform operating in wing-borne flight through an urban wind field generated using Large Eddy Simulations (LES) and a wind field created using reduced-order models based on full-order computational solutions. Our findings show that the longitudinal response of the aircraft was not greatly affected by the fidelity of the LES models or if the spatial variation was considered while evaluating the full-order wind model. This is encouraging as it indicates that the full LES generation of the wind field may not be necessary, which decreases the complexity and time needed in this analysis. Differences are present when comparing the lateral response, owing to the differences in the asymmetric loading of the planform in the full and reduced order models. These differences seen in the lateral responses are expected to increase for planforms with smaller wing loadings, which could pose challenges. Additionally, the response of the aircraft to the mean wind field, the temporal average of the full order model, was misrepresentative in the longitudinal response and greatly under-predicted control surface activity, particularly in the lateral response.

Urban wind field prediction based on sparse sensors and physics‐informed graph‐assisted auto‐encoder

AbstractThe urban flow wind field is a critical element for downstream research, such as mitigation of urban wind disasters, assessment of urban wind environment, and urban drone route planning. However, it is impractical to arrange a large number of sensors to monitor an urban wind flow field. Hence, acquiring the entire urban wind flow field via sparse sensors would be highly valuable. To date, no scheme including deep learning (DL) model has been specifically designed for this purpose. This study presents an innovative approach to reconstruct complex high‐resolution urban wind fields based on sparse sensors, using a physics‐informed graph neural network (GNN)‐assisted auto‐encoder. The proposed method leverages the relationship between sensors and their surrounding environment enabled by deep mining capabilities of GNNs. As a result, the utilization and emphasis on sparse sensors data are significantly enhanced. The continuity equation of fluid flow is incorporated into the loss function of the convolution neural network to improve the stability and performance of the model. The findings suggest that, in contrast to prevalent generative DL models, the proposed model yields an approximate 50% reduction in root mean square error for reconstructing high‐resolution urban wind fields for multiple wind attack angles.

A Generalization of Building Clusters in an Urban Wind Field Simulated by CFD

The urban climate has a critical influence on developing sustainable cities, and one important factor is the urban wind environment. Moreover, refining urban wind fields is required for the quantitative assessment of urban wind environments. Computational fluid dynamics (CFD) is a powerful tool for modeling the wind flow characteristics in urban areas. Although CFD has been widely used in various fields, its use for simulating urban wind fields has limitations because of the complexity of urban building models and the high computational workload. Accordingly, we consider the generalization parameters in the vertical and horizontal directions based on the CFD results and the building topology based on the state of the building nodes. We perform a two-dimensional generalization of building clusters, conduct spatial analysis in a geographic information system (GIS), and generate three-dimensional models. This generalization scheme is applied to Meiling Street in Jinjiang City, Fujian Province, China. The results indicate that the generalization decreases the number of buildings from 7003 to 3367 and the computation time from 11 h and 26 min to 10 h and 25 min. The computation efficiency is improved by 8.89%, with 1.85% changes in the average wind speed ratio. This scheme substantially improves the computational efficiency of urban wind field CFD simulations by reducing the geometric model’s complexity without compromising the accuracy. This strategy is suitable for simulating large-scale urban wind fields.

Research on the Characteristics of Urban Building Cluster Wind Field Based on UAV Wind Measurement

An innovative approach for measuring wind fields in urban building clusters using Unmanned Aerial Vehicles (UAVs) is presented. This method captures the distribution of wind fields within clusters. The results indicate that building architecture has a significant influence on wind flow characteristics at 15 m and 25 m height levels. Particularly, areas adjacent to the buildings and the wake section exhibit notable variations in wind speed and turbulence intensity compared to the incoming flow. The regions most affected include the areas flanking the buildings on either side and the intermediate section of the wake. The flow separation and convergence of incoming wind from the windward sides of the buildings notably amplify the wind load, resulting in a significant shift in wind speed and turbulence intensity within pedestrian pathways. The use of UAVs for wind measurements enables a flexible and efficient assessment of urban wind fields. These findings pave the way for further research into wind field measurements in urban architecture and a better understanding of the interference effects of buildings.

Accurate and efficient urban wind prediction at city-scale with memory-scalable graph neural network

The interaction between buildings and wind significantly impacts the comfort and safety of pedestrians, thereby influencing the sustainability of cities. Computational fluid dynamics (CFD) simulation of wind velocity in urban environments provides valuable insights into building aerodynamics. Traditional CFD solvers are limited by high computational costs, hindering practical engineering applications. Graph neural networks (GNNs) have emerged as a promising approach to accelerate CFD simulations on unstructured meshes. However, their inability to handle large-scale urban wind prediction due to high GPU memory requirements poses a challenge, as GNNs rely on GPUs for fast training and inference. To overcome this limitation, we propose SGMS-GNN, a novel GNN model that accurately and efficiently predicts wind velocity fields in urban environments while maintaining consistent GPU memory usage as the simulation domain increases. We employed a validated CFD model to generate a dataset of wind velocity fields in various urban topologies by simulating wind flow through randomly generated building layouts. Our well-generalized SGMS-GNN demonstrates accurate urban wind field predictions at city-scale, achieving a 70 % reduction in GPU memory usage compared to other GNN models. Furthermore, the proposed model outperforms the CFD model on which it is trained by running 1–2 orders of magnitude faster.

The development and validation of the Inhomogeneous Wind Scheme for Urban Street (IWSUS-v1)

Abstract. The layout of urban buildings shows significant heterogeneity, which leads to the significant spatial inhomogeneity of the wind field in and over the canopy of urban street canyons. However, most of the current urban canopy models do not fully consider the heterogeneity of the urban canopy. Large discrepancies thus exist between the wind speeds simulated by the current urban canopy models and those observed in the street canyon. In this study, a parameterization scheme for wind fields, Inhomogeneous Wind Scheme for Urban Street (IWSUS), is developed to better characterize the heterogeneity of the urban canopy. We use a computational fluid dynamics method to generate the IWSUS scheme and compare it with observations of the wind profile and turbulent flux in and over the street canyon for validation. In IWSUS, the wind speed vertical profiles at six representative positions located in a typical street canyon (i.e., the windward or leeward side of a long straight street or the inflow or outflow end) are parameterized separately. The wind profile by IWSUS thus can better describe the horizontal heterogeneity of the urban near-surface wind field, e.g., the dynamic drag effect of buildings in the lower atmospheric layer over the urbanized land use. The validation based on observations shows that the performance of simulation results by IWSUS is better than that by the exponential–logarithmic (exp-log) law widely used in the current urban schemes. We consider typical building arrangement and specific street orientations in IWSUS for wind field simulations, which can better match the distribution characteristics of street canyons around the observation point in the street canyon. The averaged wind profiles and turbulence energy fluxes in the model grids of urban areas by IWSUS are also nearer to the observations than those by the exp-log law. The normalized mean errors (NMEs) between the simulated and the observed vertical average wind speed are 49.0 % for IWSUS and 56.1 % for exp-log law in the range from the ground to 4 times the average height of the buildings and 70 % for IWSUS and 285.8 % for exp-log law in the street canyon (range from the ground to building top). This study proves that the accuracy of simulations of land surface processes and near-ground meteorological processes over the urban canopy can be improved by fully considering the heterogeneity of the urban canopy layout structures and the inhomogeneity of wind field distributions in and over the street canyon. IWSUS is expected to be coupled with mesoscale atmospheric models to improve the accuracy of the wind field, land surface energy budget, meteorological and atmospheric chemistry simulations.

The advancement of research in cool roof: Super cool roof, temperature-adaptive roof and crucial issues of application in cities

Cool roofs play a significant role in mitigating urban heat islands, improving indoor thermal comfort, and saving energy. In recent years, with advances in the manufacturing of nanophotonics and metamaterials, researchers have developed super cool roofs where the surface temperature remains below the air temperature in direct daylight and temperature-adaptive roofs where the solar reflectance or thermal emissivity can change with temperature. This paper reviews the research progress and status of conventional cool roofs, super cool roofs, and temperature-adaptive roofs. This paper affirms the role of cool roofs in mitigating urban heat islands and energy conservation. And this paper also summarizes some of the crucial issues that cool roofs may face when used in cities. The effects of cool roofs on urban wind fields, planetary boundary layer heights, and pollutants above cities as well as the effects of sky view factor, atmospheric humidity, dust, and aging on the performance of cool roofs are discussed. The results show that the use of cool roofs is limited by geography and climate. The net cooling power can reach 150 W/m2 in dry, rainless, and clear sky areas. Cool roof technology is less effective in hot and humid climates because the first atmospheric window is affected to varying degrees by the increased radiation medium in the atmosphere, while the second atmospheric window is nearly closed in hot and humid climates, weakening the terrestrial long-wave radiation entering space. The use of cool roofs in warm and humid climates (over 80% relative humidity, with temperature over 24 °C) for most summer nights may limit the radiative cooling performance of the cool roof. The large-scale use of cool roofs in cities near huge lakes or seas may affect the urban wind field, causing a cooling island effect and a local build-up of pollutants. Finally, an outlook on the research prospects of cool roofs was given to provide ideas for further research.

Diffusion characteristic of air pollutant from district heating source driven by urban wind field and layout optimization

District heating is a major contributor to urban air pollution due to the massive emissions from fossil fuel combustion. This paper investigates the diffusion characteristics of air pollutants from district heating sources (DHS) driven by the urban wind field. Firstly, the urban air quality was simulated using the coupled Weather Research and Forecasting model - Community Multiscale Air Quality model (WRF-CMAQ) based on the actual heating emissions in the study region. the results were validated using observed meteorological and environmental data. Next, the study regions were divided into sub-regions based on the prevailing wind direction during the heating season. numerical experiments were designed by planning the heating emission into sub-regions as optimized layouts for DHS. Finally, the air quality was predicted for different layouts using WRF-CMAQ and assess the output of PM2.5 as the indicator. The findings reveal that moving the DHS close to the suburban areas can significantly improve overall air quality, and the best improvement can be obtained by arranging all DHS within the defined windward or leeward sub-regions. It is expected that these findings will provide guiding principles for planning DHS from the perspective of environmental management.

A Method of Urban Wind Field Visualization Based on Deep Learning

In order to solve the problems of incomplete feature extraction, visual results that disrupt the continuity of the flow field, and unstable clustering resulting in poor streamline representation during urban wind field visualization, a three-dimensional streamline visualization method based on deep learning was proposed. This method consists of two parts: one is streamline feature learning, and the other is clustering method. The Euclidean distance represented by the streamline is used as the similarity between the streamlines for clustering, and the clustering results obtained are weighted and combined before being divided. The method was tested on a real urban wind field dataset and qualitatively compared with existing methods. The results show that this method can better balance the relationship between feature extraction and streamline distribution compared to existing methods.

PIGNN-CFD: A physics-informed graph neural network for rapid predicting urban wind field defined on unstructured mesh

Urban wind field plays an important role in quantitative assessment of urban environment. Compared to field measurement and wind tunnel experiment, Computational Fluid Dynamics (CFD) simulation having the advantages of low cost, repeatability and reliable precision is becoming a common scheme to model flow field of one fixed urban scenario, but still faces the problems of time-consuming computation and lack of scalability for practical engineering application. This paper proposes PIGNN-CFD, a novel physics-informed graph neural network for rapid predicting urban wind field based on irregular unstructured mesh data of CFD simulation. Specifically, a CFD model employing the unsteady Reynolds-Averaged Navier-Stokes (RANS) equations with the standard k-ε turbulence model, is constructed and then numerically solved by OpenFOAM to simulate urban wind field defined on unstructured mesh. After being validated by publicly available wind tunnel test data provided by the Architectural Institute of Japan (AIJ), the proposed CFD model is employed to build the training and test sample sets of urban wind fields by simulating the wind blowing through various randomly generated small-scale urban scenes. A novel physics-informed graph neural network, both approximating the training data and automatically satisfying the RANS equations, is designed and trained to perform wind field inference on unstructured mesh graph, and then scaled up to predict wind fields of arbitrary large-scale urban scenes. The predicted wind field results of two urban environments at different scales show that the well-generalized PIGNN-CFD model runs 1–2 orders of magnitude faster than the CFD model on which it is trained, while obtaining the consistent computational accuracy.

Urban Wind Field Mapping Technique for Municipal Environmental Planning: A Case Study of Cheongju-Si, Korea

The integrated management of land and environmental plans (EPs) has been a key direction of sustainable policy in Korea. Although local EPs should be established based on spatial data, much of the pertinent environmental information has not been mapped. Accordingly, we develop a wind field mapping technique for establishing local EPs. This method was developed via a numerical weather prediction model (Weather Research and Forecast—WRF), and a diagnostic meteorological model (California Meteorological model—CALMET), based on weather observation data, as well as terrain and land cover data. The developed method was applied to a case study in Cheongju-si, Korea, for verifying its effectiveness. The created wind field maps of Cheongju-si provided an intuitive visual representation of the spatial distribution of seasonal and daytime/nighttime wind patterns. Such data helped understand the directionality and patterns of winds blowing into a city, or identify stagnant areas of wind circulation. Overlaying the wind field map with maps of air pollutant emissions revealed that pollutants from industrial areas could flow along the prevailing winds into residential areas downtown. These maps also implied that open spaces, such as parks and grasslands, were recommendable for stagnant areas, instead of creating industrial complexes or residential areas.

Quantitative evaluation of impacts of the steadiness and duration of urban surface wind patterns on air quality

The complexity and heterogeneity of urban land surfaces result in inconsistencies in near-surface winds, which in turn influence the diffusion and dispersion of air pollutants. In this study, we classified urban surface wind fields, quantified their steadiness, duration, and influence on air quality using hourly wind observations from 50 meteorological stations, as well as hourly PM2.5 and NO2 concentrations from 18 monitoring stations during 2017–2018 in Shenzhen, a mega city in southern China. We found that the K-means clustering technique was reliable for distinguishing surface wind patterns within the city. Urban surface-wind patterns greatly affected pollutant concentrations. When dominated by calm, northerly wind, high PM2.5/NO2 concentration episodes occurred more frequently than those during other surface wind patterns. The urban surface transport index (USTI) was used to quantify the steadiness of surface wind classes. High pollutant concentrations were present during both high wind speed periods with a large USTI, indicating external pollutant transport, and during low wind speed periods with a small USTI, indicating pollutant accumulation. The threshold durations for surface wind fields (TDSWF) was proposed to quantify the impacts of surface wind persistence on air quality. We found that poor air quality occurred during the first several hours of a dominant wind pattern, indicating that transitions between wind patterns should be a particular focus when assessing air-quality deterioration. USTI and TDSWF are potentially applicable to other urban areas, owing to their clear definitions and simple calculation. In combination with wind speeds, these indices are likely to improve air quality forecasting and strategic decisions on air pollution emergencies, based on long time series of multiple wind and pollutant concentration observations.

Advances in CFD Modeling of Urban Wind Applied to Aerial Mobility

The feasibility, safety, and efficiency of a drone mission in an urban environment are heavily influenced by atmospheric conditions. However, numerical meteorological models cannot cope with fine-grained grids capturing urban geometries; they are typically tuned for best resolutions ranging from 1 to 10 km. To enable urban air mobility, new now-casting techniques are being developed based on different techniques, such as data assimilation, variational analysis, machine-learning algorithms, and time series analysis. Most of these methods require generating an urban wind field database using CFD codes coupled with the mesoscale models. The quality and accuracy of that database determines the accuracy of the now-casting techniques. This review describes the latest advances in CFD simulations applied to urban wind and the alternatives that exist for the coupling with the mesoscale model. First, the distinct turbulence models are introduced, analyzing their advantages and limitations. Secondly, a study of the meshing is introduced, exploring how it has to be adapted to the characteristics of the urban environment. Then, the several alternatives for the definition of the boundary conditions and the interpolation methods for the initial conditions are described. As a key step, the available order reduction methods applicable to the models are presented, so the size and operability of the wind database can be reduced as much as possible. Finally, the data assimilation techniques and the model validation are presented.