###### Abstract
For a mesoscale meteorological simulation, they used the Weather Research and Forecasting (WRF) model in a one-way nested configuration downscaling over four computational domains from 4.5 km to 60 m horizontal resolution. The horizontal grid spacing of their CFD simulation was 20 m. The results showed that the variations of gust and mean wind speeds within the urban area were well represented in the LES. Using a similar coupling approach to the WRF model with CFD simulations, [PERSON] et al. [16] and [PERSON] et al. [17] simulated the airflow over complex terrain using Reynolds-averaged Navier-Stokes (RANS) turbulence models. [PERSON] et al. [10] reproduced the strong winds associated with Typhoon Nuri on 22 August 2008 using a mesoscale model centred on the Hong Kong International Airport. Then, by coupling the mesoscale model with a CFD simulation, they investigated the effects of local topography, hangers and buildings on the significant change in the wind speed observed between the north and south runways from strong wind shear that caused a significant change in the crosswind. For the mesoscale models, the Global Spectral Model of the Japan Meteorological Agency (100 km), the Regional Spectral Model (20 km), and Regional Atmospheric Modelling System (4 km, 1.333 km, and 0.444 km) were nested. The 0.444-km mesoscale outputs were used in a CFD simulation using a RANS approach. The CFD results showed that the buildings were the main contributor to the wind speed change, which agreed with the observational data. [PERSON] et al. [9] investigated the wind and temperature fields in a dense urban area of Zurich, Switzerland during heatwaves, by coupling a regional atmospheric model, COnsortium for Small-scale Modelling (COSMO) [18], with a microscale CFD model utilising the \(k-\varepsilon\) unsteady RANS approach. The validation study against a one-point measurement (identified as the limitation of the validation study by the authors) demonstrated that their coupling approach successfully estimated the diurnal temperature with small errors, while the errors in the wind speed and direction predictions were higher due to the effect of unsteady buoyant flows induced by hot buildings and water bodies, which were captured by the CFD model and not by COSMO. [PERSON] et al. [19] studied the changes in wind speed and direction in an urban area located in Pescara, Italy, by coupling mesoscale and microscale models. Mesoscale ModelFifth Generation (MM5) [20] was used to develop a one-way nested mesoscale model using five domains with the coarsest and finest horizontal resolutions of 32.4 km and 0.4 km, respectively. The commercial software STAR-CCM+ [21] with the \(k-\varepsilon\) unsteady RANS approach was used for the microscale model to investigate wind pattern in two cases: (1) only the topography of the terrain was used; (2) the buildings were added to the model. The results demonstrated the highly localised effects of buildings on wind patterns in urban areas. Several researchers, e.g., [6; 7; 12; 22], have investigated the wind field and pollutant dispersion patterns in densely built-up urban areas by coupling NWP and microscale CFD simulations. To study the airflow and pollutant dispersion in Seoul, Korea, [PERSON] et al. [6] set up four one-way nested computational domains using the mesoscale MM5 model with the finest horizontal resolution of 1 km. The 1-km NWP outputs provided time-varying boundary conditions for a CFD simulation with a 9.72-m horizontal grid interval, utilising the RANS \(k-\varepsilon\) turbulence model. It was demonstrated that, given the complicated patterns of airflow and pollutant patterns in the presence of urban elements and building clusters, time-varying boundary conditions should be used to better estimate the urban flow and dispersion. Then, an NWP/CFD coupled model can reliably predict these patterns. The limitations, however, of this study were outlined by the authors as not considering the surface temperature in the CFD model and the relatively low resolution of the mesoscale model (1 km).