Understanding the Interactions between Atmospheric Boundary Layers and Urban Landscapes through Large-Eddy Simulations: Impacts on Low-Level Winds and Turbulence

The recent emergence of unmanned aerial system (UAS) and urban air mobility (UAM), the new frontiers in air transportation operating at low altitudes, is expanding the scope of aviation turbulence forecasting system from ensemble-mean to eddy-resolving forecasts (Muñoz-Esparza et al. 2018). Despite its tangible benefits, the operational turbulence-resolving forecasting at scales of 10-100 m is still hard to achieve due to its high computational costs, and resolving the smaller-scale urban landscape for UAM applications poses additional challenges. In this study, we explore the impacts of the urban landscape on low-level turbulence and the associated implications of weather at the micro scales using the GPU-accelerated large-eddy simulation FastEddy model (Sauer et al., 2019; Muñoz-Esparza et al., 2019).

We first identify the minimum resolution required to simulate urban winds and turbulence at an acceptable level of fidelity while minimizing the computational expense, by performing simulations of turbulent flow around an isolated cubic building for a neutral boundary layer case. The model resolution (Δ) is changed by varying the number of grid points across the length of the cubic building (H); i.e., N = Δ/H, and for a fixed value of H (H = 120 m), we test N ranging from 60 to 2. The two highest-resolution simulations (N = 60 and N = 24) reproduce well the overall effects of an isolated building to the turbulent flow, and the comparison of the all simulations indicates that the model resolution needs to be between 6 and 12 grid points per building to capture the urban landscape impact on turbulence (e.g., temporal variations of winds around the building, energy spectra in the wake region, and etc.). We then investigate the interplay between building size and the integral length scale of atmospheric turbulence (L_turb). This is accomplished by testing different configurations of building sizes and turbulence characteristics resulting in H/L_turb ranging from 0.1 to 3. It is demonstrated that the impact of the urban landscape decreases with H/L_turb, indicating the importance of the background atmospheric turbulence (i.e., weather conditions) for accurate turbulence predictions in urban environments.

We further extend our analysis on the urban landscape impact by performing simulations of downtown Dallas area. The realistic three-dimensional building information is generated from Dallas Lidar dataset and building footprints, provided by the Texas Natural Resource Information System and the City of Dallas GIS Department, respectively. The weather dataset, which provides the atmospheric boundary layer forcing conditions, is generated from a series of 48-hour weather forecasts centered at downtown Dallas using the Weather Research and Forecasting model. Detailed analysis of the urban-resolving simulations will be presented, highlighting the impact of characteristic weather patterns on wind and turbulence distributions within the urban landscape.