Abstract: Large-eddy simulations (LESS) are frequently used to model the planetary boundary layer, and the choice of the grid cell size, numerical schemes and sub grid model can significantly influence the simulation results. In the present paper the impact of grid spacing on LES of an idealized atmospheric convective boundary layer (CBL), for which the statistics and flow structures are well understood, is assessed for two mesoscale models: the Regional Atmospheric Modeling System (RAMS) and the Weather Research and Forecasting model (WRF). Nine simulations are performed on a fixed computational domain (6 x 6 x 2 km), combining three different horizontal (120, 60, 30 m) and vertical (20, 10, 5 m) spacings. The impact of the cell size on the CBL is investigated by comparing turbulence statistics and velocity spectra. The results demonstrate that both WRF and RAMS can perform LES of the CBL under consideration without requiring extremely high computational loads, but they also indicate the importance of adopting a computational grid that is adequate for the numerical schemes and subgrid models used. In both RAMS and WRF a horizontal cell size of 30 m is required to obtain a suitable turbulence reproduction throughout the CBL height. Considering the vertical grid spacing, WRF produced similar results for all the three tested values, while in RAMS it should be ensured that the aspect ratio of the cells does not exceed a value of 3. The two models were found to behave differently in function of the grid resolution, and they have different shortcomings in their prediction of CBL turbulence. WRF exhibits enhanced damping at the smallest scales, while RAMS is prone to the appearance of spurious fluctuations in the flow when the grid aspect ratio is too high. (C) 2015 Elsevier Ltd. All rights reserved.

Abstract: Reynolds-averaged Navier-Stokes (RANS) simulations are often used in the wind engineering practice for the analysis of turbulent bluff body flows. An approach that allows identifying the uncertainty related to the use of reduced-order turbulence models in RANS simulations would significantly increase the confidence in the use of simulation results as a basis for design decisions. In the present study we apply a strategy that enables quantifying these uncertainties by introducing perturbations in the Reynolds stress tensor to simulations of the flow in downtown Oklahoma City. The method is combined with a framework to quantify uncertainties in the inflow wind direction and intensity, and the final result of the UQ approach is compared to field measurement data for the velocity at 13 locations in the downtown area. (C) 2015 Elsevier Ltd. All rights reserved.