Computational Fluid Dynamics is increasingly used to design buildings and cities for optimal pedestrian wind comfort, air quality, thermal comfort, energy efficiency, and resiliency to extreme wind events. The large natural variability and complex physics that are characteristic of these flow problems can compromise the accuracy of the simulation results, thereby hindering their use in the design process. In this talk I will show how uncertainty quantification and data assimilation can be leveraged to evaluate and improve the predictive capabilities of Reynolds-averaged Navier-Stokes simulations for urban flow and dispersion. I will focus on quantifying inflow and turbulence model form uncertainties for two different urban environments: Oklahoma City and Stanford's campus. For both test cases, the predictive capabilities of the models will be evaluated by comparing the model results to field measurements.