Linear Error Dynamics for Turbulent Flow in Urban Street Canyons

The ability to make forecasts depends on atmospheric predictability and the growth of errors. It has recently been shown that the predictability of urban boundary layers differs in important respects from that of the free atmosphere on the mesoscale and larger; in particular, nonlinearity may play a less prominent role in the error evolution. This paper investigates the applicability of linear theory to the error evolution in turbulent street-canyon flow. Using large-eddy simulation, streamwise aspect ratios between 0.15 and 1.50, and identical-twin experiments, it is shown that the growth rate of the error kinetic energy can be estimated from Eulerian averages and that linear theory provides insight into the spatial structure of the error field after saturation. The results should be applicable to cities with deep and closely spaced canyons. Implications for data assimilation and modeling are discussed.