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Time integration of the unsteady Reynolds-averaged Navier–Stokes (URANS) equations is the principal approach used in numerical weather prediction. This approach represents a balanced compromise between accuracy and computational cost. The URANS equations require the flow to be decomposed into an ensemble mean and excursions that are presumed to be entirely related to turbulence, thereby enabling conventional closure schemes to be used to describe their statistics. Implicit in such a decomposition is the assumption of a spectral gap between the unsteadiness in the mean flow and the scales of turbulence. Modelling challenges arise when some of the unresolved fluctuations are related to non-turbulent, structured motions that can also blur the spectral gap and render conventional closure schemes ineffective. This work seeks to clarify modelling issues that occur when unresolved fluctuations include submesoscale motions and persistent secondary circulations related to surface heterogeneities. Because submeso motions and persistent secondary circulations are not random, new theoretical tactics are discussed to represent their effects on URANS transport. By reviewing the interpretation of fluctuating terms in the URANS equations, we suggest the use of large-eddy simulations, direct numerical simulations and field measurements to guide the development of closure schemes that explicitly include fluxes due to submeso motions and persistent secondary circulations. [ABSTRACT FROM AUTHOR]

The lower nocturnal boundary layer is governed by intermittent turbulence which is thought to be triggered by sporadic activity of so-called sub-mesoscale motions in a complex way. We analyze intermittent turbulence based on an assumed relation between the vertical gradients of the sub-mean scales and turbulence kinetic energy. We analyze high-resolution nocturnal eddy-correlation data from 30-m tower collected during the Fluxes over Snow Surfaces II field program. The non-turbulent velocity signal is decomposed using a discrete wavelet transform into three ranges of scales interpreted as the mean, jet and sub-mesoscales. The vertical gradients of the sub-mean scales are estimated using finite differences. The turbulence kinetic energy is modelled as a discrete-time autoregressive process with exogenous variables, where the latter ones are the vertical gradients of the sub-mean scales. The parameters of the discrete model evolve in time depending on the locally-dominant turbulence-production scales. The three regimes with averaged model parameters are estimated using a subspace-clustering algorithm which illustrates a weak bimodal distribution in the energy phase space of turbulence and sub-mesoscale motions for the very stable boundary layer. One mode indicates turbulence modulated by sub-mesoscale motions. Furthermore, intermittent turbulence appears if the sub-mesoscale intensity exceeds 10 % of the mean kinetic energy in strong stratification. [ABSTRACT FROM AUTHOR]