Dynamic modelling of sea-surface roughness for large-eddy simulation of wind over ocean wavefield

Abstract

AbstractWind blowing over the ocean surface can be treated as a turbulent boundary layer over a multiscale rough surface with moving roughness elements, the waves. Large-eddy simulation (LES) of such flows is challenging because LES resolves wind–wave interactions only down to the grid scale,$\Delta $, while the effects of subgrid-scale (SGS) waves on the wind need to be modelled. Usually, a surface-layer model based on the law of the wall is used; but the surface roughness has been known to depend on the local wind and wave conditions and is difficult to parameterize. In this study, a dynamic model for the SGS sea-surface roughness is developed, with the roughness corresponding to the SGS waves expressed as${\alpha }_{w} \hspace{0.167em} { \sigma }_{\eta }^{\Delta } $. Here,${ \sigma }_{\eta }^{\Delta } $is the effective amplitude of the SGS waves, modelled as a weighted integral of the SGS wave spectrum based on the geometric and kinematic properties of the waves for which five candidate expressions are examined. Moreover,${\alpha }_{w} $is an unknown dimensionless model coefficient determined dynamically based on the first-principles constraint that the total surface drag force or average surface stress must be independent of the LES filter scale$\Delta $. The feasibility and consistency of the dynamic sea-surface roughness models are assessed bya prioritests using data from high-resolution LES with near-surface resolution, appropriately filtered. Also, these data are used fora posterioritests of the dynamic sea-surface roughness models in LES with near-surface modelling. It is found that the dynamic modelling approach can successfully capture the effects of SGS waves on the wind turbulence withoutad hocprescription of the model parameter${\alpha }_{w} $. Also, for${ \sigma }_{\eta }^{\Delta } $, a model based on the kinematics of wind–wave relative motion achieves the best performance among the five candidate models.