Improving Turbulence Representation in High-Resolution Numerical Weather Prediction

Abstract

Numerical weather prediction (NWP) models are now capable of operating at horizontal resolutions in the 100 m to 1 km range, a grid spacing similar in scale to that of the turbulent eddies present in the atmospheric convective boundary layer (CBL). Known as the ‘grey zone’ of turbulence, this regime is characterized by significant contributions from both the resolved and subgrid components to represent the dominant motions of the system. This study investigates the properties of the grey zone of turbulence, and proposes enhancements to existing methods of turbulence representation. Firstly, a very simple model based on turbulence kinetic energy (TKE) is presented; it characterizes the fundamental nature of CBL turbulence as a balance between thermal buoyancy and dissipation. Leading on from this, the grey zone is investigated using a large-eddy simulation (LES) model. The onset of resolved turbulent motion is identified as a key issue, and after an in-depth analysis of the mechanisms that contribute to this problem, improvements are proposed to offset the effects. These include: 1) a modification of the sub-grid turbulence scheme to allow added scale awareness, thereby adding more control over the dissipation of energy and 2) modification to the perturbations of the potential temperature field at the grid scale. The techniques are capable of significantly improving the timing of convective onset. Following on from the large-eddy simulation study, the grey zone is investigated in the Met Office Unified Model (UM). After an analysis of grey-zone simulations in real case studies, the new techniques are again tested. Although some improvement in convective onset timing and boundary-layer structure is obtained using the techniques, these new methods do not seem to offer a practical advantage over previously implemented approaches. However, an analysis of boundary-layer structures and convective shower distribution does present insight into how perturbations at the grid scale can influence the distribution and timing of these features.