The Onset of Resolved Boundary-Layer Turbulence at Grey-Zone Resolutions

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 examines how the initiation of resolved turbulence-a concept commonly referred to as 'spin-up'-can be delayed during the evolution of a simulated CBL in the grey zone. We identify the importance of imposed pseudo-random perturbations of potential temperature (θ) for the development of the resolved fields showing that without such perturbations, resolved turbulence does not become established at all. When the perturbations are organized, spin-up can develop more rapidly, and we find that the earliest spin-up times can be achieved by applying an idealized profile of variance to derive the θ perturbation values. The perturbation structures are shown to be most effective when applied at intervals following the mixed-layer time scale, t * , rather than perturbing only at the initial time. We also propose a modification to the three-dimensional Smagorinsky turbulence closure, in which the Smagorinsky constant is replaced by a scale-dependent coefficient. Both the approaches of: (1) applying structured θ perturbations, and (2) using a dynamically-evolving Smagorinsky coefficient are shown to encourage faster spin-up independently of each other, but the best results clearly emerge when the two methods are applied concurrently.

Probing the ‘grey zone’ of NWP – is higher resolution always better?

“…In Kealy et al (), we found that the manner of imposing the potential temperature perturbations makes a large impact on the CBL evolution in the grey zone. Wondering if a similar effect would appear in a real case, we ran the UM at both 1.5km (the vicinity of the turbulence mesoscale limit) and 400m (deep in the turbulence grey zone), both with the perturbations applied and without them.…”

“…Similar to Figure , but showing the Kealy et al () method of accelerating spin‐up. The ‘CsCoMLS’ simulation features the addition of structured perturbations to the potential temperature, along with assigning a dependence on boundary‐layer depth to the diffusivity.…”

“…Figure presents an example of the spin‐up issue that the grey zone creates. Adapted from our work in Kealy et al (), the figure shows a timeseries of turbulence kinetic energy (TKE) for the evolving CBL during day 33 of the Wangara experiment, a well‐studied Australian boundary‐layer field campaign. TKE is a quantity derived from the variability in the momentum fields.…”

The future of forecasting for renewable energy

“…Incorrectly representing them in a model could, for example, result in systematic errors in wind speed or cloud cover, amongst other things. Careful perturbations of turbulence need to be introduced into the model to allow proper treatment of turbulence (Kealy, Efstathiou, & Beare, 2019). Radar data can be used to derive turbulence statistics for evaluating better parameterization of turbulence in NWP models (Feist et al, 2019).…”

Simulating Real Atmospheric Boundary Layers at Gray-Zone Resolutions: How Do Currently Available Turbulence Parameterizations Perform?

“…Modeling SGS turbulence in the gray zone without missing or double counting turbulent energy is a relatively recent topic of research that still lacks robust and validated solutions [9][10][11][12][13]. For grid spacings larger than the gray-zone upper limit, traditional mesoscale modeling approaches can be used to model the impacts of SGS turbulence on the resolved fields.…”