Some of the strongest challenges for numerical weather prediction come from the unsteady nature of certain flows, in particular nocturnal and stable boundary layers. Strong winds generate sufficient turbulence to overcome buoyant damping, leading to continuous turbulence well described by classical similarity theory. In weaker winds, however, turbulence often has a more sporadic character, being interrupted by non-turbulent flows such as internal gravity waves, density currents or wind gusts. Numerical models represent such flows poorly.
Some efforts to characterise these intermittent flows have employed statistical clustering techniques. This approach has successfully identified periods in which active non-turbulent motions modulate the turbulent dynamics, inducing very stable flows. Stable temperature stratification also affects the structure of turbulence, often introducing strong anisotropies into the flow. Some experimental evidence indicates that the relaxation of such anisotropic turbulence back toward isotropy is not linearly proportional to the degree of anisotropy, as some classical models suggest, but follows more complex, non-linear dependences on the anisotropy tensor.
In a new paper, LML External Fellow Davide Faranda and colleagues focus on several key questions concerning the evolution of anisotropic flow states, in particular by considering invariants linked to topological properties. This allows the investigation of potential preferred directions for the evolution of turbulent states of anisotropy. The paper explores how persistent different states of anisotropy are, and also whether, in stably stratified conditions, there are preferred trajectories in the anisotropy phase space. The analysis rests on turbulence measurements from the Fluxes Over Snow Surfaces II campaign (FLOSSII), and offers results which can be used to improve the representation of non-stationary turbulence under the influence of sub-mesoscale motions.
A pre-print of the paper is available at https://arxiv.org/abs/1809.07031