Identification of Non-Stationary Turbulent Events for Different Regimes of Stable Boundary Layer Turbulence

Stably stratified atmospheric flows are home to many complex non-turbulent motions that can exhibit structures such as ramp-cliff patterns, waves or microfronts. These motions are commonly denoted as submesomotions. The scientific community clearly lacks understanding of the origin and dynamics of these motions and of the extent to which they affect turbulent mixing in the stable boundary layer (SBL). Classical modeling approaches fail to reproduce turbulent dissipation in this context. We will show results of a combination of two advanced statistical approaches applied to near-surface SBL turbulence data. There is evidence from turbulence measurements records that much of the lower frequency variability of velocity and temperature fluctuations is summarized through a few patterns. The turbulent event detection (TED) method is one of the statistical techniques considered here and allows for classification of flow motions into these different observed patterns. The second technique allows for a clustering of turbulence data into periods with different influence of non-turbulent motions on the turbulent fluctuations of the vertical wind velocity. By combining the two approaches, we will show that we are able to separate regimes of SBL turbulence that have different frequencies of occurrence of non-turbulent motions. We identify two regimes of weak-wind SBL turbulence for which the range of timescales of turbulence and submesomotions, and hence their scale separation (or lack of separation) differs. We will investigate if individual structures in each identified regime of SBL turbulence differ despite similar wind and stability conditions.