Classes of structures in the stable atmospheric boundary layer

This article analyses ubiquitous flow structures which affect the dynamics of stable atmospheric boundary layers. These structures introduce non-stationarity and intermittency to turbulent mixing, thus invalidating the usual scaling laws and numerical model parametrizations, but their characteristics and generating mechanisms are still generally unknown. Detecting these unknown events from time series requires techniques that do not assume particular geometries or amplitudes of the flow structures. We use a recently developed such method with some modifications to study the night-time structures over a three-month period during the FLOSSII experiment. The structures cover about 26% of the dataset, and can be categorized using clustering into only three classes with similar characteristics. The largest class, including about 50% of the events, contains smooth structures, often with wave-like shapes, which occur in stronger winds and weak stability. The second class, including sharper structures with large kurtosis, is characterized by weaker winds and stronger stability. The smallest class, including about 20% of the events, contains predominantly sharp step-like structures, or microfronts. They occur in the weakest winds with strong stability. Sharper, and particularly shallower, structures are related to transient low-level wind maxima which create inflection points and may affect generation of turbulence. Furthermore, large wind directional shear, which is another source of transient inflection points, is generated even by deep coherent structures when the background wind is weaker than the structure intensity. These results show that the complexity of structures can be reduced for the purpose of further analysis using a proper classification. Mapping common characteristics of such events leads to their better understanding, which, if combined with similar analyses of other boundary-layer data, could lead to improving their effects in numerical models.