Statistical Investigation of Flow Structures in Different Regimes of the Stable Boundary Layer

Abstract

A combination of methods originating from non-stationary timeseries analysis is applied to two datasets of near surface turbulence in order to gain insights on the non-stationary enhancement mechanism of intermittent turbulence in the stable atmospheric boundary layer (SBL). We identify regimes of SBL turbulence for which the range of timescales of turbulence and submeso motions, and hence their scale separation (or lack of separation) differs. Ubiquitous flow structures, or events, are extracted from the turbulence data in each flow regime. We relate flow regimes characterised by very stable stratification but different scales activity to a signature of flow structures thought to be submeso motions.

Small-Scale Variability in the Nocturnal Boundary Layer

“…Cold air can originate over colder surfaces related to even weak heterogeneity of the soil and vegetation (Van de Wiel et al 2002) or may be generated in cloud-free areas embedded within a general cloud cover and then advect over adjacent surfaces. Vercauteren et al (2016Vercauteren et al ( , 2019 find that stable conditions can be partitioned into subclasses based on whether or not submeso motions are significant and generate significant turbulence. Stable conditions can also be subdivided based on whether or not the submeso horizontal motions are separated in scale from the largest turbulence eddies (partial spectral gap).…”

“…However, in the deeper, less stable boundary layer, they find two subclasses, one with significant organized submeso variation of temperature and one without such structure. Acevedo et al (2014), Vercauteren et al (2019), and others have emphasized that the nature of the submeso motions varies between sites. The unpredictable variation of temperature and wind speed and direction on small time scales and the site differences are of considerable practical interest with respect to dispersion, frost occurrence and fog forecasting (Izett et al 2018).…”

Detecting Regime Transitions of the Nocturnal and Polar Near-Surface Temperature Inversion

“…The reduced vertical mixing results in a decoupling from the surface, such that similarity theory breaks down (Acevedo et al 2015). Intermittent bursts tend to be responsible for most of the turbulent transport (Acevedo et al 2006;Vercauteren et al 2016). Such bursts alter the temperature inversion and can sometimes drive transitions from strongly to weakly stable boundary layers.…”

Time–space variations of temperature in the nocturnal boundary layer

“…Such temperature changes are of practical interest in terms of short-term prediction of frost, ice, and fog formation (Steeneveld et al, 2015;Izett et al, 2018) or in terms of other temperature thresholds, such as in the definition of chilling hours (Jiménez et al, 2020). Temperature variations due to nonturbulent motions on the scale of an hour or less are caused by a complex variety of submesoscale (submeso) motions (Acevedo et al, 2014;Vercauteren et al, 2019b) that include microfronts and wind direction shifts (Lang et al, 2018), internal gravity waves (Viana et al, 2010;Sun et al, 2015b), nearly horizontal two-dimensional modes (Anfossi et al, 2005;Mortarini et al, 2016;Cava et al, 2017), locally generated large-scale structures (Ansorge and Mellado, 2014), and more complex modes without names. These motions may occur simultaneously and perturb the local flow collectively (e.g., Sun et al, 2015a;Vercauteren et al, 2016;Cava et al, 2019b;Stefanello et al, 2020).…”

“…This analysis showed that the temporary demise of the cold pools sometimes proceeded quickly in terms of surface winds but more slowly in terms of warming. Acevedo et al (2014), Vercauteren et al (2019b), and others have emphasized that the nature of the submeso motions varies between sites, apparently due to surface heterogeneity and terrain, even if weak. In addition, even gentle topography can induce standing waves and horizontal variations of the flow (Steeneveld et al, 2008).…”