## 1. Introduction

[2] Basic shapes of coherent structures in turbulent flows frequently include sharp edges or zones of semi-discontinuities [e.g., *Hunt et al.*, 2010] as often viewed in terms of ramp-cliff structures of passive scalars [*Wilczak*, 1984; *Warhaft*, 2000; *Shraiman and Siggia*, 2000] and microfronts [e.g., *Mahrt*, 1991]. These features were reported as early as *Taylor* [1958] for temperature in the atmospheric boundary layer (ABL) and attributed to convective plumes. Later laboratory and numerical studies showed that these structures can occur irrespective of the sign of thermal stability [e.g., *Mestayer et al.*, 1976; *Gibson et al.*, 1977; *Warhaft*, 2000, and references therein], without the presence of a boundary [e.g., *Gibson et al.*, 1977; *Wroblewski et al.*, 2007], and even appear in two-dimensional random Gaussian velocity fields [*Holzer and Siggia*, 1994]. *Shraiman and Siggia* [2000] and others therefore suggest that the statistics of the scalar turbulence can be decoupled from the underlying velocity field, and as such the scalar intermittency, defined as the departure from a gaussian distribution with the excess of large bursts at smaller scales, is inherent to the mixing process itself. Due to these characteristics, the scalar turbulence yielded to a simpler theoretical approach [*Shraiman and Siggia*, 2000]. However, it seems that the structure of turbulent velocity fluctuations is less amenable to such a treatment.

[3] Events or coherent structures, such as ramp-cliff patterns discussed above, appear to dominate atmospheric turbulent flows in canopies due to the canopy-induced inflection point instability [e.g., *Finnigan*, 2000]. It has recently been proposed that these events render the flow “more deterministic”, in a sense that their presence reduces the embedding dimensions of underlying phase space attractors [e.g., *Campanharo et al.*, 2008]. In this manner, the otherwise highly complex atmospheric turbulent flows start behaving as lower-dimensional less chaotic systems [e.g., *Wesson et al.*, 2003]. In addition, there are many different physical processes in the atmosphere, often of abrupt nature, that force, modify and coexist with the turbulence over a broad range of scales [e.g., *Belušić and Mahrt*, 2008], including atmospheric gravity waves, thermally induced mesoscale flows and so forth. Therefore, the event-like features are ubiquitous in the atmosphere at all scales and significantly contribute to flow properties, such as transport of scalars and generation of smaller scale turbulence [e.g., *Sun et al.*, 2004; *Vindel and Yagüe*, 2011].

[4] The range of scales just larger than the three-dimensional turbulence, sometimes termed submesoscales, appears to be the least understood aspect of the ABL, particularly in the weak-wind stable ABL. There the flow is evidently composed of events with a wide range of amplitudes [e.g., *Mahrt*, 2011]. While large-amplitude well defined events are more often examined [e.g., *Sun et al.*, 2002; *Viana et al.*, 2009], the results are not easily extendable to common weaker events. Therefore, the origin and nature of these motions is at this point unknown. The most probable scenario is that we are witnessing a melange of gravity waves, drainage flows, shear instabilities, solitons and other more complex modes.

[5] Simple visual inspections of various ABL time series reveal that common shapes appear on a large range of timescales, from the smallest turbulence scales up to tens of hours. These shapes may be of any amplitude, but are of surprisingly similar geometries across the scales in spite of substantial change of physics with scale. This provokes another question: Is it possible to distinguish between different scales, having time series with equal number of points, but not knowing the sampling rate? An answer is indicated in randomly chosen examples in Figure 1, where an untrained eye cannot distinguish between the shapes on different scales.

[6] The purpose of this study is to examine the dependence of different flow geometries on stability and timescale, as well as document structural shapes and their orientations in the time series. Usually, the quest for coherent structures is limited to some expected timescales (such as 1 min) and to large amplitudes [e.g., *Gao et al.*, 1989; *Krusche and De Oliveira*, 2004; *Barthlott et al.*, 2007]. In this study, we apply neither restriction. In contrast to previous studies, we allow a very large range of timescales, here between 3 s and 2 h. The large-amplitude limit is relaxed by not using variance-based methods, but instead simple linear correlation (section 2). This study is intended as a preliminary survey in order to gain information and determine future avenues for in-depth analyses.