A general theory for the characterization of submeso‐scale motions and turbulence in the atmospheric surface layer. (17th November 2020)

• Record Type: Journal Article
• Authors: Urbancic, Gabin H.; Suomi, Irene; Vihma, Timo
• Is Part Of: Quarterly journal of the Royal Meteorological Society. Volume 147:Number 734(2021)
• Page Start: 660
• Page End: 678
• Publication Date: 2020-11-17
• Subjects: atmospheric surface layer -- atmospheric turbulence -- stable boundary layers -- submeso‐scale motions -- wind meandering; Meteorology -- Periodicals; 551.5
• DOI: 10.1002/qj.3939
• Languages: English
• ISSNs: 0035-9009

Abstract

Abstract: Oscillations in wind speed and temperature are commonly observed in the atmospheric surface layer due to the ubiquitous presence of submeso‐scale motions (SSMs). These motions are of particular importance during stable and low‐wind conditions as their amplitudes are large compared with the background turbulence. We characterize both SSMs and turbulence by utilizing a universal feature of the Eulerian autocorrelation function (EAF) called the negative lobe. The negative lobe is the first negative minimum in the EAF and occurs when an oscillation is present in the signal. A novel analytic formulation of the EAF allows for the representation of both turbulence and SSMs over all atmospheric conditions. This is accomplished by taking into account the separation of scales between turbulence and SSMs. In addition to SSMs during stable conditions and for weak winds, large‐amplitude oscillations are also observed during moderate to high winds. These oscillations are caused by large‐scale turbulent structures known as longitudinal streaks and are correctly represented by the new theory. Due to the general applicability of the new theory, it is possible to develop a new and improved understanding of SSMs and turbulence. Abstract : The time series of (a) an oscillation with constant amplitude and decreasing frequency (black) and an oscillation with constant frequency and decaying amplitude (red), and (b) the autocorrelation of the signals in (a).