Stably Stratified Atmospheric Boundary Layers: Event Detection and Classification for Turbulent Time Series

Abstract

The atmospheric boundary layer is the lowest part of the atmosphere where life takes place. For understanding weather patterns affecting human lives a good understanding of the dynamics in this layer is required. One important process in the atmospheric boundary layer is turbulence. Decisions that affect human life must be made daily based on predictions of turbulent flows. Hence it is fundamental to understand the physical processes behind it. But so far there are still many processes which are not fully understood and cannot be statistically modelled. This is especially true for the stably stratified atmospheric boundary layer which typically occurs during the night or above cooler surfaces like glaciers. Apart from turbulence it includes small scale non turbulent motions of complex origins that are poorly understood by the scientific community. As stated in the paper by Vercauteren and Klein (2015), the presence of such motions could affect turbulent mixing to a large extent if the thermal stratification is very high. Usual approaches which aim to recognize events in the atmospheric boundary layer assume certain physical processes and then search for a trace of these in the atmospheric time series. This can be acomplished by searching for certain geometries or large amplitudes. However, many events in atmospheric series result from yet unidentified physical processes. Consequently a new approach is necessary. A statistical method was recently developed by Kang et al. (2015a) to detect events in noisy time series. This method does not assume any underlying physical processes to extract events from the time series. Nevertheless, physical mechanisms can be investigated a posteriori by analyzing the extracted events. In this thesis we will analyse parts of the Snow-Horizontal Array Turbulence Study (SnoHATS) dataset (Bou-Zeid et al. (2010)) which was collected over a glacier by using a slightly modified version of this event detection method. In their analysis, Kang et al. (2015) investigated events of a certain scale by defining a maximal duration of events, and filtering out small-scale variability. In this thesis, we will investigate different scales of motion by applying the method for multiple timescales. We will thereby test the sensitivity of the method to technical criteria related to timescales of variability. In this thesis we will focus on scales from 1 to 30 minutes. The rationale behind this choice of time window is that motions on scales between 1 and 30 minutes in stable conditions are typically dominated by wavelike motions, microfronts and other complex structures of unknown origin (Mahrt, (2011)). We will refer to such motions as submesomotions in the rest of the thesis. In chapter 2 the concept of turbulence in the atmospheric boundary layer is explained in more detail by first stating how we define turbulence and then illustrating the origin of turbulence in the atmospheric boundary layer. Afterwards, in chapter 3, the dataset is described and in chapter 4 the steps in the event detection method by Kang et al. (2015a) are explained. The multiscale approach and the results from the event detection procedure are described in chapter 5.