Investigating the intermittency of turbulence in the stable atmospheric boundary layer - a field data and stochastic modeling approach

Intermittent turbulence is a common feature of stably stratified atmospheric flows, yet its modeling is still problematic. Mesoscale motions such as gravity waves, Kelvin Helmholtz instabilities or density currents may trigger intermittent turbulence and greatly complicate the modeling and measurements of the stable boundary layer (SBL). In this study we investigate the intermittency of turbulence in very stable conditions by applying new statistical analysis tools to the existing SnoHATS dataset, collected in Switzerland over the Glacier de la Plaine Morte in 2006. These tools could then be used to develop stochastic parameterization for the SBL for use in weather or climate models. The SnoHATS dataset includes measurements of atmospheric turbulence collected by horizontal arrays of sonic anemometers. This study applies timeseries analysis tools developed for meteorological data to analyze the SnoHATS dataset with a new perspective. Turbulence in very stable conditions exhibits intermittency, and there is interplay between larger scale atmospheric flow features (at the so-called submesoscales) and onset of turbulence. We investigate the use of statistical tools such as hidden Markov models (HMM) and nonstationary multivariate autoregressive factor models (VARX) as a way to define the interactions between lower frequency modes and turbulence modes. The statistical techniques allow for separation of the data according to metastable states, such as quiet and turbulent periods in the stratified atmosphere.