Visibility network analysis of large-scale intermittency in convective surface layer turbulence

TL;DR

This study uses visibility network analysis on experimental data to explore how large-scale intermittency affects temperature and velocity signals in convective turbulence, revealing nonlinear dependencies and interactions.

Contribution

It introduces a network-based approach to analyze the temporal structure of turbulence signals, highlighting differences between temperature and velocity intermittency.

Findings

Temperature intermittency linked to strong nonlinear dependencies.

Velocity signals show a competition between linear and nonlinear effects.

Network measures reveal distinct behaviors in temperature versus velocity signals.

Abstract

Large-scale intermittency is a widely observed phenomenon in convective surface layer turbulence that induces non-Gaussian temperature statistics, while such signature is not observed for velocity signals. Although approaches based on probability density functions have been used so far, those are not able to explain to what extent the signals' temporal structure impacts the statistical characteristics of the velocity and temperature fluctuations. To tackle this issue, a visibility network analysis is carried out on a field-experimental dataset from a convective atmospheric surface layer flow. Through surrogate data and network-based measures, we demonstrate that the temperature intermittency is related to strong non-linear dependencies in the temperature signals. Conversely, a competition between linear and non-linear effects tends to inhibit the temperature-like intermittency behaviour in streamwise and vertical velocities. Based on present findings, new research avenues are likely to be opened up in studying large-scale intermittency in convective turbulence.