Use of thermal signal for the investigation of near-surface turbulence

Organised motion of air in the roughness sublayer of the atmosphere was investigated using novel temperature sensing and data science methods. Despite accuracy drawbacks, current fibre-optic distributed temperature sensing (DTS) and thermal imaging (TIR) instruments offer frequent, moderately precise and highly localised observations of thermal signal in a domain geometry suitable for micrometeorological applications near the surface. The goal of this study was to combine DTS and TIR for the investigation of temperature and wind field statistics. Horizontal and vertical cross-sections allowed a tomographic investigation of the spanwise and streamwise evolution of organised motion, opening avenues for analysis without assumptions on scale relationships. Events in the temperature signal on the order of seconds to minutes could be identified, localised, and classified using signal decomposition and machine learning techniques. However, small-scale turbulence patterns at the surface appeared difficult to resolve due to the heterogeneity of the thermal properties of the vegetation canopy, which are not immediately evident visually. The results highlight a need for physics-aware data science techniques that treat scale and shape of temperature structures in combination, rather than as separate features.

Automated summary

This study investigated organised motion of air in the roughness sublayer of the atmosphere using novel temperature sensing and data science methods. Despite drawbacks in accuracy, current fibre-optic distributed temperature sensing (DTS) and thermal imaging (TIR) instruments provide frequent, moderately precise and highly localised observations of thermal signal. The results highlight the need for physics-aware data science techniques that consider both the scale and shape of temperature structures.