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

Abstract

Abstract. Organized motions in the roughness sub-layer were investigated using novel temperature sensing and data science methods. Despite accuracy draw backs, current fibre-optic temperature (DTS) and thermal imaging (TIR) instruments offer frequent, moderately precise and highly localized observations of thermal signal in a domain geometry suitable for micro-meteorological applications near the surface. 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. Variance events on the order of seconds to minutes could be identified, localized, 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 with the naked eye. The results highlight a need for physics-aware data sciences techniques that treat scale and shape of temperature structures in combination, rather than as separate features.