Analyzing the footprints of near-surface aqueous turbulence: An image processing-based approach

[1] In this contribution, a detailed investigation of surface thermal patterns on the water surface is presented, with wind speeds ranging from 1 to 7 m s–1 and various surface conditions. Distinct structures can be observed on the surface—small-scale short-lived structures termed fish scales and larger-scale cold streaks that are consistent with the footprints of Langmuir circulations. The structure of the surface heat pattern depends strongly on wind-induced stress. Consistent behavior regarding the spacing of cold streaks can be observed in a range of laboratory facilities when expressed as a function of water-sided friction velocity, u*. This behavior systematically decreased until a point of saturation at u* = 0.7 cm/s. We present a new image processing-based approach to the analysis of the spacing of cold streaks based on a machine learning approach to classify the thermal footprints of near-surface turbulence. Comparison is made with studies of Langmuir circulation and the following key points are found. Results suggest a saturation in the tangential stress, anticipating that similar behavior will be observed in the open ocean. A relation to Langmuir numbers shows that thermal footprints in infrared images are consistent with Langmuir circulations and depend strongly on wind wave conditions.