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

• DOI: 10.1002/jgrc.20102
• Publication Date: 2013-04-23T12:33:49Z
• Authors: J. Schnieders; C. S. Garbe; W. L. Peirson; G. B. Smith; C. J. Zappa
• Keywords: Surface waves (Oceanography); FOS: Earth and related environmental sciences; 551; Oceanography; 530; Turbulence--Mathematical models; 01 natural sciences; Image processing--Data processing; FOS: Mathematics; Ocean circulation--Data processing; 14. Life underwater; Hydrology; Mathematics; 0105 earth and related environmental sciences

AbstractIn 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.