Year: 2025
Type: article
Abstract: Quasi two‐dimensional visualizations of microstructure in the thermocline are created by processing pod signals in a nonstandard way. As the moored instrument is pumped by surface waves, the fast thermistors at each end trace out vertically overlapping paths, from which we produce 2–3 m tall swaths. A swath capturing the unmistakable form of Kelvin–Helmholtz billows provides a proof of concept—albeit by relying on what is a rare event in our data set in which the flow is generally stable (as per the Richardson number). More commonly, swaths exhibit steppy temperature fields during weaker turbulence, an abundance of small overturns during stronger turbulence, or some combination thereof. To examine this continuum statistically, we take a 13‐month data set from an equatorial pod at a 120‐m depth and divide it into 53,000 swaths, each 10‐min long. Swaths are ordered based on their associated buoyancy Reynolds number inferred from our standard pod processing. Clear visual differences in swath characteristics arise when comparing across an order of magnitude or more (e.g., vs. –100 vs. ). With the help of a convolutional neural network to semiobjectively pick representative swaths, we present our best estimates of what microstructure looks like in two dimensions as a function of .
DOI: 10.1029/2024jc022134
Authors: Kenneth G. Hughes, James N. Moum
Institutions: University of Waikato, Oregon State University
Language: English
Subfield: Oceanography
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,070
OpenAlex ID: W4409465725