Ocean Science
Ocean Science
OS
Articles | Volume 22, issue 2
Articles | Volume 22, issue 2
https://doi.org/10.5194/os-22-1377-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-22-1377-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 22, issue 2
Technical note
 | Highlight paper
 | 
28 Apr 2026
Technical note | Highlight paper |  | 28 Apr 2026

A method for quantifying correlation in the shape of oceanographic profile data

Mark Taylor and Stephanie Henson

Data sets

Data and code accompanying "A method for quantifying correlation in the shape of oceanographic profile data" Mark Taylor and Stephanie Henson https://doi.org/10.5281/zenodo.18164579

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Editorial statement
This is the first application of a particular mathematical framework to oceanographic observations, which could potentially be used widely for many applications, for example, to data from moorings, autonomous platforms and ocean models, with possible use in observing system optimisation, data assimilation and the analysis of vertically structured ocean processes.
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Short summary
Oceanographic profiles comprise measurements of variables across depths. Here, a method is presented to calculate the correlation between profiling datasets by quantifying profile shape variability. This enables dependencies between multiple variables, and spatial or temporal changes in a single variable, to be described. Two case studies demonstrate the method using profiling data from a stationary mooring and drifting floats.
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