Articles | Volume 22, issue 2
https://doi.org/10.5194/os-22-893-2026
https://doi.org/10.5194/os-22-893-2026
Technical note
 | 
19 Mar 2026
Technical note |  | 19 Mar 2026

Best practices for estimating turbulent dissipation from oceanic single-point velocity timeseries observations

Cynthia E. Bluteau, Danielle J. Wain, Julia C. Mullarney, and Craig L. Stevens

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Cited articles

Bendat, J. S. and Piersol, A. G.: Random Data: Analysis and Measurement Procedures, Probability and statistics, 3rd edn., Wiley – Interscience, ISBN 0471317330, 2000. a, b
Bluteau, C.: Assessing statistical fitting methods used for estimating turbulence parameters, Limnol. Oceanogr.: Methods, https://doi.org/10.1002/lom3.10729, 2025a. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t
Bluteau, C.: Synthetic spectra for assessing statistical fitting methods used to estimate ocean turbulence, Zenodo [data set], https://doi.org/10.5281/zenodo.10576543, 2025b. a, b, c, d, e
Bluteau, C., Stevens, C., Wain, D., and Mullarney, J.: NetCDF templates and code for creating and loading ATOMIX ADV benchmark datasets, Zenodo [data set, code], https://doi.org/10.5281/zenodo.16798905, 2025. a, b, c
Bluteau, C. E., Jones, N. L., and Ivey, G. N.: Estimating turbulent kinetic energy dissipation using the inertial subrange method in environmental flows, Limnol. and Oceanogr.: Methods, 9, 302–321, https://doi.org/10.4319/lom.2011.9.302, 2011. a, b, c, d, e, f, g, h, i
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This article provides best practices for estimating an ocean turbulence parameter, epsilon, from velocity measurements. Improper data handling can lead to significant errors in the estimated mixing, propagating into estimates of heat transfers, salt, dissolved gases, and nutrients. The article explains how to process velocity datasets using benchmark datasets from different instruments and platforms in varied ocean environments. The datasets allow users to test their processing algorithms.
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