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Removing Biases in Oceanic Turbulent Kinetic Energy Dissipation Rate Estimated from Microstructure Shear Data

Bruno FerronaUniversity Brest, CNRS, IFREMER, IRD, Laboratoire d’Océanographie Physique et Spatiale, IUEM, Brest, France

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P. Bouruet AubertotbLOCEAN-IPSL, Sorbonne University (UPMC, Univ Paris 06)-CNRS-IRD-MNHN, Paris, France

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Y. CuypersbLOCEAN-IPSL, Sorbonne University (UPMC, Univ Paris 06)-CNRS-IRD-MNHN, Paris, France

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C. VicaUniversity Brest, CNRS, IFREMER, IRD, Laboratoire d’Océanographie Physique et Spatiale, IUEM, Brest, France

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Abstract

To calculate a turbulent kinetic energy dissipation rate from the microstructure vertical shear of the horizontal velocity via a spectral analysis, shear spectra need first to be cleaned from vibrations of the moving vehicle. Unambiguously, this study shows that the spectral cleaning must be applied all over the frequency range and not only at frequencies larger than 10 Hz, as a recent study suggested. For a Vertical Microstructure Profiler (VMP-6000), not correcting for vehicle vibrations below 10 Hz leads to overestimated dissipation rates from 50% to 700% for usual downcast velocities and for weak dissipation rates (ε < 1 × 10−9 W kg−1). Vibrations concern all vehicles, but the exact vibrational frequency signature depends on the vehicle shape and its downcast velocity. In any case, a spectral cleaning over the whole frequency range is strongly advised. This study also reports on a systematic low bias of inferred dissipation rates induced by the spectral cleaning when too few degrees of freedom are available for the cleaning, which is usually the default of the standard processing. Whatever the dissipation rate level, not correcting for the bias leads to underestimated dissipation rates by a factor 1.4–2.7 (with usual parameters), the exact amplitude of the bias depending on the number of degrees of freedom and on the number of independent accelerometer axes used for the cleaning. It is strongly advised that such a bias be taken into account to recompute dissipation rates of past datasets and for future observations.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Bruno Ferron, bruno.ferron@ifremer.fr

Abstract

To calculate a turbulent kinetic energy dissipation rate from the microstructure vertical shear of the horizontal velocity via a spectral analysis, shear spectra need first to be cleaned from vibrations of the moving vehicle. Unambiguously, this study shows that the spectral cleaning must be applied all over the frequency range and not only at frequencies larger than 10 Hz, as a recent study suggested. For a Vertical Microstructure Profiler (VMP-6000), not correcting for vehicle vibrations below 10 Hz leads to overestimated dissipation rates from 50% to 700% for usual downcast velocities and for weak dissipation rates (ε < 1 × 10−9 W kg−1). Vibrations concern all vehicles, but the exact vibrational frequency signature depends on the vehicle shape and its downcast velocity. In any case, a spectral cleaning over the whole frequency range is strongly advised. This study also reports on a systematic low bias of inferred dissipation rates induced by the spectral cleaning when too few degrees of freedom are available for the cleaning, which is usually the default of the standard processing. Whatever the dissipation rate level, not correcting for the bias leads to underestimated dissipation rates by a factor 1.4–2.7 (with usual parameters), the exact amplitude of the bias depending on the number of degrees of freedom and on the number of independent accelerometer axes used for the cleaning. It is strongly advised that such a bias be taken into account to recompute dissipation rates of past datasets and for future observations.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Bruno Ferron, bruno.ferron@ifremer.fr

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