An Efficient Scheme for Onboard Reduction of Moored χpod Data

An Efficient Scheme for Onboard Reduction of Moored χpod Data AbstractA scheme for significantly reducing data sampled on turbulence devices (χpods) deployed on remote oceanographic moorings is proposed. Each χpod is equipped with a pitot-static tube, two fast-response thermistors, a three-axis linear accelerometer, and a compass. In preprocessing, voltage means, variances, and amplitude of the subrange (inertial-convective subrange of the turbulence) of the voltage spectrum representing the temperature gradient are computed. Postprocessing converts voltages to engineering units, in particular mean flow speed (and velocity), temperature, temperature gradient, and the rate of destruction of the temperature variance χ from which other turbulence quantities, such as heat flux, are derived. On 10-min averages, this scheme reduces the data by a factor of roughly 24 000 with a small (5%) low bias compared to complete estimates using inertial-convective subrange scaling of calibrated temperature gradient spectra. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Atmospheric and Oceanic Technology American Meteorological Society

An Efficient Scheme for Onboard Reduction of Moored χpod Data

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0426
D.O.I.
10.1175/JTECH-D-17-0118.1
Publisher site
See Article on Publisher Site

Abstract

AbstractA scheme for significantly reducing data sampled on turbulence devices (χpods) deployed on remote oceanographic moorings is proposed. Each χpod is equipped with a pitot-static tube, two fast-response thermistors, a three-axis linear accelerometer, and a compass. In preprocessing, voltage means, variances, and amplitude of the subrange (inertial-convective subrange of the turbulence) of the voltage spectrum representing the temperature gradient are computed. Postprocessing converts voltages to engineering units, in particular mean flow speed (and velocity), temperature, temperature gradient, and the rate of destruction of the temperature variance χ from which other turbulence quantities, such as heat flux, are derived. On 10-min averages, this scheme reduces the data by a factor of roughly 24 000 with a small (5%) low bias compared to complete estimates using inertial-convective subrange scaling of calibrated temperature gradient spectra.

Journal

Journal of Atmospheric and Oceanic TechnologyAmerican Meteorological Society

Published: Nov 30, 2017

References

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