Technical note: Turbulence measurements from a light autonomous underwater vehicle

Abstract

Abstract. A self-contained turbulence instrument from Rockland Scientific was installed on a light autonomous underwater vehicle (AUV) from OceanScan Marine Systems and Technology Lda. We report on the data quality and discuss limitations of dissipation estimated from two shear probes during a deployment in the Barents Sea in February 2021. The AUV mission lasted for 5 h, operating at a typical horizontal speed of 1.1 m s−1. The AUV was programmed to find and cross the maximum along-path thermal gradient at 10, 20 and 30 m depths along 4 km transects. Although the AUV vibrations contaminate the shear probe records, the noise is mitigated by removing vibration-induced components from shear spectra using the accelerometer signal measured in multiple directions. Dissipation rate estimates in the observed transects varied in the range 1×10-8 and 6×10-6 W kg−1, with the values from the two orthogonal probes typically in agreement to within a factor of 2. Dissipation estimates from the AUV show good agreement with nearby vertical microstructure profiles obtained from the ship during the transects, indicating that the turbulence measurements from the AUV are reliable for this relatively turbulent environment. However, the lowest reliable dissipation rates are limited to 5×10-8 W kg−1, making this setup unfit for use in quiescent environments.

Turbulent diapycnal fluxes as a pilot Essential Ocean Variable

“…Shorter segments have larger variability and longer segments have smaller variability. Similar levels of agreement are described by Oakey (1982) who compared ϵ derived from a shear probe to that derived from a thin film thermometer, and by Kolås et al (2022) who compared outputs from two shear probes installed on an AUV. (Further examples are given in Section 3.1 of the turbulence methodology review by Burchard et al (2008)).…”

Lake Water Temperature Modeling in an Era of Climate Change: Data Sources, Models, and Future Prospects

“…The introduction of a new generation of temperature sensors in place of mercury thermometers significantly improved the resolution of the measurements (from O(0.1) K Niedrist et al, 2018) to O(0.1) mK (Van Haren et al, 2005)) and the automation of the monitoring stations increased the sampling frequency. In this case, more than reporting on the wide spectrum of possible sampling frequencies that are available nowadays (up to 1,024 Hz for microstructure purposes, see e.g., Kolås et al (2022), but the choice is dependent on the compromise between desired resolution of the final data set and available storage capacity), it is probably more relevant commenting on the advantages introduced by automated stations in terms of limiting the well-recognized influence of weather conditions on the availability/quality of the final measurements. On the one hand manual measurements suffer from the so-called "fair weather bias," where manual sampling is avoided or impossible in bad weather conditions, and on the other hand the quality and accuracy of these measurements, when their acquisition is logistically possible, are affected by the weather conditions (Rand et al, 2022).…”

Still Arctic? - The changing Barents Sea

“…One area that offers hope for rapidly filling knowledge gaps is the recent development of new instrumentcarrying platforms (remotely operated vehicles, autonomous underwater vehicles, unmanned surface vehicles, drones, buoys and satellites) and improved physical and biological sensors (temperature/salinity, acoustic, optical, and turbulence; e.g., Engelsen et al, 2002Engelsen et al, , 2004Fossum et al, 2018;Johnsen et al, 2018;Ludvigsen et al, 2018;Kolås et al, 2022). Such instrument-carrying robots can provide high-resolution data in time and space, filling observational gaps and adding to ongoing long-term monitoring (e.g., Arneberg et al, 2020).…”