Acoustic Doppler Current Velocity Measurements in the Ocean from Autonomous Underwater Gliders

Abstract:

Water velocities computed from high-resolution observations collected by an acoustic Doppler current profiler (ADCP) mounted on an underwater, autonomous glider yield in-situ water current data with valuable applications. For example, ocean current velocities are significant for validating coastal circulation models, studying the impacts of hurricanes and post-hurricane ocean recovery, monitoring the spreading of harmful algal blooms and dead zones, evaluating regions for potential marine hydrokinetic energy extraction, and estimating transport of volume, heat, sediments, nutrients, and pollutants. Obtaining water velocities from glider-ADCP observations involves a series of detailed data-processing steps which are customized to account for glider and sensor manufacturer differences and configuration options. Existing MathWorks® MATLAB code is adapted to compute water velocities for observations collected by a buoyancy-driven SeaExplorer glider equipped with an upward-facing, Nortek AD2CP-Glider sensor. The Department of Geological Sciences at The University of Texas at San Antonio (UTSA) acquired a Nortek AD2CP-equipped SeaExplorer glider, SEA039, in 2018. The glider's two-week test mission dataset, which was collected in April 2018 in the Northwestern Mediterranean Sea to test proper functioning of all equipment and sensors, is utilized for this ADCP-processing code development. Water velocities obtained using this code customization are statistically analyzed, visualized, and compared to manufacturer-processed results for the same dataset. Development of ADCP-processing code for an AD2CP-equipped SeaExplorer glider will enable UTSA to conduct in-house processing of existing and future SeaExplorer-observed water velocity data. This capability is essential to expand the department’s research to include ocean sciences and to complement the research activities of UTSA’s NASA Center for Advanced Measurements in Extreme Environments (CAMEE) by incorporating new observational technologies and data science.