AbstractSubmesoscale fronts arising from mesoscale stirring are ubiquitous in the ocean and have a strong impact on upper ocean dynamics. This work presents a method to optimize the sampling of ocean fronts with autonomous vehicles at meso- and submesoscales, based on a combination of numerical forecast and autonomous planning. This method uses a 48-hour forecast from a real-time high-resolution data-assimilative primitive-equation ocean model, feature detection techniques, and a planner that controls the observing platform. The method is tested in Monterey Bay during a nine-day experiment focused on sampling sub-surface thermohaline-compensated structures using a Seaglider as the ocean observing platform. Based on model estimations, the sampling “gain”, defined as the magnitude of isopycnal tracer variability sampled, is 50% larger in the feature-chasing case with respect to a non feature-tracking scenario. The ability of the model to reproduce, in space and time, thermohaline submesoscale features is evaluated by quantitatively comparing model and glider results. The model reproduces the vertical (~ 50–200 m thick) and lateral (~ 5–20 km) scales of sub-surface subducting fronts and near-bottom features observed in the glider data. Differences between model and glider data are, in part, attributed to the selected glider optimal interpolation parameters, as well as to uncertainties in the forecasting of the location of the structures. This method can be exported to any place in the ocean where high-resolution data assimilative model output is available, and it allows the incorporation of multiple observing platforms.
Journal of Atmospheric and Oceanic Technology – American Meteorological Society
Published: Jan 31, 2018
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