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Autonomous sampling of ocean submesoscale fronts with ocean gliders and numerical model forecasting

Flexas, Mar M. and Troesch, Martina I. and Chien, Steve and Thompson, Andrew F. and Chu, Selina and Branch, Andrew and Farrara, John D. and Chao, Yi (2018) Autonomous sampling of ocean submesoscale fronts with ocean gliders and numerical model forecasting. Journal of Atmospheric and Oceanic Technology, 35 (3). pp. 503-521. ISSN 0739-0572. doi:10.1175/JTECH-D-17-0037.1.

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Submesoscale fronts arising from mesoscale stirring are ubiquitous in the ocean and have a strong impact on upper-ocean dynamics. This work presents a method for optimizing 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-h 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, off the coast of California, during a 9-day experiment focused on sampling subsurface 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 the model and glider results. The model reproduces the vertical (~50–200 m thick) and lateral (~5–20 km) scales of subsurface subducting fronts and near-bottom features observed in the glider data. The differences between model and glider data are, in part, attributed to the selected glider optimal interpolation parameters and 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 for the incorporation of multiple observing platforms.

Item Type:Article
Related URLs:
URLURL TypeDescription
Flexas, Mar M.0000-0002-0617-3004
Thompson, Andrew F.0000-0003-0322-4811
Branch, Andrew0000-0002-9877-6944
Additional Information:© 2018 American Meteorological Society. Manuscript received 2 March 2017, in final form 2 November 2017; Published online: March 15, 2018. We thank Hongchun (Carrie) Zhang for her help with the ROMS modeling; Giuliana Viglione, Zack Erickson, and Xiaozhou Ruan for their help deploying, trimming, and recovering the Caltech glider; and Patrice Klein for his useful comments. Color maps used in this contribution are from Thyng et al. (2016). This work is funded by the Keck Institute for Space Studies (generously supported by the W. M. Keck Foundation) through the project “Science-Driven Autonomous and Heterogeneous Robotic Networks: A Vision for Future Ocean Observations” ( This work was, in part, performed at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. The authors are grateful to three anonymous reviewers, whose comments helped improve the manuscript.
Group:Keck Institute for Space Studies
Funding AgencyGrant Number
W. M. Keck FoundationUNSPECIFIED
Keck Institute for Space Studies (KISS)UNSPECIFIED
Issue or Number:3
Record Number:CaltechAUTHORS:20180312-112312825
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:85245
Deposited By: Tony Diaz
Deposited On:12 Mar 2018 20:05
Last Modified:15 Nov 2021 20:27

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