• Branch, A., M. Troesch, S. Chu, S. Chien, Y. Chao, J. Farrara, and A. Thompson, 2016: Evaluating scientific coverage strategies for a heterogeneous fleet of marine assets using a predictive model of ocean currents. The 26th International Conference on Automated Planning and Scheduling: Proceedings of the 10th Scheduling and Planning Applications Workshop (SPARK), S. Bernardini et al., Eds., ICAPS, 10–19, http://icaps16.icaps-conference.org/proceedings/spark16.pdf.

  • Brito, M., D. Smeed, and G. Griffiths, 2014: Underwater glider reliability and implications for survey design. J. Atmos. Oceanic Technol., 31, 28582870, https://doi.org/10.1175/JTECH-D-13-00138.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chao, Y., and Coauthors, 2018: Development, implementation and validation of a California coastal ocean modeling, data assimilation, and forecasting system. Deep-Sea Res. II, https://doi.org/10.1016/j.dsr2.2017.04.013, in press.

    • Crossref
    • Export Citation
  • Checkley, D. M., and J. A. Barth, 2009: Patterns and processes in the California Current System. Prog. Oceanogr., 83, 4964, https://doi.org/10.1016/j.pocean.2009.07.028.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cruz, N. A., and A. C. Matos, 2010: Adaptive sampling of thermoclines with autonomous underwater vehicles. Proc. OCEANS 2010 MTS/IEEE Seattle, Seattle, WA, IEEE, 6 pp., https://doi.org/10.1109/OCEANS.2010.5663903.

    • Crossref
    • Export Citation
  • Cruz, N. A., and A. C. Matos, 2014: Autonomous tracking of a horizontal boundary. Proc. 2014 Oceans—St. John’s, St. John’s, NL, Canada, IEEE, 6 pp., https://doi.org/10.1109/OCEANS.2014.7003275.

    • Crossref
    • Export Citation
  • Curtin, T. B., and J. G. Bellingham, 2009: Progress toward autonomous ocean sampling networks. Deep-Sea Res. II, 56, 6267, https://doi.org/10.1016/j.dsr2.2008.09.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Curtin, T. B., J. G. Bellingham, J. Catipovic, and D. Webb, 1993: Autonomous oceanographic sampling networks. Oceanography, 6, 8694, https://doi.org/10.5670/oceanog.1993.03.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Das, J., F. Py, T. Maughan, T. O. Reilly, M. Messié, J. Ryan, G. S. Sukhatme, and K. Rajan, 2012: Coordinated sampling of dynamic oceanographic features with underwater vehicles and drifters. Int. J. Rob. Res., 31, 626646, https://doi.org/10.1177/0278364912440736.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davis, R. E., C. C. Eriksen, and C. P. Jones, 2002: Autonomous buoyancy-driven underwater gliders. Technology and Applications of Autonomous Underwater Vehicles, G. Griffiths, Ed., CRC Press, 37–58.

    • Search Google Scholar
    • Export Citation
  • Davis, R. E., M. D. Ohman, D. L. Rudnick, J. T. Sherman, and B. Hodges, 2008: Glider surveillance of physics and biology in the southern California Current System. Limnol. Oceanogr., 53, 21512168, https://doi.org/10.4319/lo.2008.53.5_part_2.2151.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davis, R. E., N. E. Leonard, and D. M. Frantantoni, 2009: Routing strategies for underwater gliders. Deep-Sea Res. II, 56, 173187, https://doi.org/10.1016/j.dsr2.2008.08.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Egbert, G. D., and S. Y. Erofeeva, 2002: Efficient inverse modeling of barotropic ocean tides. J. Atmos. Oceanic Technol., 19, 183204, https://doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eriksen, C. C., T. J. Osse, R. D. Light, T. Wen, T. W. Lehman, P. L. Sabin, J. W. Ballard, and A. M. Chiodi, 2001: Seaglider: A long-range autonomous underwater vehicle for oceanographic research. IEEE J. Oceanic Eng., 26, 424436, https://doi.org/10.1109/48.972073.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Felzenszwalb, P., and D. Huttenlocher, 2004: Efficient graph-based image segmentation. Int. J. Comput. Vision, 59, 167181, https://doi.org/10.1023/B:VISI.0000022288.19776.77.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ferrari, R., and K. Polzin, 2005: Finescale structure of the TS relation in the eastern North Atlantic. J. Phys. Oceanogr., 35, 14371454, https://doi.org/10.1175/JPO2763.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fiorelli, E., N. E. Leonard, P. Bhatta, D. Paley, R. Bachmayer, and D. M. Frantantoni, 2004: Multi-AUV control and adaptive sampling in Monterey Bay. Proc. 2004 IEEE/OES Autonomous Underwater Vehicles, Sebasco, ME, IEEE, 134–147, https://doi.org/10.1109/AUV.2004.1431204.

    • Crossref
    • Export Citation
  • Flament, P., 2002: A state variable for characterizing water masses and their diffusive stability: Spiciness. Prog. Oceanogr., 54, 493501, https://doi.org/10.1016/S0079-6611(02)00065-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Frajka-Williams, E., C. K. Eriksen, P. B. Rhines, and R. R. Harcourt, 2011: Determining vertical water velocities from Seaglider. J. Atmos. Oceanic Technol., 28, 16411656, https://doi.org/10.1175/2011JTECHO830.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Garau, B., S. Ruiz, W. G. Zhang, A. Pascual, E. Heslop, J. Kerfoot, and J. Tintore, 2011: Thermal lag correction on Slocum CTD glider data. J. Atmos. Oceanic Technol., 28, 10651071, https://doi.org/10.1175/JTECH-D-10-05030.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Godin, M., Y. Zhang, J. Ryan, T. Hoover, and J. Bellingham, 2011: Phytoplankton bloom patch center localization by the Tethys Autonomous Underwater Vehicle. Proc. OCEANS’11 MTS/IEEE Kona, Waikoloa, HI, IEEE, 6 pp., https://doi.org/10.23919/OCEANS.2011.6107161.

    • Crossref
    • Export Citation
  • Haidvogel, D. B., H. G. Arango, K. Hedstrom, A. Beckmann, P. Malanotte-Rizzoli, and A. F. Shchepetkin, 2000: Model evaluation experiments in the North Atlantic Basin: Simulations in nonlinear terrain-following coordinates. Dyn. Atmos. Oceans, 32, 239281, https://doi.org/10.1016/S0377-0265(00)00049-X.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hickey, B. M., 1979: The California Current System: Hypotheses and facts. Prog. Oceanogr., 8, 191279, https://doi.org/10.1016/0079-6611(79)90002-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jacox, M. G., E. L. Hazen, K. D. Zaba, D. L. Rudnick, C. A. Edwards, A. M. Moore, and S. J. Bograd, 2016: Impacts of the 2015–2016 El Niño on the California Current System: Early assessment and comparison to past events. Geophys. Res. Lett., 43, 70727080, https://doi.org/10.1002/2016GL069716.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klein, P., A. M. Treguier, and B. L. Hua, 1998: Three-dimensional stirring of thermohaline fronts. J. Mar. Res., 56, 589612, https://doi.org/10.1357/002224098765213595.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leonard, N., D. Paley, R. Davis, D. Fratantoni, F. Lekien, and F. Zhang, 2010: Coordinated control of an underwater glider fleet in an adaptive ocean sampling field experiment in Monterey Bay. J. Field Rob., 27, 718740, https://doi.org/10.1002/rob.20366.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, L., W. Huang, I. Y. H. Gu, and Q. Tian, 2002: Foreground object detection in changing background based on color co-occurrence statistics. Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision (WACV 2002), IEEE, 269–274, https://doi.org/10.1109/ACV.2002.1182193.

    • Crossref
    • Export Citation
  • Li, Z., Y. Chao, J. C. McWilliams, and K. Ide, 2008a: A three-dimensional variational data assimilation scheme for the Regional Ocean Modeling System. J. Atmos. Oceanic Technol., 25, 20742090, https://doi.org/10.1175/2008JTECHO594.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, Z., Y. Chao, J. C. McWilliams, and K. Ide, 2008b: A three-dimensional variational data assimilation scheme for the Regional Ocean Modeling System: Implementation and basic experiments J. Geophys. Res., 113, C05002, https://doi.org/10.1029/2006JC004042.

    • Search Google Scholar
    • Export Citation
  • Li, Z., Y. Chao, J. D. Farrara, and J. C. McWilliams, 2013: Impacts of distinct observations during the 2009 Prince William Sound field experiment: A data assimilation study. Cont. Shelf Res., 63 (Suppl.), S209–S222, https://doi.org/10.1016/j.csr.2012.06.018.

    • Crossref
    • Export Citation
  • Li, Z., J. C. McWilliams, K. Ide, and J. D. Farrara, 2015a: Coastal ocean data assimilation using a multi-scale three-dimensional variational scheme. Ocean Dyn., 65, 10011015, https://doi.org/10.1007/s10236-015-0850-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, Z., J. C. McWilliams, K. Ide, and J. D. Farrara, 2015b: A multiscale variational data assimilation scheme: Formulation and illustration. Mon. Wea. Rev., 143, 38043822, https://doi.org/10.1175/MWR-D-14-00384.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lynn, R. J., and J. J. Simpson, 1987: The California Current System: The seasonal variability of its physical characteristics. J. Geophys. Res., 92, 12 94712 966, https://doi.org/10.1029/JC092iC12p12947.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Magazzeni, D., F. Py, M. Fox, D. Long, and K. Rajan, 2014: Policy learning for autonomous feature tracking. Auton. Rob., 37, 4769, https://doi.org/10.1007/s10514-013-9375-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McClatchie, S., 2014: Regional Fisheries Oceanography of the California Current System: The CalCOFI Program. Springer, 235 pp., https://doi.org/10.1007/978-94-007-7223-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McWilliams, J. C., 2016: Submesoscale currents in the ocean. Proc. Roy. Soc. London, 472A, 20160117, https://doi.org/10.1098/rspa.2016.0117.

    • Search Google Scholar
    • Export Citation
  • Molemaker, M. J., J. C. McWilliams, and W. K. Dewar, 2015: Submesoscale instability and generation of mesoscale anticyclones near a separation of the California Undercurrent. J. Phys. Oceanogr., 45, 613629, https://doi.org/10.1175/JPO-D-13-0225.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Munk, W., 1981: Internal waves and small-scale processes. Evolution of Physical Oceanography: Scientific Surveys in Honor of Henry Stommel, B. A. Warren and C. Wunsch, Eds., MIT Press, 264–291.

    • Search Google Scholar
    • Export Citation
  • Petillo, S., H. Schmidt, and A. Balasuriya, 2012: Constructing a distributed AUV network for underwater plume-tracking operations. Int. J. Distrib. Sens. Networks, 8, 191235, https://doi.org/10.1155/2012/191235.

    • Crossref
    • Export Citation
  • Powell, J. R., and M. D. Ohman, 2015: Covariability of zooplankton gradients with glider-detected density fronts in the Southern California Current System. Deep-Sea Res. II, 112, 7990, https://doi.org/10.1016/j.dsr2.2014.04.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ramp, S., and Coauthors, 2009: Preparing to predict: The Second Autonomous Ocean Sampling Network (AOSN-II) experiment in the Monterey Bay. Deep-Sea Res. II, 56, 6886, https://doi.org/10.1016/j.dsr2.2008.08.013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reid, J. L., Jr., and R. A. Schwartzlose, 1962: Direct measurements of the Davidson Current off central California. J. Geophys. Res., 67, 24912497, https://doi.org/10.1029/JZ067i006p02491.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ruddick, B., and A. E. Gargett, 2003: Oceanic double-infusion: Introduction. Prog. Oceanogr., 56, 381393, https://doi.org/10.1016/S0079-6611(03)00024-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rudnick, D. L., 2016: Ocean research enabled by underwater gliders. Annu. Rev. Mar. Sci., 8, 519541, https://doi.org/10.1146/annurev-marine-122414-033913.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rudnick, D. L., and S. T. Cole, 2011: On sampling the ocean using underwater gliders. J. Geophys. Res., 116, C08010, https://doi.org/10.1029/2010JC006849.

    • Crossref
    • Export Citation
  • Rudnick, D. L., R. E. Davis, C. C. Eriksen, D. M. Fratantoni, and M. J. Perry, 2004: Underwater gliders for ocean research. Mar. Technol. Soc. J., 38, 7384, https://doi.org/10.4031/002533204787522703.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rudnick, D. L., R. E. Davis, and J. T. Sherman, 2016: Spray underwater glider operations. J. Atmos. Oceanic Technol., 33, 11131122, https://doi.org/10.1175/JTECH-D-15-0252.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rudnick, D. L., K. D. Zaba, R. E. Todd, and R. E. Davis, 2017: A climatology of the California Current System from a network of underwater gliders. Prog. Oceanogr., 154, 64106, https://doi.org/10.1016/j.pocean.2017.03.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shchepetkin, A. F., and J. McWilliams, 2005: The Regional Ocean Modeling System: A split-explicit, free-surface, topography-following-coordinate ocean model. Ocean Modell., 9, 347404, https://doi.org/10.1016/j.ocemod.2004.08.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sherman, J. T., R. E. Davis, W. B. Owens, and J. Valdes, 2001: The autonomous underwater glider spray. IEEE J. Oceanic Eng., 26, 437446, https://doi.org/10.1109/48.972076.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Slørdal, L. H., 1997: The pressure gradient force in sigma-coordinate ocean models. Int. J. Numer. Methods Fluids, 24, 9871017, https://doi.org/10.1002/(SICI)1097-0363(19970530)24:10<987::AID-FLD527>3.0.CO;2-V.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, K. S., and R. Ferrari, 2009: The production and dissipation of compensated thermohaline variance by mesoscale stirring. J. Phys. Oceanogr., 39, 24772501, https://doi.org/10.1175/2009JPO4103.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stauffer, C., and W. E. L. Grimson, 1999: Adaptive background mixture models for real-time tracking. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, IEEE, 246252.

  • Stommel, H. M., 1962: On the cause of the temperature-salinity curve in the ocean. Proc. Natl. Acad. Sci. USA, 48, 764766, https://doi.org/10.1073/pnas.48.5.764.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, L., Y. Li, S. Yan, J. Wang, and Z. Chen, 2016: Thermocline tracking using a portable autonomous underwater vehicle based on adaptive threshold. Proc. OCEANS 2016—Shanghai, IEEE, Shanghai, China, 4 pp., https://doi.org/10.1109/OCEANSAP.2016.7485419.

    • Crossref
    • Export Citation
  • Thomas, L. N., A. Tandon, and A. Mahadevan, 2008: Submesoscale processes and dynamics. Ocean Modeling in an Eddying Regime, Geophys. Monogr.,Vol. 177, Amer. Geophys. Union, 17–38, https://doi.org/10.1029/177GM04.

    • Search Google Scholar
    • Export Citation
  • Thompson, A. F., and Coauthors, 2017: Satellites to the seafloor: Towards fully autonomous ocean sampling. Oceanography, 30 (2), 160168, https://doi.org/10.5670/oceanog.2017.238.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thyng, K., C. Greene, R. Hetland, H. Zimmerle, and S. DiMarco, 2016: True colors of oceanography: Guidelines for effective and accurate colormap selection. Oceanography, 29 (3), 913, https://doi.org/10.5670/oceanog.2016.66.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Todd, R. E., D. L. Rudnick, R. E. Davis, and M. D. Ohman, 2011a: Underwater gliders reveal rapid arrival of El Niño effects off California’s coast. Geophys. Res. Lett., 38, L03609, https://doi.org/10.1029/2010GL046376.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Todd, R. E., D. L. Rudnick, M. R. Mazloff, and R. E. Davis, 2011b: Poleward flows in the southern California Current System: Glider observations and numerical simulation. J. Geophys. Res., 116, C02026, https://doi.org/10.1029/2010JC006536.

    • Search Google Scholar
    • Export Citation
  • Todd, R. E., D. L. Rudnick, M. R. Mazloff, B. D. Cornuelle, and R. E. Davis, 2012: Thermohaline structure in the California Current System: Observations and modeling of spice variance. J. Geophys. Res., 117, C02008, https://doi.org/10.1029/2011JC007589.

    • Search Google Scholar
    • Export Citation
  • Veronis, G., 1972: On the properties of seawater defined by temperature, salinity and pressure. J. Mar. Res., 30, 227255.

  • Webb, D. C., P. J. Simonetti, and C. P. Jones, 2001: SLOCUM: An underwater glider propelled by environmental energy. IEEE J. Oceanic Eng., 26, 447452, https://doi.org/10.1109/48.972077.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zaba, K. D., and D. L. Rudnick, 2016: The 2014–2015 warming anomaly in the Southern California Current System observed by underwater gliders. Geophys. Res. Lett., 43, 12411248, https://doi.org/10.1002/2015GL067550.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, F., D. M. Fratantoni, D. Paley, N. E. Leonard, and J. M. Lund, 2007: Control of coordinated patterns for ocean sampling. Int. J. Control, 80, 11861199, https://doi.org/10.1080/00207170701222947.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, Y., J. G. Bellingham, M. Godin, J. P. Ryan, R. S. McEwen, B. Kieft, B. Hobson, and T. Hoover, 2010: Thermocline tracking based on peak-gradient detection by an autonomous underwater vehicle. Proc. OCEANS 2010 MTS/IEEE Seattle, Seattle, WA, IEEE, 4 pp., https://doi.org/10.1109/OCEANS.2010.5664545.

    • Crossref
    • Export Citation
  • Zhang, Y., R. S. McEwen, J. P. Ryan, J. G. Bellingham, H. Thomas, C. H. Thompson, and E. Rienecker, 2011: A peak-capture algorithm used on an autonomous underwater vehicle in the 2010 Gulf of Mexico oil spill response scientific survey. J. Field Rob., 28, 484496, https://doi.org/10.1002/rob.20399.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, Y., M. A. Godin, J. G. Bellingham, and J. P. Ryan, 2012: Using an autonomous underwater vehicle to track a coastal upwelling front. IEEE J. Oceanic Eng., 37, 338347, https://doi.org/10.1109/JOE.2012.2197272.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, Y., J. G. Bellingham, J. P. Ryan, B. Kieft, and M. J. Stanway, 2016: Autonomous four-dimensional mapping and tracking of a coastal upwelling front by an autonomous underwater vehicle. J. Field Rob., 33, 6781, https://doi.org/10.1002/rob.21617.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Autonomous Sampling of Ocean Submesoscale Fronts with Ocean Gliders and Numerical Model Forecasting

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  • 1 California Institute of Technology, Pasadena, California
  • 2 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California
  • 3 California Institute of Technology, Pasadena, California
  • 4 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California
  • 5 Remote Sensing Solutions, Monrovia, California
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ABSTRACT

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.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Mar M. Flexas, marf@caltech.edu

ABSTRACT

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.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Mar M. Flexas, marf@caltech.edu
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