YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • AMS
    • Journal of Atmospheric and Oceanic Technology
    • View Item
    •   YE&T Library
    • AMS
    • Journal of Atmospheric and Oceanic Technology
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    Autonomous Sampling of Ocean Submesoscale Fronts with Ocean Gliders and Numerical Model Forecasting

    Source: Journal of Atmospheric and Oceanic Technology:;2018:;volume 035:;issue 003::page 503
    Author:
    Flexas, Mar M.
    ,
    Troesch, Martina I.
    ,
    Chien, Steve
    ,
    Thompson, Andrew F.
    ,
    Chu, Selina
    ,
    Branch, Andrew
    ,
    Farrara, John D.
    ,
    Chao, Yi
    DOI: 10.1175/JTECH-D-17-0037.1
    Publisher: American Meteorological Society
    Abstract: 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 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.
    • Download: (2.619Mb)
    • Show Full MetaData Hide Full MetaData
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      Autonomous Sampling of Ocean Submesoscale Fronts with Ocean Gliders and Numerical Model Forecasting

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4261012
    Collections
    • Journal of Atmospheric and Oceanic Technology

    Show full item record

    contributor authorFlexas, Mar M.
    contributor authorTroesch, Martina I.
    contributor authorChien, Steve
    contributor authorThompson, Andrew F.
    contributor authorChu, Selina
    contributor authorBranch, Andrew
    contributor authorFarrara, John D.
    contributor authorChao, Yi
    date accessioned2019-09-19T10:03:12Z
    date available2019-09-19T10:03:12Z
    date copyright1/31/2018 12:00:00 AM
    date issued2018
    identifier otherjtech-d-17-0037.1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4261012
    description abstractABSTRACTSubmesoscale 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.
    publisherAmerican Meteorological Society
    titleAutonomous Sampling of Ocean Submesoscale Fronts with Ocean Gliders and Numerical Model Forecasting
    typeJournal Paper
    journal volume35
    journal issue3
    journal titleJournal of Atmospheric and Oceanic Technology
    identifier doi10.1175/JTECH-D-17-0037.1
    journal fristpage503
    journal lastpage521
    treeJournal of Atmospheric and Oceanic Technology:;2018:;volume 035:;issue 003
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian