• Login
    About WHOAS
    View Item 
    •   WHOAS Home
    • USGS Woods Hole Coastal and Marine Science Center
    • Sediment Transport
    • View Item
    •   WHOAS Home
    • USGS Woods Hole Coastal and Marine Science Center
    • Sediment Transport
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of WHOASCommunities & CollectionsBy Issue DateAuthorsTitlesKeywordsThis CollectionBy Issue DateAuthorsTitlesKeywords

    My Account

    LoginRegister

    Statistics

    View Usage Statistics

    Surface drift prediction in the Adriatic Sea using hyper-ensemble statistics on atmospheric, ocean and wave models : uncertainties and probability distribution areas

    Thumbnail
    View/Open
    rixen_etal.pdf (1.201Mb)
    Date
    2007-02-20
    Author
    Rixen, Michel  Concept link
    Ferreira-Coelho, E.  Concept link
    Signell, Richard P.  Concept link
    Metadata
    Show full item record
    Citable URI
    https://hdl.handle.net/1912/2048
    As published
    https://doi.org/10.1016/j.jmarsys.2007.02.015
    DOI
    10.1016/j.jmarsys.2007.02.015
    Keyword
     Forecast; Surface drift; Multi-model super-ensembles; Linear regression; Ocean models; Atmospheric models; Wave models 
    Abstract
    Despite numerous and regular improvements in underlying models, surface drift prediction in the ocean remains a challenging task because of our yet limited understanding of all processes involved. Hence, deterministic approaches to the problem are often limited by empirical assumptions on underlying physics. Multi-model hyper-ensemble forecasts, which exploit the power of an optimal local combination of available information including ocean, atmospheric and wave models, may show superior forecasting skills when compared to individual models because they allow for local correction and/or bias removal. In this work, we explore in greater detail the potential and limitations of the hyper-ensemble method in the Adriatic Sea, using a comprehensive surface drifter database. The performance of the hyper-ensembles and the individual models are discussed by analyzing associated uncertainties and probability distribution maps. Results suggest that the stochastic method may reduce position errors significantly for 12 to 72 h forecasts and hence compete with pure deterministic approaches.
    Description
    Author Posting. © NATO Undersea Research Centre, 2007. This article is posted here by permission of NATO Undersea Research Centre for personal use, not for redistribution. The definitive version was published in Journal of Marine Systems 69 (2008): 86-98, doi:10.1016/j.jmarsys.2007.02.015.
    Collections
    • Sediment Transport
    Suggested Citation
    Journal of Marine Systems 69 (2008): 86-98
     

    Related items

    Showing items related by title, author, creator and subject.

    • Thumbnail

      Natural variability and anthropogenic trends in oceanic oxygen in a coupled carbon cycle–climate model ensemble 

      Frolicher, T. L.; Joos, Fortunat; Plattner, Gian-Kasper; Steinacher, M.; Doney, Scott C. (American Geophysical Union, 2009-02-13)
      Internal and externally forced variability in oceanic oxygen (O2) are investigated on different spatiotemporal scales using a six-member ensemble from the National Center for Atmospheric Research CSM1.4-carbon coupled ...
    • Thumbnail

      Drift and mixing under the ocean surface : a coherent one-dimensional description with application to unstratified conditions 

      Rascle, Nicolas; Ardhuin, Fabrice; Terray, Eugene A. (American Geophysical Union, 2006-03-24)
      Waves have many effects on near-surface dynamics: Breaking waves enhance mixing, waves are associated with a Lagrangian mean drift (the Stokes drift), waves act on the mean flow by creating Langmuir circulations and a ...
    • Thumbnail

      Surface velocity in the equatorial oceans (20N-20S) calculated from historical ship drifts 

      Richardson, Philip L.; McKee, Theresa K. (Woods Hole Oceanographic Institution, 1989-04)
      Ship drift velocity observations were used to calculate and plot monthly mean and yearly mean velocities in 2° latitude by 5° longitude boxes for the Atlantic, Pacific, and Indian Oceans. The vector maps shown here provide ...
    All Items in WHOAS are protected by original copyright, with all rights reserved, unless otherwise indicated. WHOAS also supports the use of the Creative Commons licenses for original content.
    A service of the MBLWHOI Library | About WHOAS
    Contact Us | Send Feedback | Privacy Policy
    Core Trust Logo