YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • AMS
    • Monthly Weather Review
    • View Item
    •   YE&T Library
    • AMS
    • Monthly Weather Review
    • 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

    Assimilation of Subsurface Thermal Data into a Simple Ocean Model for the Initialization of an Intermediate Tropical Coupled Ocean-Atmosphere Forecast Model

    Source: Monthly Weather Review:;1995:;volume( 123 ):;issue: 010::page 3103
    Author:
    Kleeman, Richard
    ,
    Moore, Andrew M.
    ,
    Smith, Neville R.
    DOI: 10.1175/1520-0493(1995)123<3103:AOSTDI>2.0.CO;2
    Publisher: American Meteorological Society
    Abstract: An adjoint variational assimilation technique is used to assimilate observations of both the oceanic state and wind stress data into an intermediate coupled ENSO prediction model. This method of initialization is contrasted with the more usual method, which uses only wind stress data to establish the initial state of the ocean. It is shown that ocean temperature data has a positive impact on the prediction skill in such models. On the basis of hindcasts for the period 1982?91, it is shown that NIN03 SST anomaly correlations greater than 0.7 can be obtained for hindcasts of duration up to 13 months and greater than 0.6 up to 16 months. There are also clear indications of skill at two years.
    • Download: (1.005Mb)
    • Show Full MetaData Hide Full MetaData
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      Assimilation of Subsurface Thermal Data into a Simple Ocean Model for the Initialization of an Intermediate Tropical Coupled Ocean-Atmosphere Forecast Model

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4203527
    Collections
    • Monthly Weather Review

    Show full item record

    contributor authorKleeman, Richard
    contributor authorMoore, Andrew M.
    contributor authorSmith, Neville R.
    date accessioned2017-06-09T16:10:31Z
    date available2017-06-09T16:10:31Z
    date copyright1995/10/01
    date issued1995
    identifier issn0027-0644
    identifier otherams-62615.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4203527
    description abstractAn adjoint variational assimilation technique is used to assimilate observations of both the oceanic state and wind stress data into an intermediate coupled ENSO prediction model. This method of initialization is contrasted with the more usual method, which uses only wind stress data to establish the initial state of the ocean. It is shown that ocean temperature data has a positive impact on the prediction skill in such models. On the basis of hindcasts for the period 1982?91, it is shown that NIN03 SST anomaly correlations greater than 0.7 can be obtained for hindcasts of duration up to 13 months and greater than 0.6 up to 16 months. There are also clear indications of skill at two years.
    publisherAmerican Meteorological Society
    titleAssimilation of Subsurface Thermal Data into a Simple Ocean Model for the Initialization of an Intermediate Tropical Coupled Ocean-Atmosphere Forecast Model
    typeJournal Paper
    journal volume123
    journal issue10
    journal titleMonthly Weather Review
    identifier doi10.1175/1520-0493(1995)123<3103:AOSTDI>2.0.CO;2
    journal fristpage3103
    journal lastpage3114
    treeMonthly Weather Review:;1995:;volume( 123 ):;issue: 010
    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