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    Global Sea Surface Temperature Forecasts Using a Pairwise Dynamic Combination Approach

    Source: Journal of Climate:;2010:;volume( 024 ):;issue: 007::page 1869
    Author:
    Chowdhury, Shahadat
    ,
    Sharma, Ashish
    DOI: 10.1175/2010JCLI3632.1
    Publisher: American Meteorological Society
    Abstract: his paper dynamically combined three multivariate forecasts where spatially and temporally variant combination weights are estimated using a nearest-neighbor approach. The case study presented combines forecasts from three climate models for the period 1958?2001. The variables of interest here are the monthly global sea surface temperature anomalies (SSTA) at a 5° ? 5° latitude?longitude grid, predicted 3 months in advance. The forecast from the static weight combination is used as the base case for comparison. The forecasted sea surface temperature using the dynamic combination algorithm offers consistent improvements over the static combination approach for all seasons. This improved skill is achieved over at least 93% of the global grid cells, in four 10-yr independent validation segments. Dynamically combined forecasts reduce the mean-square error of the SSTA by at least 25% for 72% of the global grid cells when compared against the best-performing single forecast among the three climate models considered.
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      Global Sea Surface Temperature Forecasts Using a Pairwise Dynamic Combination Approach

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4212415
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    contributor authorChowdhury, Shahadat
    contributor authorSharma, Ashish
    date accessioned2017-06-09T16:35:43Z
    date available2017-06-09T16:35:43Z
    date copyright2011/04/01
    date issued2010
    identifier issn0894-8755
    identifier otherams-70614.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4212415
    description abstracthis paper dynamically combined three multivariate forecasts where spatially and temporally variant combination weights are estimated using a nearest-neighbor approach. The case study presented combines forecasts from three climate models for the period 1958?2001. The variables of interest here are the monthly global sea surface temperature anomalies (SSTA) at a 5° ? 5° latitude?longitude grid, predicted 3 months in advance. The forecast from the static weight combination is used as the base case for comparison. The forecasted sea surface temperature using the dynamic combination algorithm offers consistent improvements over the static combination approach for all seasons. This improved skill is achieved over at least 93% of the global grid cells, in four 10-yr independent validation segments. Dynamically combined forecasts reduce the mean-square error of the SSTA by at least 25% for 72% of the global grid cells when compared against the best-performing single forecast among the three climate models considered.
    publisherAmerican Meteorological Society
    titleGlobal Sea Surface Temperature Forecasts Using a Pairwise Dynamic Combination Approach
    typeJournal Paper
    journal volume24
    journal issue7
    journal titleJournal of Climate
    identifier doi10.1175/2010JCLI3632.1
    journal fristpage1869
    journal lastpage1877
    treeJournal of Climate:;2010:;volume( 024 ):;issue: 007
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian