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    Estimation of Uncertainty in Temperature Observations Made at Meteorological Stations Using a Probabilistic Spatiotemporal Approach

    Source: Journal of Applied Meteorology and Climatology:;2014:;volume( 053 ):;issue: 006::page 1538
    Author:
    Xu, Cheng-Dong
    ,
    Wang, Jin-Feng
    ,
    Hu, Mao-Gui
    ,
    Li, Qing-Xiang
    DOI: 10.1175/JAMC-D-13-0179.1
    Publisher: American Meteorological Society
    Abstract: probabilistic spatiotemporal approach based on a spatial regression test (SRT-PS) is proposed for the quality control of climate data. It provides a quantitative probability that represents the uncertainty in each temperature observation. The assumption of SRT-PS is that there might be large uncertainty in the station record if there is a large residual difference between the record estimated in the spatial regression test and the true station record. The result of SRT-PS is expressed as a confidence probability ranging from 0 to 1, where a value closer to 1 indicates less uncertainty. The potential of SRT-PS to estimate quantitatively the uncertainty in temperature observations was demonstrated using an annual temperature dataset for China for the period 1971?2000 with seeded errors. SRT-PS was also applied to assess a real dataset, and was compared with two traditional quality control approaches: biweight mean and biweight standard deviation and SRT. The study provides a new approach to assess quantitatively the uncertainty in temperature observations at meteorological stations.
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      Estimation of Uncertainty in Temperature Observations Made at Meteorological Stations Using a Probabilistic Spatiotemporal Approach

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4217173
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    contributor authorXu, Cheng-Dong
    contributor authorWang, Jin-Feng
    contributor authorHu, Mao-Gui
    contributor authorLi, Qing-Xiang
    date accessioned2017-06-09T16:49:50Z
    date available2017-06-09T16:49:50Z
    date copyright2014/06/01
    date issued2014
    identifier issn1558-8424
    identifier otherams-74898.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4217173
    description abstractprobabilistic spatiotemporal approach based on a spatial regression test (SRT-PS) is proposed for the quality control of climate data. It provides a quantitative probability that represents the uncertainty in each temperature observation. The assumption of SRT-PS is that there might be large uncertainty in the station record if there is a large residual difference between the record estimated in the spatial regression test and the true station record. The result of SRT-PS is expressed as a confidence probability ranging from 0 to 1, where a value closer to 1 indicates less uncertainty. The potential of SRT-PS to estimate quantitatively the uncertainty in temperature observations was demonstrated using an annual temperature dataset for China for the period 1971?2000 with seeded errors. SRT-PS was also applied to assess a real dataset, and was compared with two traditional quality control approaches: biweight mean and biweight standard deviation and SRT. The study provides a new approach to assess quantitatively the uncertainty in temperature observations at meteorological stations.
    publisherAmerican Meteorological Society
    titleEstimation of Uncertainty in Temperature Observations Made at Meteorological Stations Using a Probabilistic Spatiotemporal Approach
    typeJournal Paper
    journal volume53
    journal issue6
    journal titleJournal of Applied Meteorology and Climatology
    identifier doi10.1175/JAMC-D-13-0179.1
    journal fristpage1538
    journal lastpage1546
    treeJournal of Applied Meteorology and Climatology:;2014:;volume( 053 ):;issue: 006
    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