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    Performance of Quality Assurance Procedures on Daily Precipitation

    Source: Journal of Atmospheric and Oceanic Technology:;2007:;volume( 024 ):;issue: 005::page 821
    Author:
    You, Jinsheng
    ,
    Hubbard, Kenneth G.
    ,
    Nadarajah, Saralees
    ,
    Kunkel, Kenneth E.
    DOI: 10.1175/JTECH2002.1
    Publisher: American Meteorological Society
    Abstract: The search for precipitation quality control (QC) methods has proven difficult. The high spatial and temporal variability associated with precipitation data causes high uncertainty and edge creep when regression-based approaches are applied. Precipitation frequency distributions are generally skewed rather than normally distributed. The commonly assumed normal distribution in QC methods is not a good representation of the actual distribution of precipitation and is inefficient in identifying the outliers. This paper first explores the use of a single gamma distribution, fit to all precipitation data, in a quality assurance test. A second test, the multiple intervals gamma distribution (MIGD) method, is introduced. It assumes that meteorological conditions that produce a certain range in average precipitation at surrounding stations will produce a predictable range at the target station. The MIGD bins the average of precipitation at neighboring stations; then, for the events in a specific bin, an associated gamma distribution is derived by fit to the same events at the target station. The new gamma distributions can then be used to establish the threshold for QC according to the user-selected probability of exceedance. This paper also explores a test (Q test) for precipitation, which uses a metric based on comparisons with neighboring stations. The performance of the three approaches is evaluated by assessing the fraction of ?known? errors that can be identified in a seeded error dataset. The single gamma distribution and Q-test approach were found to be relatively efficient at identifying extreme precipitation values as potential outliers. However, the MIGD method outperforms the other two QC methods. This method identifies more seeded errors and results in fewer type I errors than the other methods. It will be adopted in the Applied Climatic Information System (ACIS) for precipitation quality control.
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      Performance of Quality Assurance Procedures on Daily Precipitation

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4227716
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    contributor authorYou, Jinsheng
    contributor authorHubbard, Kenneth G.
    contributor authorNadarajah, Saralees
    contributor authorKunkel, Kenneth E.
    date accessioned2017-06-09T17:23:30Z
    date available2017-06-09T17:23:30Z
    date copyright2007/05/01
    date issued2007
    identifier issn0739-0572
    identifier otherams-84386.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4227716
    description abstractThe search for precipitation quality control (QC) methods has proven difficult. The high spatial and temporal variability associated with precipitation data causes high uncertainty and edge creep when regression-based approaches are applied. Precipitation frequency distributions are generally skewed rather than normally distributed. The commonly assumed normal distribution in QC methods is not a good representation of the actual distribution of precipitation and is inefficient in identifying the outliers. This paper first explores the use of a single gamma distribution, fit to all precipitation data, in a quality assurance test. A second test, the multiple intervals gamma distribution (MIGD) method, is introduced. It assumes that meteorological conditions that produce a certain range in average precipitation at surrounding stations will produce a predictable range at the target station. The MIGD bins the average of precipitation at neighboring stations; then, for the events in a specific bin, an associated gamma distribution is derived by fit to the same events at the target station. The new gamma distributions can then be used to establish the threshold for QC according to the user-selected probability of exceedance. This paper also explores a test (Q test) for precipitation, which uses a metric based on comparisons with neighboring stations. The performance of the three approaches is evaluated by assessing the fraction of ?known? errors that can be identified in a seeded error dataset. The single gamma distribution and Q-test approach were found to be relatively efficient at identifying extreme precipitation values as potential outliers. However, the MIGD method outperforms the other two QC methods. This method identifies more seeded errors and results in fewer type I errors than the other methods. It will be adopted in the Applied Climatic Information System (ACIS) for precipitation quality control.
    publisherAmerican Meteorological Society
    titlePerformance of Quality Assurance Procedures on Daily Precipitation
    typeJournal Paper
    journal volume24
    journal issue5
    journal titleJournal of Atmospheric and Oceanic Technology
    identifier doi10.1175/JTECH2002.1
    journal fristpage821
    journal lastpage834
    treeJournal of Atmospheric and Oceanic Technology:;2007:;volume( 024 ):;issue: 005
    contenttypeFulltext
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    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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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