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    The Analog Method as a Simple Statistical Downscaling Technique: Comparison with More Complicated Methods

    Source: Journal of Climate:;1999:;volume( 012 ):;issue: 008::page 2474
    Author:
    Zorita, Eduardo
    ,
    von Storch, Hans
    DOI: 10.1175/1520-0442(1999)012<2474:TAMAAS>2.0.CO;2
    Publisher: American Meteorological Society
    Abstract: The derivation of local scale information from integrations of coarse-resolution general circulation models (GCM) with the help of statistical models fitted to present observations is generally referred to as statistical downscaling. In this paper a relatively simple analog method is described and applied for downscaling purposes. According to this method the large-scale circulation simulated by a GCM is associated with the local variables observed simultaneously with the most similar large-scale circulation pattern in a pool of historical observations. The similarity of the large-scale circulation patterns is defined in terms of their coordinates in the space spanned by the leading observed empirical orthogonal functions. The method can be checked by replicating the evolution of the local variables in an independent period. Its performance for monthly and daily winter rainfall in the Iberian Peninsula is compared to more complicated techniques, each belonging to one of the broad families of existing statistical downscaling techniques: a method based on canonical correlation analysis, as representative of linear methods; a method based on classification and regression trees, as representative of a weather generator based on classification methods; and a neural network, as an example of deterministic nonlinear methods. It is found in these applications that the analog method performs in general as well as the more complicated methods, and it can be applied to both normally and nonnormally distributed local variables. Furthermore, it produces the right level of variability of the local variable and preserves the spatial covariance between local variables. On the other hand linear multivariate methods offer a clearer physical interpretation that supports more strongly its validity in an altered climate. Classification and neural networks are generally more complicated methods and do not directly offer a physical interpretation.
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      The Analog Method as a Simple Statistical Downscaling Technique: Comparison with More Complicated Methods

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    contributor authorZorita, Eduardo
    contributor authorvon Storch, Hans
    date accessioned2017-06-09T15:45:47Z
    date available2017-06-09T15:45:47Z
    date copyright1999/08/01
    date issued1999
    identifier issn0894-8755
    identifier otherams-5278.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4192600
    description abstractThe derivation of local scale information from integrations of coarse-resolution general circulation models (GCM) with the help of statistical models fitted to present observations is generally referred to as statistical downscaling. In this paper a relatively simple analog method is described and applied for downscaling purposes. According to this method the large-scale circulation simulated by a GCM is associated with the local variables observed simultaneously with the most similar large-scale circulation pattern in a pool of historical observations. The similarity of the large-scale circulation patterns is defined in terms of their coordinates in the space spanned by the leading observed empirical orthogonal functions. The method can be checked by replicating the evolution of the local variables in an independent period. Its performance for monthly and daily winter rainfall in the Iberian Peninsula is compared to more complicated techniques, each belonging to one of the broad families of existing statistical downscaling techniques: a method based on canonical correlation analysis, as representative of linear methods; a method based on classification and regression trees, as representative of a weather generator based on classification methods; and a neural network, as an example of deterministic nonlinear methods. It is found in these applications that the analog method performs in general as well as the more complicated methods, and it can be applied to both normally and nonnormally distributed local variables. Furthermore, it produces the right level of variability of the local variable and preserves the spatial covariance between local variables. On the other hand linear multivariate methods offer a clearer physical interpretation that supports more strongly its validity in an altered climate. Classification and neural networks are generally more complicated methods and do not directly offer a physical interpretation.
    publisherAmerican Meteorological Society
    titleThe Analog Method as a Simple Statistical Downscaling Technique: Comparison with More Complicated Methods
    typeJournal Paper
    journal volume12
    journal issue8
    journal titleJournal of Climate
    identifier doi10.1175/1520-0442(1999)012<2474:TAMAAS>2.0.CO;2
    journal fristpage2474
    journal lastpage2489
    treeJournal of Climate:;1999:;volume( 012 ):;issue: 008
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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