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    Statistical Precipitation Downscaling over the Northwestern United States Using Numerically Simulated Precipitation as a Predictor

    Source: Journal of Climate:;2003:;volume( 016 ):;issue: 005::page 799
    Author:
    Widmann, Martin
    ,
    Bretherton, Christopher S.
    ,
    Salathé, Eric P.
    DOI: 10.1175/1520-0442(2003)016<0799:SPDOTN>2.0.CO;2
    Publisher: American Meteorological Society
    Abstract: This study investigates whether GCM-simulated precipitation is a good predictor for regional precipitation over Washington and Oregon. In order to allow for a detailed comparison of the estimated precipitation with observations, the simulated precipitation is taken from the NCEP?NCAR reanalysis, which nearly perfectly represents the historic pressure, temperature, and humidity, but calculates precipitation according to the model physics and parameterizations. Three statistical downscaling methods are investigated: (i) local rescaling of the simulated precipitation, and two newly developed methods, namely, (ii) downscaling using singular value decomposition (SVD) with simulated precipitation as the predictor, and (iii) local rescaling with a dynamical correction. Both local scaling methods are straightforward to apply to GCMs that are used for climate change experiments and seasonal forecasts, since they only need control runs for model fitting. The SVD method requires for model fitting special reanalysis-type GCM runs nudged toward observations from a historical period (selection of analogs from the GCM chosen to optimally match the historical weather states might achieve similar results). The precipitation-based methods are compared with conventional statistical downscaling using SVD with various large-scale predictors such as geopotential height, temperature, and humidity. The skill of the different methods for reconstructing historical wintertime precipitation (1958?94) over Oregon and Washington is tested on various spatial scales as small as 50 km and on temporal scales from months to decades. All methods using precipitation as a predictor perform considerably better than the conventional downscaling. The best results using conventional methods are obtained with geopotential height at 1000 hPa or humidity at 850 hPa as predictors. In these cases correlations of monthly observed and reconstructed precipitation on the 50-km scale range from 0.43 to 0.65. The inclusion of several predictor fields does not improve the reconstructions, since they are all highly correlated. Local rescaling of simulated precipitation yields much higher correlations between 0.7 and 0.9, with the exception of the rain shadow of the Cascade Mountains in the Columbia Basin (eastern Washington). When the simulated precipitation is used as a predictor in SVD-based downscaling correlations also reach 0.7 in eastern Washington. Dynamical correction improves the local scaling considerably in the rain shadow and yields correlations almost as high as with the SVD method. Its combination of high skill and ease to use make it particularly attractive for GCM precipitation downscaling.
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      Statistical Precipitation Downscaling over the Northwestern United States Using Numerically Simulated Precipitation as a Predictor

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4203456
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    contributor authorWidmann, Martin
    contributor authorBretherton, Christopher S.
    contributor authorSalathé, Eric P.
    date accessioned2017-06-09T16:10:21Z
    date available2017-06-09T16:10:21Z
    date copyright2003/03/01
    date issued2003
    identifier issn0894-8755
    identifier otherams-6255.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4203456
    description abstractThis study investigates whether GCM-simulated precipitation is a good predictor for regional precipitation over Washington and Oregon. In order to allow for a detailed comparison of the estimated precipitation with observations, the simulated precipitation is taken from the NCEP?NCAR reanalysis, which nearly perfectly represents the historic pressure, temperature, and humidity, but calculates precipitation according to the model physics and parameterizations. Three statistical downscaling methods are investigated: (i) local rescaling of the simulated precipitation, and two newly developed methods, namely, (ii) downscaling using singular value decomposition (SVD) with simulated precipitation as the predictor, and (iii) local rescaling with a dynamical correction. Both local scaling methods are straightforward to apply to GCMs that are used for climate change experiments and seasonal forecasts, since they only need control runs for model fitting. The SVD method requires for model fitting special reanalysis-type GCM runs nudged toward observations from a historical period (selection of analogs from the GCM chosen to optimally match the historical weather states might achieve similar results). The precipitation-based methods are compared with conventional statistical downscaling using SVD with various large-scale predictors such as geopotential height, temperature, and humidity. The skill of the different methods for reconstructing historical wintertime precipitation (1958?94) over Oregon and Washington is tested on various spatial scales as small as 50 km and on temporal scales from months to decades. All methods using precipitation as a predictor perform considerably better than the conventional downscaling. The best results using conventional methods are obtained with geopotential height at 1000 hPa or humidity at 850 hPa as predictors. In these cases correlations of monthly observed and reconstructed precipitation on the 50-km scale range from 0.43 to 0.65. The inclusion of several predictor fields does not improve the reconstructions, since they are all highly correlated. Local rescaling of simulated precipitation yields much higher correlations between 0.7 and 0.9, with the exception of the rain shadow of the Cascade Mountains in the Columbia Basin (eastern Washington). When the simulated precipitation is used as a predictor in SVD-based downscaling correlations also reach 0.7 in eastern Washington. Dynamical correction improves the local scaling considerably in the rain shadow and yields correlations almost as high as with the SVD method. Its combination of high skill and ease to use make it particularly attractive for GCM precipitation downscaling.
    publisherAmerican Meteorological Society
    titleStatistical Precipitation Downscaling over the Northwestern United States Using Numerically Simulated Precipitation as a Predictor
    typeJournal Paper
    journal volume16
    journal issue5
    journal titleJournal of Climate
    identifier doi10.1175/1520-0442(2003)016<0799:SPDOTN>2.0.CO;2
    journal fristpage799
    journal lastpage816
    treeJournal of Climate:;2003:;volume( 016 ):;issue: 005
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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