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    Downscaling Precipitation and Temperature with Temporal Neural Networks

    Source: Journal of Hydrometeorology:;2005:;Volume( 006 ):;issue: 004::page 483
    Author:
    Coulibaly, Paulin
    ,
    Dibike, Yonas B.
    ,
    Anctil, François
    DOI: 10.1175/JHM409.1
    Publisher: American Meteorological Society
    Abstract: The issues of downscaling the outputs of a global climate model (GCM) to a scale that is appropriate to hydrological impact studies are investigated using a temporal neural network approach. The time-lagged feed-forward neural network (TLFN) is proposed for downscaling daily total precipitation and daily maximum and minimum temperature series for the Serpent River watershed in northern Quebec (Canada). The downscaling models are developed and validated using large-scale predictor variables derived from the National Centers for Environmental Prediction?National Center for Atmospheric Research (NCEP?NCAR) reanalysis dataset. Atmospheric predictors such as specific humidity, wind velocity, and geopotential height are identified as the most relevant inputs to the downscaling models. The performance of the TLFN downscaling model is also compared to a statistical downscaling model (SDSM). The downscaling results suggest that the TLFN is an efficient method for downscaling both daily precipitation and temperature series. The best downscaling models were then applied to the outputs of the Canadian Global Climate Model (CGCM1), forced with the Intergovernmental Panel on Climate Change (IPCC) IS92a scenario. Changes in average precipitation between the current and the future scenarios predicted by the TLFN are generally found to be smaller than those predicted by the SDSM model. Furthermore, application of the downscaled data for hydrologic impact analysis in the Serpent River resulted in an overall increasing trend in mean annual flow as well as earlier spring peak flow. The results also demonstrate the emphasis that should be given in identifying the appropriate downscaling tools for impact studies by showing how a future climate scenario downscaled with different downscaling methods could result in significantly different hydrologic impact simulation results for the same watershed.
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      Downscaling Precipitation and Temperature with Temporal Neural Networks

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    contributor authorCoulibaly, Paulin
    contributor authorDibike, Yonas B.
    contributor authorAnctil, François
    date accessioned2017-06-09T17:13:42Z
    date available2017-06-09T17:13:42Z
    date copyright2005/08/01
    date issued2005
    identifier issn1525-755X
    identifier otherams-81416.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4224417
    description abstractThe issues of downscaling the outputs of a global climate model (GCM) to a scale that is appropriate to hydrological impact studies are investigated using a temporal neural network approach. The time-lagged feed-forward neural network (TLFN) is proposed for downscaling daily total precipitation and daily maximum and minimum temperature series for the Serpent River watershed in northern Quebec (Canada). The downscaling models are developed and validated using large-scale predictor variables derived from the National Centers for Environmental Prediction?National Center for Atmospheric Research (NCEP?NCAR) reanalysis dataset. Atmospheric predictors such as specific humidity, wind velocity, and geopotential height are identified as the most relevant inputs to the downscaling models. The performance of the TLFN downscaling model is also compared to a statistical downscaling model (SDSM). The downscaling results suggest that the TLFN is an efficient method for downscaling both daily precipitation and temperature series. The best downscaling models were then applied to the outputs of the Canadian Global Climate Model (CGCM1), forced with the Intergovernmental Panel on Climate Change (IPCC) IS92a scenario. Changes in average precipitation between the current and the future scenarios predicted by the TLFN are generally found to be smaller than those predicted by the SDSM model. Furthermore, application of the downscaled data for hydrologic impact analysis in the Serpent River resulted in an overall increasing trend in mean annual flow as well as earlier spring peak flow. The results also demonstrate the emphasis that should be given in identifying the appropriate downscaling tools for impact studies by showing how a future climate scenario downscaled with different downscaling methods could result in significantly different hydrologic impact simulation results for the same watershed.
    publisherAmerican Meteorological Society
    titleDownscaling Precipitation and Temperature with Temporal Neural Networks
    typeJournal Paper
    journal volume6
    journal issue4
    journal titleJournal of Hydrometeorology
    identifier doi10.1175/JHM409.1
    journal fristpage483
    journal lastpage496
    treeJournal of Hydrometeorology:;2005:;Volume( 006 ):;issue: 004
    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
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