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    Four-Dimensional Variational Data Assimilation for the Blizzard of 2000

    Source: Monthly Weather Review:;2002:;volume( 130 ):;issue: 008::page 1967
    Author:
    Zupanski, Milija
    ,
    Zupanski, Dusanka
    ,
    Parrish, David F.
    ,
    Rogers, Eric
    ,
    DiMego, Geoffrey
    DOI: 10.1175/1520-0493(2002)130<1967:FDVDAF>2.0.CO;2
    Publisher: American Meteorological Society
    Abstract: Four-dimensional variational (4DVAR) data assimilation experiments for the East Coast winter storm of 25 January 2000 (i.e., ?blizzard of 2000?) were performed. This storm has received wide attention in the United States, because it was one of the major failures of the operational forecast system. All operational models of the U.S. National Weather Service (NWS) failed to produce heavy precipitation over the Carolina?New Jersey corridor, especially during the early stage of the storm development. The considered analysis cycle of this study is that of 0000 to 1200 UTC 24 January. This period was chosen because the forecast from 1200 UTC 24 January had the most damaging guidance for the forecasters at the National Weather Service offices and elsewhere. In the first set of experiments, the assimilation and forecast results between the 4DVAR and the operational three-dimensional variational (3DVAR) data assimilation method are compared. The most striking difference is in the accumulated precipitation amounts. The 4DVAR experiment produced almost perfect 24-h accumulated precipitation during the first 24 h of the forecast (after data assimilation), with accurate heavy precipitation over North and South Carolina. The operational 3DVAR-based forecast badly underforecast precipitation. The reason for the difference is traced back to the initial conditions. Apparently, the 4DVAR data assimilation was able to create strong surface convergence and an excess of precipitable water over Georgia. This initial convection was strengthened by a low-level jet in the next 6?12 h, finally resulting in a deep convection throughout the troposphere. In the second set of experiments, the impact of model error adjustment and precipitation assimilation is examined by comparing the forecasts initiated from various 4DVAR experiments. The results strongly indicate the need for the model error adjustment in the 4DVAR algorithm, as well as the clear benefit of assimilation of the hourly accumulated precipitation.
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      Four-Dimensional Variational Data Assimilation for the Blizzard of 2000

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4205048
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    • Monthly Weather Review

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    contributor authorZupanski, Milija
    contributor authorZupanski, Dusanka
    contributor authorParrish, David F.
    contributor authorRogers, Eric
    contributor authorDiMego, Geoffrey
    date accessioned2017-06-09T16:14:31Z
    date available2017-06-09T16:14:31Z
    date copyright2002/08/01
    date issued2002
    identifier issn0027-0644
    identifier otherams-63985.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4205048
    description abstractFour-dimensional variational (4DVAR) data assimilation experiments for the East Coast winter storm of 25 January 2000 (i.e., ?blizzard of 2000?) were performed. This storm has received wide attention in the United States, because it was one of the major failures of the operational forecast system. All operational models of the U.S. National Weather Service (NWS) failed to produce heavy precipitation over the Carolina?New Jersey corridor, especially during the early stage of the storm development. The considered analysis cycle of this study is that of 0000 to 1200 UTC 24 January. This period was chosen because the forecast from 1200 UTC 24 January had the most damaging guidance for the forecasters at the National Weather Service offices and elsewhere. In the first set of experiments, the assimilation and forecast results between the 4DVAR and the operational three-dimensional variational (3DVAR) data assimilation method are compared. The most striking difference is in the accumulated precipitation amounts. The 4DVAR experiment produced almost perfect 24-h accumulated precipitation during the first 24 h of the forecast (after data assimilation), with accurate heavy precipitation over North and South Carolina. The operational 3DVAR-based forecast badly underforecast precipitation. The reason for the difference is traced back to the initial conditions. Apparently, the 4DVAR data assimilation was able to create strong surface convergence and an excess of precipitable water over Georgia. This initial convection was strengthened by a low-level jet in the next 6?12 h, finally resulting in a deep convection throughout the troposphere. In the second set of experiments, the impact of model error adjustment and precipitation assimilation is examined by comparing the forecasts initiated from various 4DVAR experiments. The results strongly indicate the need for the model error adjustment in the 4DVAR algorithm, as well as the clear benefit of assimilation of the hourly accumulated precipitation.
    publisherAmerican Meteorological Society
    titleFour-Dimensional Variational Data Assimilation for the Blizzard of 2000
    typeJournal Paper
    journal volume130
    journal issue8
    journal titleMonthly Weather Review
    identifier doi10.1175/1520-0493(2002)130<1967:FDVDAF>2.0.CO;2
    journal fristpage1967
    journal lastpage1988
    treeMonthly Weather Review:;2002:;volume( 130 ):;issue: 008
    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