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    Impact Assessment of Assimilating NASA’s RapidScat Surface Wind Retrievals in the NOAA Global Data Assimilation System

    Source: Monthly Weather Review:;2017:;volume 146:;issue 004::page 929
    Author:
    Liu, Ling
    ,
    Garrett, Kevin
    ,
    Maddy, Eric S.
    ,
    Boukabara, Sid-Ahmed
    DOI: 10.1175/MWR-D-16-0179.1
    Publisher: American Meteorological Society
    Abstract: AbstractThe National Aeronautics and Space Administration (NASA) RapidScat scatterometer on board the International Space Station (ISS) provides observations of surface winds that can be assimilated into numerical weather prediction (NWP) forecast models. In this study, the authors assess the data quality of the RapidScat Level 2B surface wind vector retrievals and the impact of those observations on the National Oceanic and Atmospheric Administration (NOAA) Global Forecast System (GFS). The RapidScat is found to provide quality measurements of surface wind speed and direction in nonprecipitating conditions and to provide observations that add both information and robustness to the global satellite observing system used in NWP models. The authors find that with an assumed uncertainty in wind speed of around 2 m s?1, the RapidScat has neutral impact on the short-range forecast of surface wind vectors in the tropics but improves both the analysis and background field of surface wind vectors. However, the deployment of RapidScat on the ISS presents some challenges for use of these wind vector observations in operational NWP, including frequent maneuvers of the spacecraft that could alter instrument performance.
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      Impact Assessment of Assimilating NASA’s RapidScat Surface Wind Retrievals in the NOAA Global Data Assimilation System

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4261140
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    • Monthly Weather Review

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    contributor authorLiu, Ling
    contributor authorGarrett, Kevin
    contributor authorMaddy, Eric S.
    contributor authorBoukabara, Sid-Ahmed
    date accessioned2019-09-19T10:03:55Z
    date available2019-09-19T10:03:55Z
    date copyright12/15/2017 12:00:00 AM
    date issued2017
    identifier othermwr-d-16-0179.1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4261140
    description abstractAbstractThe National Aeronautics and Space Administration (NASA) RapidScat scatterometer on board the International Space Station (ISS) provides observations of surface winds that can be assimilated into numerical weather prediction (NWP) forecast models. In this study, the authors assess the data quality of the RapidScat Level 2B surface wind vector retrievals and the impact of those observations on the National Oceanic and Atmospheric Administration (NOAA) Global Forecast System (GFS). The RapidScat is found to provide quality measurements of surface wind speed and direction in nonprecipitating conditions and to provide observations that add both information and robustness to the global satellite observing system used in NWP models. The authors find that with an assumed uncertainty in wind speed of around 2 m s?1, the RapidScat has neutral impact on the short-range forecast of surface wind vectors in the tropics but improves both the analysis and background field of surface wind vectors. However, the deployment of RapidScat on the ISS presents some challenges for use of these wind vector observations in operational NWP, including frequent maneuvers of the spacecraft that could alter instrument performance.
    publisherAmerican Meteorological Society
    titleImpact Assessment of Assimilating NASA’s RapidScat Surface Wind Retrievals in the NOAA Global Data Assimilation System
    typeJournal Paper
    journal volume146
    journal issue4
    journal titleMonthly Weather Review
    identifier doi10.1175/MWR-D-16-0179.1
    journal fristpage929
    journal lastpage942
    treeMonthly Weather Review:;2017:;volume 146:;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
    yabeshDSpacePersian