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    Global Data Assimilation and Forecast Experiments Using SSM/I Wind Speed Data Derived from a Neural Network Algorithm

    Source: Weather and Forecasting:;1997:;volume( 012 ):;issue: 004::page 859
    Author:
    Yu, T-W.
    ,
    Iredell, M.
    ,
    Keyser, D.
    DOI: 10.1175/1520-0434(1997)012<0859:GDAAFE>2.0.CO;2
    Publisher: American Meteorological Society
    Abstract: A neural network algorithm used in this study to derive Special Sensor Microwave/Imager (SSM/I) wind speeds from the Defense Meteorological Satellite Program satellite-observed brightness temperatures is briefly reviewed. The SSM/I winds derived from the neural network algorithm are not only of better quality, but also cover a larger area when compared to those generated from the currently operational Goodberlet algorithm. The areas of increased coverage occur mainly over the regions of active weather developments where the operational Goodberlet algorithm fails to produce good quality wind data due to high moisture contents of the atmosphere. These two main characteristics associated with the SSM/I winds derived from the neural network algorithm are discussed. SSM/I wind speed data derived from both the neural network algorithm and the operational Goodberlet algorithm are tested in parallel global data assimilation and forecast experiments for a period of about three weeks. The results show that the use of neural-network-derived SSM/I wind speed data leads to a greater improvement in the first-guess wind fields than use of wind data generated by the operational algorithm. Similarly, comparison of the forecast results shows that use of the neural-network-derived SSM/I wind speed data in the data assimilation and forecast experiment gives better forecasts when compared to those from the operational run that uses the SSM/I winds from the Goodberlet algorithm. These results of comparison between the two parallel analyses and forecasts from the global data assimilation experiments are discussed.
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      Global Data Assimilation and Forecast Experiments Using SSM/I Wind Speed Data Derived from a Neural Network Algorithm

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4166500
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    contributor authorYu, T-W.
    contributor authorIredell, M.
    contributor authorKeyser, D.
    date accessioned2017-06-09T14:54:08Z
    date available2017-06-09T14:54:08Z
    date copyright1997/12/01
    date issued1997
    identifier issn0882-8156
    identifier otherams-2929.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4166500
    description abstractA neural network algorithm used in this study to derive Special Sensor Microwave/Imager (SSM/I) wind speeds from the Defense Meteorological Satellite Program satellite-observed brightness temperatures is briefly reviewed. The SSM/I winds derived from the neural network algorithm are not only of better quality, but also cover a larger area when compared to those generated from the currently operational Goodberlet algorithm. The areas of increased coverage occur mainly over the regions of active weather developments where the operational Goodberlet algorithm fails to produce good quality wind data due to high moisture contents of the atmosphere. These two main characteristics associated with the SSM/I winds derived from the neural network algorithm are discussed. SSM/I wind speed data derived from both the neural network algorithm and the operational Goodberlet algorithm are tested in parallel global data assimilation and forecast experiments for a period of about three weeks. The results show that the use of neural-network-derived SSM/I wind speed data leads to a greater improvement in the first-guess wind fields than use of wind data generated by the operational algorithm. Similarly, comparison of the forecast results shows that use of the neural-network-derived SSM/I wind speed data in the data assimilation and forecast experiment gives better forecasts when compared to those from the operational run that uses the SSM/I winds from the Goodberlet algorithm. These results of comparison between the two parallel analyses and forecasts from the global data assimilation experiments are discussed.
    publisherAmerican Meteorological Society
    titleGlobal Data Assimilation and Forecast Experiments Using SSM/I Wind Speed Data Derived from a Neural Network Algorithm
    typeJournal Paper
    journal volume12
    journal issue4
    journal titleWeather and Forecasting
    identifier doi10.1175/1520-0434(1997)012<0859:GDAAFE>2.0.CO;2
    journal fristpage859
    journal lastpage865
    treeWeather and Forecasting:;1997:;volume( 012 ):;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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