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    Use of Satellite-derived Rainfall for Improving Tropical Forecasts

    Source: Monthly Weather Review:;1992:;volume( 120 ):;issue: 011::page 2540
    Author:
    Mathur, Mukut B.
    ,
    Bedi, H. S.
    ,
    Krishnamurti, T. N.
    ,
    Kanamitsu, Masao
    ,
    Woollen, Jack S.
    DOI: 10.1175/1520-0493(1992)120<2540:UOSDRF>2.0.CO;2
    Publisher: American Meteorological Society
    Abstract: Sparsity of conventional data over tropical oceans makes it difficult to analyze well the moisture and divergence fields, and therefore the diabatic forcing of the tropical atmosphere is not well predicted in numerical models. A nudging procedure to improve the precipitation forecast in the National Meteorological Center (NMC) Medium Range Forecast Model (MRF) is developed. The convective parameterization scheme is modified to adjust the predicted rainfall amounts toward the observations in this method. In the absence of conventional data, the rainfall estimates from the satellite measures of the outward-going longwave radiation are utilized as the observed precipitation. Several forecasts from the MRF are presented to show the improvements in intensity and location of the intertropical convergence zone and tropical disturbances with the application of the nudging procedure. Additionally, spurious cyclone and excessive rainfall that were predicted without this procedure either failed to form or their intensifies were considerably reduced. Results from incorporation of the modified convective scheme in the global data-assimilation system within the NMC forecast model are also discussed. The analysis, the subsequent 72-h forecast circulation, and the rainfall amounts are improved with the use of this scheme.
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      Use of Satellite-derived Rainfall for Improving Tropical Forecasts

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4202896
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    • Monthly Weather Review

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    contributor authorMathur, Mukut B.
    contributor authorBedi, H. S.
    contributor authorKrishnamurti, T. N.
    contributor authorKanamitsu, Masao
    contributor authorWoollen, Jack S.
    date accessioned2017-06-09T16:09:01Z
    date available2017-06-09T16:09:01Z
    date copyright1992/11/01
    date issued1992
    identifier issn0027-0644
    identifier otherams-62047.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4202896
    description abstractSparsity of conventional data over tropical oceans makes it difficult to analyze well the moisture and divergence fields, and therefore the diabatic forcing of the tropical atmosphere is not well predicted in numerical models. A nudging procedure to improve the precipitation forecast in the National Meteorological Center (NMC) Medium Range Forecast Model (MRF) is developed. The convective parameterization scheme is modified to adjust the predicted rainfall amounts toward the observations in this method. In the absence of conventional data, the rainfall estimates from the satellite measures of the outward-going longwave radiation are utilized as the observed precipitation. Several forecasts from the MRF are presented to show the improvements in intensity and location of the intertropical convergence zone and tropical disturbances with the application of the nudging procedure. Additionally, spurious cyclone and excessive rainfall that were predicted without this procedure either failed to form or their intensifies were considerably reduced. Results from incorporation of the modified convective scheme in the global data-assimilation system within the NMC forecast model are also discussed. The analysis, the subsequent 72-h forecast circulation, and the rainfall amounts are improved with the use of this scheme.
    publisherAmerican Meteorological Society
    titleUse of Satellite-derived Rainfall for Improving Tropical Forecasts
    typeJournal Paper
    journal volume120
    journal issue11
    journal titleMonthly Weather Review
    identifier doi10.1175/1520-0493(1992)120<2540:UOSDRF>2.0.CO;2
    journal fristpage2540
    journal lastpage2560
    treeMonthly Weather Review:;1992:;volume( 120 ):;issue: 011
    contenttypeFulltext
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