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    Scale-Dependent Representation of the Information Content of Observations in the Global Ensemble Kalman Filter Data Assimilation

    Source: Monthly Weather Review:;2016:;volume( 144 ):;issue: 008::page 2927
    Author:
    Žagar, Nedjeljka
    ,
    Anderson, Jeffrey
    ,
    Collins, Nancy
    ,
    Hoar, Timothy
    ,
    Raeder, Kevin
    ,
    Lei, Lili
    ,
    Tribbia, Joseph
    DOI: 10.1175/MWR-D-15-0401.1
    Publisher: American Meteorological Society
    Abstract: lobal data assimilation systems for numerical weather prediction (NWP) are characterized by significant uncertainties in tropical analysis fields. Furthermore, the largest spread of global ensemble forecasts in the short range on all scales is in the tropics. The presented results suggest that these properties hold even in the perfect-model framework and the ensemble Kalman filter data assimilation with a globally homogeneous network of wind and temperature profiles. The reasons for this are discussed by using the modal analysis, which provides information about the scale dependency of analysis and forecast uncertainties and information about the efficiency of data assimilation to reduce the prior uncertainties in the balanced and inertio-gravity dynamics.The scale-dependent representation of variance reduction of the prior ensemble by the data assimilation shows that the peak efficiency of data assimilation is on the synoptic scales in the midlatitudes that are associated with quasigeostrophic dynamics. In contrast, the variance associated with the inertia?gravity modes is less successfully reduced on all scales. A smaller information content of observations on planetary scales with respect to the synoptic scales is discussed in relation to the large-scale tropical uncertainties that current data assimilation methodologies do not address successfully. In addition, it is shown that a smaller reduction of the large-scale uncertainties in the prior state for NWP in the tropics than in the midlatitudes is influenced by the applied radius for the covariance localization.
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      Scale-Dependent Representation of the Information Content of Observations in the Global Ensemble Kalman Filter Data Assimilation

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4230868
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    • Monthly Weather Review

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    contributor authorŽagar, Nedjeljka
    contributor authorAnderson, Jeffrey
    contributor authorCollins, Nancy
    contributor authorHoar, Timothy
    contributor authorRaeder, Kevin
    contributor authorLei, Lili
    contributor authorTribbia, Joseph
    date accessioned2017-06-09T17:33:39Z
    date available2017-06-09T17:33:39Z
    date copyright2016/08/01
    date issued2016
    identifier issn0027-0644
    identifier otherams-87222.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4230868
    description abstractlobal data assimilation systems for numerical weather prediction (NWP) are characterized by significant uncertainties in tropical analysis fields. Furthermore, the largest spread of global ensemble forecasts in the short range on all scales is in the tropics. The presented results suggest that these properties hold even in the perfect-model framework and the ensemble Kalman filter data assimilation with a globally homogeneous network of wind and temperature profiles. The reasons for this are discussed by using the modal analysis, which provides information about the scale dependency of analysis and forecast uncertainties and information about the efficiency of data assimilation to reduce the prior uncertainties in the balanced and inertio-gravity dynamics.The scale-dependent representation of variance reduction of the prior ensemble by the data assimilation shows that the peak efficiency of data assimilation is on the synoptic scales in the midlatitudes that are associated with quasigeostrophic dynamics. In contrast, the variance associated with the inertia?gravity modes is less successfully reduced on all scales. A smaller information content of observations on planetary scales with respect to the synoptic scales is discussed in relation to the large-scale tropical uncertainties that current data assimilation methodologies do not address successfully. In addition, it is shown that a smaller reduction of the large-scale uncertainties in the prior state for NWP in the tropics than in the midlatitudes is influenced by the applied radius for the covariance localization.
    publisherAmerican Meteorological Society
    titleScale-Dependent Representation of the Information Content of Observations in the Global Ensemble Kalman Filter Data Assimilation
    typeJournal Paper
    journal volume144
    journal issue8
    journal titleMonthly Weather Review
    identifier doi10.1175/MWR-D-15-0401.1
    journal fristpage2927
    journal lastpage2945
    treeMonthly Weather Review:;2016:;volume( 144 ):;issue: 008
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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