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    Cloud-Resolving Hurricane Initialization and Prediction through Assimilation of Doppler Radar Observations with an Ensemble Kalman Filter

    Source: Monthly Weather Review:;2009:;volume( 137 ):;issue: 007::page 2105
    Author:
    Zhang, Fuqing
    ,
    Weng, Yonghui
    ,
    Sippel, Jason A.
    ,
    Meng, Zhiyong
    ,
    Bishop, Craig H.
    DOI: 10.1175/2009MWR2645.1
    Publisher: American Meteorological Society
    Abstract: This study explores the assimilation of Doppler radar radial velocity observations for cloud-resolving hurricane analysis, initialization, and prediction with an ensemble Kalman filter (EnKF). The case studied is Hurricane Humberto (2007), the first landfalling hurricane in the United States since the end of the 2005 hurricane season and the most rapidly intensifying near-landfall storm in U.S. history. The storm caused extensive damage along the southeast Texas coast but was poorly predicted by operational models and forecasters. It is found that the EnKF analysis, after assimilating radial velocity observations from three Weather Surveillance Radars-1988 Doppler (WSR-88Ds) along the Gulf coast, closely represents the best-track position and intensity of Humberto. Deterministic forecasts initialized from the EnKF analysis, despite displaying considerable variability with different lead times, are also capable of predicting the rapid formation and intensification of the hurricane. These forecasts are also superior to simulations without radar data assimilation or with a three-dimensional variational scheme assimilating the same radar observations. Moreover, nearly all members from the ensemble forecasts initialized with EnKF analysis perturbations predict rapid formation and intensification of the storm. However, the large ensemble spread of peak intensity, which ranges from a tropical storm to a category 2 hurricane, echoes limited predictability in deterministic forecasts of the storm and the potential of using ensembles for probabilistic forecasts of hurricanes.
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      Cloud-Resolving Hurricane Initialization and Prediction through Assimilation of Doppler Radar Observations with an Ensemble Kalman Filter

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4211114
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    contributor authorZhang, Fuqing
    contributor authorWeng, Yonghui
    contributor authorSippel, Jason A.
    contributor authorMeng, Zhiyong
    contributor authorBishop, Craig H.
    date accessioned2017-06-09T16:31:39Z
    date available2017-06-09T16:31:39Z
    date copyright2009/07/01
    date issued2009
    identifier issn0027-0644
    identifier otherams-69444.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4211114
    description abstractThis study explores the assimilation of Doppler radar radial velocity observations for cloud-resolving hurricane analysis, initialization, and prediction with an ensemble Kalman filter (EnKF). The case studied is Hurricane Humberto (2007), the first landfalling hurricane in the United States since the end of the 2005 hurricane season and the most rapidly intensifying near-landfall storm in U.S. history. The storm caused extensive damage along the southeast Texas coast but was poorly predicted by operational models and forecasters. It is found that the EnKF analysis, after assimilating radial velocity observations from three Weather Surveillance Radars-1988 Doppler (WSR-88Ds) along the Gulf coast, closely represents the best-track position and intensity of Humberto. Deterministic forecasts initialized from the EnKF analysis, despite displaying considerable variability with different lead times, are also capable of predicting the rapid formation and intensification of the hurricane. These forecasts are also superior to simulations without radar data assimilation or with a three-dimensional variational scheme assimilating the same radar observations. Moreover, nearly all members from the ensemble forecasts initialized with EnKF analysis perturbations predict rapid formation and intensification of the storm. However, the large ensemble spread of peak intensity, which ranges from a tropical storm to a category 2 hurricane, echoes limited predictability in deterministic forecasts of the storm and the potential of using ensembles for probabilistic forecasts of hurricanes.
    publisherAmerican Meteorological Society
    titleCloud-Resolving Hurricane Initialization and Prediction through Assimilation of Doppler Radar Observations with an Ensemble Kalman Filter
    typeJournal Paper
    journal volume137
    journal issue7
    journal titleMonthly Weather Review
    identifier doi10.1175/2009MWR2645.1
    journal fristpage2105
    journal lastpage2125
    treeMonthly Weather Review:;2009:;volume( 137 ):;issue: 007
    contenttypeFulltext
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