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    Intercomparison and Coupling of Ensemble and Four-Dimensional Variational Data Assimilation Methods for the Analysis and Forecasting of Hurricane Karl (2010)

    Source: Monthly Weather Review:;2014:;volume( 142 ):;issue: 009::page 3347
    Author:
    Poterjoy, Jonathan
    ,
    Zhang, Fuqing
    DOI: 10.1175/MWR-D-13-00394.1
    Publisher: American Meteorological Society
    Abstract: his study examines the performance of ensemble and variational data assimilation systems for the Weather Research and Forecasting (WRF) Model. These methods include an ensemble Kalman filter (EnKF), an incremental four-dimensional variational data assimilation (4DVar) system, and a hybrid system that uses a two-way coupling between the two approaches (E4DVar). The three methods are applied to assimilate routinely collected data and field observations over a 10-day period that spans the life cycle of Hurricane Karl (2010), including the pregenesis disturbance that preceded its development into a tropical cyclone. In general, forecasts from the E4DVar analyses are found to produce smaller 48?72-h forecast errors than the benchmark EnKF and 4DVar methods for all variables and verification methods tested in this study. The improved representation of low- and midlevel moisture and vorticity in the E4DVar analyses leads to more accurate track and intensity predictions by this system. In particular, E4DVar analyses provide persistently more skillful genesis and rapid intensification forecasts than the EnKF and 4DVar methods during cycling. The data assimilation experiments also expose additional benefits of the hybrid system in terms of physical balance, computational cost, and the treatment of asynoptic observations near the beginning of the assimilation window. These factors make it a practical data assimilation method for mesoscale analysis and forecasting, and for tropical cyclone prediction.
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      Intercomparison and Coupling of Ensemble and Four-Dimensional Variational Data Assimilation Methods for the Analysis and Forecasting of Hurricane Karl (2010)

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    contributor authorPoterjoy, Jonathan
    contributor authorZhang, Fuqing
    date accessioned2017-06-09T17:31:52Z
    date available2017-06-09T17:31:52Z
    date copyright2014/09/01
    date issued2014
    identifier issn0027-0644
    identifier otherams-86805.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4230404
    description abstracthis study examines the performance of ensemble and variational data assimilation systems for the Weather Research and Forecasting (WRF) Model. These methods include an ensemble Kalman filter (EnKF), an incremental four-dimensional variational data assimilation (4DVar) system, and a hybrid system that uses a two-way coupling between the two approaches (E4DVar). The three methods are applied to assimilate routinely collected data and field observations over a 10-day period that spans the life cycle of Hurricane Karl (2010), including the pregenesis disturbance that preceded its development into a tropical cyclone. In general, forecasts from the E4DVar analyses are found to produce smaller 48?72-h forecast errors than the benchmark EnKF and 4DVar methods for all variables and verification methods tested in this study. The improved representation of low- and midlevel moisture and vorticity in the E4DVar analyses leads to more accurate track and intensity predictions by this system. In particular, E4DVar analyses provide persistently more skillful genesis and rapid intensification forecasts than the EnKF and 4DVar methods during cycling. The data assimilation experiments also expose additional benefits of the hybrid system in terms of physical balance, computational cost, and the treatment of asynoptic observations near the beginning of the assimilation window. These factors make it a practical data assimilation method for mesoscale analysis and forecasting, and for tropical cyclone prediction.
    publisherAmerican Meteorological Society
    titleIntercomparison and Coupling of Ensemble and Four-Dimensional Variational Data Assimilation Methods for the Analysis and Forecasting of Hurricane Karl (2010)
    typeJournal Paper
    journal volume142
    journal issue9
    journal titleMonthly Weather Review
    identifier doi10.1175/MWR-D-13-00394.1
    journal fristpage3347
    journal lastpage3364
    treeMonthly Weather Review:;2014:;volume( 142 ):;issue: 009
    contenttypeFulltext
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