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    Simultaneous Estimation of Microphysical Parameters and Atmospheric State with Simulated Radar Data and Ensemble Square Root Kalman Filter. Part I: Sensitivity Analysis and Parameter Identifiability

    Source: Monthly Weather Review:;2008:;volume( 136 ):;issue: 005::page 1630
    Author:
    Tong, Mingjing
    ,
    Xue, Ming
    DOI: 10.1175/2007MWR2070.1
    Publisher: American Meteorological Society
    Abstract: The possibility of estimating fundamental parameters common in single-moment ice microphysics schemes using radar observations is investigated for a model-simulated supercell storm by examining parameter sensitivity and identifiability. These parameters include the intercept parameters for rain, snow, and hail/graupel, and the bulk densities of snow and hail/graupel. These parameters are closely involved in the definition of drop/particle size distributions of microphysical species but often assume highly uncertain specified values. The sensitivity of model forecast within data assimilation cycles to the parameter values, and the issue of solution uniqueness of the estimation problem, are examined. The ensemble square root filter (EnSRF) is employed for model state estimation. Sensitivity experiments show that the errors in the microphysical parameters have a larger impact on model microphysical fields than on wind fields; radar reflectivity observations are therefore preferred over those of radial velocity for microphysical parameter estimation. The model response time to errors in individual parameters are also investigated. The results suggest that radar data should be used at about 5-min intervals for parameter estimation. The response functions calculated from ensemble mean forecasts for all five individual parameters show concave shapes, with unique minima occurring at or very close to the true values; therefore, true values of these parameters can be retrieved at least in those cases where only one parameter contains error. The identifiability of multiple parameters together is evaluated from their correlations with forecast reflectivity. Significant levels of correlation are found that can be interpreted physically. As the number of uncertain parameters increases, both the level and the area coverage of significant correlations decrease, implying increased difficulties with multiple-parameter estimation. The details of the estimation procedure and the results of a complete set of estimation experiments are presented in Part II of this paper.
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      Simultaneous Estimation of Microphysical Parameters and Atmospheric State with Simulated Radar Data and Ensemble Square Root Kalman Filter. Part I: Sensitivity Analysis and Parameter Identifiability

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4207566
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    contributor authorTong, Mingjing
    contributor authorXue, Ming
    date accessioned2017-06-09T16:21:00Z
    date available2017-06-09T16:21:00Z
    date copyright2008/05/01
    date issued2008
    identifier issn0027-0644
    identifier otherams-66251.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4207566
    description abstractThe possibility of estimating fundamental parameters common in single-moment ice microphysics schemes using radar observations is investigated for a model-simulated supercell storm by examining parameter sensitivity and identifiability. These parameters include the intercept parameters for rain, snow, and hail/graupel, and the bulk densities of snow and hail/graupel. These parameters are closely involved in the definition of drop/particle size distributions of microphysical species but often assume highly uncertain specified values. The sensitivity of model forecast within data assimilation cycles to the parameter values, and the issue of solution uniqueness of the estimation problem, are examined. The ensemble square root filter (EnSRF) is employed for model state estimation. Sensitivity experiments show that the errors in the microphysical parameters have a larger impact on model microphysical fields than on wind fields; radar reflectivity observations are therefore preferred over those of radial velocity for microphysical parameter estimation. The model response time to errors in individual parameters are also investigated. The results suggest that radar data should be used at about 5-min intervals for parameter estimation. The response functions calculated from ensemble mean forecasts for all five individual parameters show concave shapes, with unique minima occurring at or very close to the true values; therefore, true values of these parameters can be retrieved at least in those cases where only one parameter contains error. The identifiability of multiple parameters together is evaluated from their correlations with forecast reflectivity. Significant levels of correlation are found that can be interpreted physically. As the number of uncertain parameters increases, both the level and the area coverage of significant correlations decrease, implying increased difficulties with multiple-parameter estimation. The details of the estimation procedure and the results of a complete set of estimation experiments are presented in Part II of this paper.
    publisherAmerican Meteorological Society
    titleSimultaneous Estimation of Microphysical Parameters and Atmospheric State with Simulated Radar Data and Ensemble Square Root Kalman Filter. Part I: Sensitivity Analysis and Parameter Identifiability
    typeJournal Paper
    journal volume136
    journal issue5
    journal titleMonthly Weather Review
    identifier doi10.1175/2007MWR2070.1
    journal fristpage1630
    journal lastpage1648
    treeMonthly Weather Review:;2008:;volume( 136 ):;issue: 005
    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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