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    A Localized Particle Filter for High-Dimensional Nonlinear Systems

    Source: Monthly Weather Review:;2015:;volume( 144 ):;issue: 001::page 59
    Author:
    Poterjoy, Jonathan
    DOI: 10.1175/MWR-D-15-0163.1
    Publisher: American Meteorological Society
    Abstract: his paper presents a new data assimilation approach based on the particle filter (PF) that has potential for nonlinear/non-Gaussian applications in geoscience. Particle filters provide a Monte Carlo approximation of a system?s probability density, while making no assumptions regarding the underlying error distribution. The proposed method is similar to the PF in that particles?also referred to as ensemble members?are weighted based on the likelihood of observations in order to approximate posterior probabilities of the system state. The new approach, denoted the local PF, extends the particle weights into vector quantities to reduce the influence of distant observations on the weight calculations via a localization function. While the number of particles required for standard PFs scales exponentially with the dimension of the system, the local PF provides accurate results using relatively few particles. In sensitivity experiments performed with a 40-variable dynamical system, the local PF requires only five particles to prevent filter divergence for both dense and sparse observation networks. Comparisons of the local PF and ensemble Kalman filters (EnKFs) reveal advantages of the new method in situations resembling geophysical data assimilation applications. In particular, the new filter demonstrates substantial benefits over EnKFs when observation networks consist of densely spaced measurements that relate nonlinearly to the model state?analogous to remotely sensed data used frequently in weather analyses.
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      A Localized Particle Filter for High-Dimensional Nonlinear Systems

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4230758
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    contributor authorPoterjoy, Jonathan
    date accessioned2017-06-09T17:33:08Z
    date available2017-06-09T17:33:08Z
    date copyright2016/01/01
    date issued2015
    identifier issn0027-0644
    identifier otherams-87123.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4230758
    description abstracthis paper presents a new data assimilation approach based on the particle filter (PF) that has potential for nonlinear/non-Gaussian applications in geoscience. Particle filters provide a Monte Carlo approximation of a system?s probability density, while making no assumptions regarding the underlying error distribution. The proposed method is similar to the PF in that particles?also referred to as ensemble members?are weighted based on the likelihood of observations in order to approximate posterior probabilities of the system state. The new approach, denoted the local PF, extends the particle weights into vector quantities to reduce the influence of distant observations on the weight calculations via a localization function. While the number of particles required for standard PFs scales exponentially with the dimension of the system, the local PF provides accurate results using relatively few particles. In sensitivity experiments performed with a 40-variable dynamical system, the local PF requires only five particles to prevent filter divergence for both dense and sparse observation networks. Comparisons of the local PF and ensemble Kalman filters (EnKFs) reveal advantages of the new method in situations resembling geophysical data assimilation applications. In particular, the new filter demonstrates substantial benefits over EnKFs when observation networks consist of densely spaced measurements that relate nonlinearly to the model state?analogous to remotely sensed data used frequently in weather analyses.
    publisherAmerican Meteorological Society
    titleA Localized Particle Filter for High-Dimensional Nonlinear Systems
    typeJournal Paper
    journal volume144
    journal issue1
    journal titleMonthly Weather Review
    identifier doi10.1175/MWR-D-15-0163.1
    journal fristpage59
    journal lastpage76
    treeMonthly Weather Review:;2015:;volume( 144 ):;issue: 001
    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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