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    • Graduate School - MU Theses and Dissertations (MU)
    • Theses and Dissertations (MU)
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    • 2009 Dissertations (MU)
    • 2009 MU dissertations - Access restricted to MU
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    Hierarchical physical-statistical forecasting in the atmospheric sciences

    Song, Yong, 1974-.
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    [PDF] short.pdf (12.92Kb)
    [PDF] research.pdf (5.793Mb)
    Date
    2009
    Format
    Thesis
    Metadata
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    Abstract
    [ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] A class of hierarchical Bayesian models is introduced for Physical-Statistical forecasting purposes in the Atmospheric Sciences. The first project describes a methodological approach to implement a stochastic trigger function for convective initiation in the Kain-Fritsch (KF) convective parameterization scheme within the Penn State/NCAR Mesoscale Model version 5 (MM5). The second project introduces a spatio-temporal dynamic model that has a physical basis and incorporates Bayesian parameterizations and sequential importance sampling estimation to track and forecast the movement of multiple storm cells. The third project describes a finite difference model, in the framework of Bayesian hierarchical modeling (BHM), for investigating the possibility of forecasting the change of relative vorticity on a constant pressure surface in the middle troposphere over the globe.
    URI
    https://hdl.handle.net/10355/9689
    https://doi.org/10.32469/10355/9689
    Degree
    Ph. D.
    Thesis Department
    Statistics (MU)
    Rights
    Access is limited to the campus of the University of Missouri--Columbia.
    Collections
    • 2009 MU dissertations - Access restricted to MU
    • Statistics electronic theses and dissertations (MU)

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