• Browse
    • Author
    • Year
    • Platform
    • Organizations
    • Programs
    • Research Networks
    • Type
  • Search
    • Simple
    • Advanced
  • About
    • About
    • Policies
    • Citation Guide
  • Login
    Logo Alfred Wegener Institut
    Logo Alfred Wegener Institut
    Alfred-Wegener-Institut
    Helmholtz-Zentrum für Polar-
    und Meeresforschung
    • Imprint
    • Contact
    • OAI
    • RSS 2.0

    EPIC.awi.de

    Home
    • Browse
      • Author
      • Year
      • Platform
      • Organizations
      • Programs
      • Research Networks
      • Type
    • Search
      • Simple
      • Advanced
    • About
      • About
      • Policies
      • Citation Guide
    • Login
      Login

      Sequential parameter estimation for stochastic systems

      Edit Item Edit Item

      General Information:

      Citation:
      Kivman, G. (2003): Sequential parameter estimation for stochastic systems , Nonlinear Processes in Geophysics 10, pp. 253-259 .
      Cite this page as:
      hdl:10013/epic.15309
      Contact Email:
      gkivman@awi-bremerhaven.de
      Related Data:

      Download:

      [img]
      Preview
      PDF (Fulltext)
      Kiv2001a.pdf

      Download (632kB) | Preview
      Cite this document as:
      hdl:10013/epic.15309.d001
      Abstract:

      The quality of the prediction of the dynamical system evolutionis determined by the accuracy to which initial conditions andforcing are known. Availability of future observations permitsreducing the effects of errors in assessment the external modelparameters by means of a filtering algorithm. However, traditionalfiltering schemes do not take into account uncertainties in specifyingthe internal model parameters and thus cannot reduce their contributionto the forecast errors. An extension of the Sequential ImportanceResampling filter (SIR) is proposed to this aim. The filter is verifiedagainst the Ensemble Kalman filer (EnKF) in application to the stochasticLorenz system. It is shown that the SIR is capable to estimatethe system parameters and to predicts the evolution of the system witha remarkably better accuracy than the EnKF. This highlights a severedrawback of any Kalman filtering scheme: due to utilizing only first twostatistical moments in the analysis step it is unable to deal withprobability density functions badly approximated by the normaldistribution.

      Further Details:

      Item Type:
      Article
      Authors:
      Kivman, G.
      Divisions:
      AWI Organizations > Climate Sciences > Climate Dynamics
      Programs:
      Basic Research > Helmholtz Independent Research
      Eprint ID:
      4740
      Logo Alfred Wegener Institut
      Alfred-Wegener-Institut
      Helmholtz-Zentrum für Polar-
      und Meeresforschung
      Logo Helmholtz

      • Browse
        • Author
        • Year
        • Platform
        • Organizations
        • Programs
        • Research Networks
        • Type
      • Search
        • Simple
        • Advanced
      • About
        • About
        • Policies
        • Citation Guide
      • Imprint
      • Contact
      • OAI
      © Alfred-Wegener-Institut