Backward Fokker-planck Equation for Determination Of

نویسندگان

  • Peter C. Chu
  • Leonid M. Ivanov
چکیده

1. INTRODUCTION It is widely recognized that uncertainty in atmospheric and oceanic models can be traced back to two factors (Lorenz 1984a, 1987). First, in defining the state of atmosphere (or ocean), a number of errors are involved arising from the finite resolution of measurement or from discretization in a numerical experiment, as a result of which small-scale " subgrid " processes are either discarded or parameterized. Second, once present, small errors of the kind mentioned above trigger a complex response leading to their subsequent amplification. The model predictability versus boundary condition error was discussed by Chu (1999) using the Lorenz system. The model predictability can be measured by two parameters: instantaneous error (IE) and predictability time (PT). The IE and PT are used for models with and without given initial condition errors, respectively. The IE measure is widely used for model evaluation. The predictability is regarded as the model error growth due to the initial condition error. This implies that the initial condition error should be given. The evaluation process becomes to study the stability of the dynamical system with a given initial condition error and to determine either the leading (largest) Lyapunov exponent (e.g., Lorenz 1969) or the amplification factors calculated from the leading singular vectors (e.g., Farrell and Ioannou 1996 a, b). It is well known that the stability analysis using the Lyapunov exponents and the singular vectors is not unique (Has'minskii, 1980). Probabilistic stability analysis becomes available in practical application (Ehrendorfer 1994 a, b; Nicolis 1992). The statistical properties of the prediction error are described through the probability density function (PDF) satisfying the Liouville equation or the Fokker-Plank equation. Solving this

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تاریخ انتشار 2003