Recent developments in applied mathematics and mechanics: theory, experiment and practice. Devoted to the 80th anniversary of academician N.N.Yanenko

Akademgorodok, Novosibirsk, Russia, June 24 - 29, 2001

Abstracts

Novosibirsk participants

Application of the Kalman filter theory to the problem of the meteorological data assimilation

Klimova E.

Institute of Computational Technologies SB RAS (Novosibirsk)

The problem of recovery spatially - temporary distributions of meteorologic fields on observational data with engaging of prognostic models of atmosphere is named as a problem of the data assimilation. One from the most perspective approaches to a problem of the meteorological data assimilation is the application of the Kalman filter theory. Algorithm of a Kalman filter allows on sequence of data of observations for various instants and prognostic model, which is considered as dynamical-stochastic system to receive an optimum evaluation of a condition of atmosphere in a sense of a minimum variance of an error of an estimation. A serious problem at application of a Kalman filter algorithm to modern prognostic models is the high order of the forecast errors covariances matrixes used in this algorithm. One from the approaches to the solution of this problem is the application of the simplified models for calculation of the forecast errors covariances. Such algorithm is named as a suboptimal Kalman filter.

In the report the various simplified models are considered which can be applied in the suboptimal Kalman filter algorithm. The results of numerical experiments based on a Monte-Carlo method, on calculation of matrixes of the forecast errors covariances with the help of of simplified models are presented.

Note. Abstracts are published in author's edition

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