Dynamic-Stochastic Modeling of Natural Processes

Dynamic-Stochastic Modeling of Natural Processes We propose a method for the construction of dynamic-stochastic models of natural systems based on the assimilation of the data of observations in the prognostic equations of coupled processes. In these models, the method of adaptive balance of causes is used to deduce evolutionary equations of the analyzed processes and assimilate the data of observations in these equations. The deduced general equations are considered for an example of a marine ecosystem characterized by the development of four coupled processes. It is shown that the optimal prediction of these processes requires the solution of 11 systems of equations with simultaneous adaptation of prognostic estimates and the coefficients of the models to the data of observations. A numerical simulation experiment explaining the algorithm of the proposed method of modeling is considered. A conclusion is made that the application of this method in the geoinformation systems of monitoring of the environment is quite promising. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Physical Oceanography Springer Journals

Dynamic-Stochastic Modeling of Natural Processes

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Publisher
Kluwer Academic Publishers-Consultants Bureau
Copyright
Copyright © 2004 by Springer Science+Business Media, Inc.
Subject
Earth Sciences; Oceanography; Remote Sensing/Photogrammetry; Atmospheric Sciences; Climate Change; Environmental Physics
ISSN
0928-5105
eISSN
0928-5105
D.O.I.
10.1007/s11110-005-0022-5
Publisher site
See Article on Publisher Site

Abstract

We propose a method for the construction of dynamic-stochastic models of natural systems based on the assimilation of the data of observations in the prognostic equations of coupled processes. In these models, the method of adaptive balance of causes is used to deduce evolutionary equations of the analyzed processes and assimilate the data of observations in these equations. The deduced general equations are considered for an example of a marine ecosystem characterized by the development of four coupled processes. It is shown that the optimal prediction of these processes requires the solution of 11 systems of equations with simultaneous adaptation of prognostic estimates and the coefficients of the models to the data of observations. A numerical simulation experiment explaining the algorithm of the proposed method of modeling is considered. A conclusion is made that the application of this method in the geoinformation systems of monitoring of the environment is quite promising.

Journal

Physical OceanographySpringer Journals

Published: May 19, 2005

References

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