Variational Data Assimilation for a Lorenz Model Usinga Non-Standard Genetic Algorithm |
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Authors: | B Ahrens |
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Institution: | (1) Institute for Meteorology and Geophysics, University of Vienna, Austria, AT |
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Abstract: | Summary Data assimilation in meteorology and oceanography for strongly nonlinear dynamical systems is challenging. The dynamical
system studied here is the classical three-variable Lorenz model. In this context data assimilation with weak-constraint variational
methods performs better than other methods like strong-constraint variational methods or Kalman filters. The difficulty in
tracking the chaotic Lorenz orbit by assimilation of noisy observations results from the inherent instability in the system.
In variational methods a cost function has to be minimized. It is known, that in the Lorenz case the structure of the cost
function becomes more and more complex with increasing length of the assimilation time interval and with reduction of the
observational data quality. This paper proposes a non-standard implementation of a genetic algorithm for searching the global
minimum in case of a weak-constraint formulation. The good performance of this non-local search is shown, but the algorithm
is computationally demanding due to a very large number of control parameters within the weak-constraint formulation and,
thus, the algorithm is applicable for simple systems only.
Received December 12, 1998 Revised May 11, 1999 |
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