Publication Date:
2019-07-16
Description:
Two types of optimization methods were applied to a parameter optimization
problem in a coupled ocean--sea ice model of the Arctic, and applicability
and efficiency of the respective methods were examined. One optimization
utilizes a finite difference (FD) method based on a traditional gradient
descent approach, while the other adopts a micro-genetic algorithm (\unit{\mu}GA) as an example of a stochastic approach. The opt\imizations were performed
by minimizing a cost function composed of model--data misfit of ice
concentration, ice drift velocity and ice thickness. A series of
optimizations were conducted that differ in the model formulation
(``smoothed code'' versus standard code) with respect to the FD method and
in the population size and number of possibilities with respect to the
\unit{\mu}GA method. The FD method fails to estimate optimal parameters due to
the ill-shaped nature of the cost function caused by the strong
non-linearity of the system, whereas the genetic algorithms can effectively
estimate near optimal parameters. The results of the study indicate that the
sophisticated stochastic approach (\unit{\mu}GA) is of practical use for
parameter optimization of a coupled ocean--sea ice model with a medium-sized
horizontal resolution of 50\,km\,$\times$\,50\,km as used in this study.
Repository Name:
EPIC Alfred Wegener Institut
Type:
Article
,
isiRev
Format:
application/pdf
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