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| Titel |
A comparison between gradient descent and stochastic approaches for parameter optimization of a sea ice model |
| VerfasserIn |
H. Sumata, F. Kauker, R. Gerdes, C. Köberle, M. Karcher |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1812-0784
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| Digitales Dokument |
URL |
| Erschienen |
In: Ocean Science ; 9, no. 4 ; Nr. 9, no. 4 (2013-07-09), S.609-630 |
| Datensatznummer |
250018107
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| Publikation (Nr.) |
 copernicus.org/os-9-609-2013.pdf |
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| Zusammenfassung |
| 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 (μGA) as an example of a stochastic approach. The optimizations 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
μGA 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 (μGA) is of practical use for
parameter optimization of a coupled ocean–sea ice model with a medium-sized
horizontal resolution of 50 km × 50 km as used in this study. |
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