A comparison between gradient descent and stochastic approaches for parameter optimization of a sea ice model


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Hiroshi.Sumata [ at ] awi.de

Abstract

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.



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Scopus/ISI peer-reviewed
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Published
Eprint ID
33468
DOI 10.5194/os-9-609-2013

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Sumata, H. , Kauker, F. , Gerdes, R. , Koeberle, C. and Karcher, M. (2013): A comparison between gradient descent and stochastic approaches for parameter optimization of a sea ice model , Ocean Science, 9 (4), pp. 609-630 . doi: 10.5194/os-9-609-2013


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