Edit Item 

PDF
os96092013.pdf Download (14MB)  Preview 

Cite this document as:

Two types of optimization methods were applied to a parameter optimization problem in a coupled oceansea 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 microgenetic algorithm (\unit{\mu}GA) as an example of a stochastic approach. The opt\imizations were performed by minimizing a cost function composed of modeldata 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 illshaped nature of the cost function caused by the strong nonlinearity 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 oceansea ice model with a mediumsized horizontal resolution of 50\,km\,$\times$\,50\,km as used in this study.