 |
| Titel |
Application of genetic algorithms to a parameter optimization of a coupled ocea - sea ice model in the Arctic Ocean |
| VerfasserIn |
Hiroshi Sumata, Frank Kauker, Ruediger Gerdes, Cornelia Koeberle, Michael Karcher |
| Konferenz |
EGU General Assembly 2013
|
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 15 (2013) |
| Datensatznummer |
250077962
|
|
|
|
|
|
| Zusammenfassung |
| We applied genetic algorithms to a parameter optimization problem in a coupled ocean – sea ice model, and examined applicability and efficiency of this approach from the point of view of a practical use for sea ice – ocean simulation in the Arctic Ocean. Several series of parameter optimization experiments were performed by minimizing a cost function composed of model – data misfit of 3 types of sea ice properties. The result shows that the genetic algorithms can effectively estimate near optimal parameter set with a practical number of iterations, and the methods provided better results compared to a traditional gradient descent approach. The result of the study indicates that a sophisticated stochastic approach is of practical use to a parameter optimization of a coupled ocean–sea ice model. |
|
|
|
|
|
|