A covariant feature of optimal parameters for Arctic sea ice model
An automatic parameter optimization system for a coupled ocean-sea ice model is applied to investigate uniqueness of parameter set obtained from data assimilation. We set up a parameter optimization experiment, in which 15 model parameters are optimized simultaneously using a 23-years optimization w...
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ftawi:oai:epic.awi.de:48635 2023-05-15T14:26:55+02:00 A covariant feature of optimal parameters for Arctic sea ice model Sumata, Hiroshi Kauker, Frank Karcher, Michael Gerdes, Rüdiger 2018-05-15 https://epic.awi.de/id/eprint/48635/ https://hdl.handle.net/10013/epic.4743e810-5ca5-49ee-8e23-525a62ca673b unknown Sumata, H. orcid:0000-0002-2832-2875 , Kauker, F. orcid:0000-0002-7976-3005 , Karcher, M. orcid:0000-0002-9587-811X and Gerdes, R. (2018) A covariant feature of optimal parameters for Arctic sea ice model , REKLIM workshop 2018, Boppard, Germanay, 15 May 2018 - 17 May 2018 . hdl:10013/epic.4743e810-5ca5-49ee-8e23-525a62ca673b EPIC3REKLIM workshop 2018, Boppard, Germanay, 2018-05-15-2018-05-17 Conference notRev 2018 ftawi 2021-12-24T15:44:22Z An automatic parameter optimization system for a coupled ocean-sea ice model is applied to investigate uniqueness of parameter set obtained from data assimilation. We set up a parameter optimization experiment, in which 15 model parameters are optimized simultaneously using a 23-years optimization window. A series of 11 independent experiments are conducted to examine spread of objective functions, optimized sea ice fields and associated optimal parameters. The result shows sufficiently small spreads of objective functions and ice fields, whereas a significantly large spread of optimized parameters. This indicates the system gives an unique solution regarding the simulated ice fields, whereas multiple solutions regarding the associated model parameters. A correlation analysis shows the optimal parameters are inter-related and covariant. A principal component analysis (PCA) reveals that the first 3 principal components explain 70% of the variation of the optimal parameter sets, indicating a contraction of the model parameter space. Conference Object Arctic Arctic Sea ice Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center) Arctic |
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Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center) |
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ftawi |
language |
unknown |
description |
An automatic parameter optimization system for a coupled ocean-sea ice model is applied to investigate uniqueness of parameter set obtained from data assimilation. We set up a parameter optimization experiment, in which 15 model parameters are optimized simultaneously using a 23-years optimization window. A series of 11 independent experiments are conducted to examine spread of objective functions, optimized sea ice fields and associated optimal parameters. The result shows sufficiently small spreads of objective functions and ice fields, whereas a significantly large spread of optimized parameters. This indicates the system gives an unique solution regarding the simulated ice fields, whereas multiple solutions regarding the associated model parameters. A correlation analysis shows the optimal parameters are inter-related and covariant. A principal component analysis (PCA) reveals that the first 3 principal components explain 70% of the variation of the optimal parameter sets, indicating a contraction of the model parameter space. |
format |
Conference Object |
author |
Sumata, Hiroshi Kauker, Frank Karcher, Michael Gerdes, Rüdiger |
spellingShingle |
Sumata, Hiroshi Kauker, Frank Karcher, Michael Gerdes, Rüdiger A covariant feature of optimal parameters for Arctic sea ice model |
author_facet |
Sumata, Hiroshi Kauker, Frank Karcher, Michael Gerdes, Rüdiger |
author_sort |
Sumata, Hiroshi |
title |
A covariant feature of optimal parameters for Arctic sea ice model |
title_short |
A covariant feature of optimal parameters for Arctic sea ice model |
title_full |
A covariant feature of optimal parameters for Arctic sea ice model |
title_fullStr |
A covariant feature of optimal parameters for Arctic sea ice model |
title_full_unstemmed |
A covariant feature of optimal parameters for Arctic sea ice model |
title_sort |
covariant feature of optimal parameters for arctic sea ice model |
publishDate |
2018 |
url |
https://epic.awi.de/id/eprint/48635/ https://hdl.handle.net/10013/epic.4743e810-5ca5-49ee-8e23-525a62ca673b |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Arctic Sea ice |
genre_facet |
Arctic Arctic Sea ice |
op_source |
EPIC3REKLIM workshop 2018, Boppard, Germanay, 2018-05-15-2018-05-17 |
op_relation |
Sumata, H. orcid:0000-0002-2832-2875 , Kauker, F. orcid:0000-0002-7976-3005 , Karcher, M. orcid:0000-0002-9587-811X and Gerdes, R. (2018) A covariant feature of optimal parameters for Arctic sea ice model , REKLIM workshop 2018, Boppard, Germanay, 15 May 2018 - 17 May 2018 . hdl:10013/epic.4743e810-5ca5-49ee-8e23-525a62ca673b |
_version_ |
1766300404555972608 |