Sensitivity analysis and parameter tuning of a sea-ice model.
The values of many of the parameters in climate models are often not known with any great precision. We describe the use of automatic differentiation to examine the sensitivity of an uncoupled dynamic-thermodynamic sea-ice model to various parameters. We also illustrate the effectiveness of using th...
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Argonne National Laboratory
2000
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ftunivnotexas:info:ark/67531/metadc725467 2023-05-15T18:17:14+02:00 Sensitivity analysis and parameter tuning of a sea-ice model. Kim, J. G. Hovland, P. D. United States. Department of Energy. 2000-11-01 vp. Text http://digital.library.unt.edu/ark:/67531/metadc725467/ English eng Argonne National Laboratory rep-no: ANL/MCS/CP-103189 grantno: W-31109-ENG-38 osti: 768597 http://digital.library.unt.edu/ark:/67531/metadc725467/ ark: ark:/67531/metadc725467 3rd International Conference/Workshop on Automatic Differentiation: From Simulation to Optimization, Nice (FR), 06/19/2000--06/23/2000; Other Information: PBD: 1 Nov 2000; PBD: 1 Nov 2000 Tuning Sensitivity Analysis Accuracy Sensitivity Simulation Optimization 54 Environmental Sciences Climate Models Algorithms Article 2000 ftunivnotexas 2016-03-26T23:11:29Z The values of many of the parameters in climate models are often not known with any great precision. We describe the use of automatic differentiation to examine the sensitivity of an uncoupled dynamic-thermodynamic sea-ice model to various parameters. We also illustrate the effectiveness of using these sensitivity derivatives with an optimization algorithm to tune the parameters to maximize the agreement between simulated results and observational data. Article in Journal/Newspaper Sea ice University of North Texas: UNT Digital Library |
institution |
Open Polar |
collection |
University of North Texas: UNT Digital Library |
op_collection_id |
ftunivnotexas |
language |
English |
topic |
Tuning Sensitivity Analysis Accuracy Sensitivity Simulation Optimization 54 Environmental Sciences Climate Models Algorithms |
spellingShingle |
Tuning Sensitivity Analysis Accuracy Sensitivity Simulation Optimization 54 Environmental Sciences Climate Models Algorithms Kim, J. G. Hovland, P. D. Sensitivity analysis and parameter tuning of a sea-ice model. |
topic_facet |
Tuning Sensitivity Analysis Accuracy Sensitivity Simulation Optimization 54 Environmental Sciences Climate Models Algorithms |
description |
The values of many of the parameters in climate models are often not known with any great precision. We describe the use of automatic differentiation to examine the sensitivity of an uncoupled dynamic-thermodynamic sea-ice model to various parameters. We also illustrate the effectiveness of using these sensitivity derivatives with an optimization algorithm to tune the parameters to maximize the agreement between simulated results and observational data. |
author2 |
United States. Department of Energy. |
format |
Article in Journal/Newspaper |
author |
Kim, J. G. Hovland, P. D. |
author_facet |
Kim, J. G. Hovland, P. D. |
author_sort |
Kim, J. G. |
title |
Sensitivity analysis and parameter tuning of a sea-ice model. |
title_short |
Sensitivity analysis and parameter tuning of a sea-ice model. |
title_full |
Sensitivity analysis and parameter tuning of a sea-ice model. |
title_fullStr |
Sensitivity analysis and parameter tuning of a sea-ice model. |
title_full_unstemmed |
Sensitivity analysis and parameter tuning of a sea-ice model. |
title_sort |
sensitivity analysis and parameter tuning of a sea-ice model. |
publisher |
Argonne National Laboratory |
publishDate |
2000 |
url |
http://digital.library.unt.edu/ark:/67531/metadc725467/ |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
3rd International Conference/Workshop on Automatic Differentiation: From Simulation to Optimization, Nice (FR), 06/19/2000--06/23/2000; Other Information: PBD: 1 Nov 2000; PBD: 1 Nov 2000 |
op_relation |
rep-no: ANL/MCS/CP-103189 grantno: W-31109-ENG-38 osti: 768597 http://digital.library.unt.edu/ark:/67531/metadc725467/ ark: ark:/67531/metadc725467 |
_version_ |
1766191328518995968 |