Computer Model Calibration Based on Image Warping Metrics: An Application for Sea Ice Deformation
Arctic sea ice plays an important role in the global climate. Sea ice models governed by physical equations have been used to simulate the state of the ice including characteristics such as ice thickness, concentration, and motion. More recent models also attempt to capture features such as fracture...
Published in: | Journal of Agricultural, Biological and Environmental Statistics |
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ftosti:oai:osti.gov:1570252 2023-07-30T04:01:45+02:00 Computer Model Calibration Based on Image Warping Metrics: An Application for Sea Ice Deformation Guan, Yawen Sampson, Christian Tucker, J. Derek Chang, Won Mondal, Anirban Haran, Murali Sulsky, Deborah 2022-05-27 application/pdf http://www.osti.gov/servlets/purl/1570252 https://www.osti.gov/biblio/1570252 https://doi.org/10.1007/s13253-019-00353-7 unknown http://www.osti.gov/servlets/purl/1570252 https://www.osti.gov/biblio/1570252 https://doi.org/10.1007/s13253-019-00353-7 doi:10.1007/s13253-019-00353-7 54 ENVIRONMENTAL SCIENCES 2022 ftosti https://doi.org/10.1007/s13253-019-00353-7 2023-07-11T09:37:36Z Arctic sea ice plays an important role in the global climate. Sea ice models governed by physical equations have been used to simulate the state of the ice including characteristics such as ice thickness, concentration, and motion. More recent models also attempt to capture features such as fractures or leads in the ice. These simulated features can be partially misaligned or misshapen when compared to observational data, whether due to numerical approximation or incomplete physics. In order to make realistic forecasts and improve understanding of the underlying processes, it is necessary to calibrate the numerical model to field data. Traditional calibration methods based on generalized least-square metrics are flawed for linear features such as sea ice cracks. We develop a statistical emulation and calibration framework that accounts for feature misalignment and misshapenness, which involves optimally aligning model output with observed features using cutting-edge image registration techniques. Here, this work can also have application to other physical models which produce coherent structures. Other/Unknown Material Arctic Sea ice SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) Arctic Journal of Agricultural, Biological and Environmental Statistics 24 3 444 463 |
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Open Polar |
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SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) |
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ftosti |
language |
unknown |
topic |
54 ENVIRONMENTAL SCIENCES |
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54 ENVIRONMENTAL SCIENCES Guan, Yawen Sampson, Christian Tucker, J. Derek Chang, Won Mondal, Anirban Haran, Murali Sulsky, Deborah Computer Model Calibration Based on Image Warping Metrics: An Application for Sea Ice Deformation |
topic_facet |
54 ENVIRONMENTAL SCIENCES |
description |
Arctic sea ice plays an important role in the global climate. Sea ice models governed by physical equations have been used to simulate the state of the ice including characteristics such as ice thickness, concentration, and motion. More recent models also attempt to capture features such as fractures or leads in the ice. These simulated features can be partially misaligned or misshapen when compared to observational data, whether due to numerical approximation or incomplete physics. In order to make realistic forecasts and improve understanding of the underlying processes, it is necessary to calibrate the numerical model to field data. Traditional calibration methods based on generalized least-square metrics are flawed for linear features such as sea ice cracks. We develop a statistical emulation and calibration framework that accounts for feature misalignment and misshapenness, which involves optimally aligning model output with observed features using cutting-edge image registration techniques. Here, this work can also have application to other physical models which produce coherent structures. |
author |
Guan, Yawen Sampson, Christian Tucker, J. Derek Chang, Won Mondal, Anirban Haran, Murali Sulsky, Deborah |
author_facet |
Guan, Yawen Sampson, Christian Tucker, J. Derek Chang, Won Mondal, Anirban Haran, Murali Sulsky, Deborah |
author_sort |
Guan, Yawen |
title |
Computer Model Calibration Based on Image Warping Metrics: An Application for Sea Ice Deformation |
title_short |
Computer Model Calibration Based on Image Warping Metrics: An Application for Sea Ice Deformation |
title_full |
Computer Model Calibration Based on Image Warping Metrics: An Application for Sea Ice Deformation |
title_fullStr |
Computer Model Calibration Based on Image Warping Metrics: An Application for Sea Ice Deformation |
title_full_unstemmed |
Computer Model Calibration Based on Image Warping Metrics: An Application for Sea Ice Deformation |
title_sort |
computer model calibration based on image warping metrics: an application for sea ice deformation |
publishDate |
2022 |
url |
http://www.osti.gov/servlets/purl/1570252 https://www.osti.gov/biblio/1570252 https://doi.org/10.1007/s13253-019-00353-7 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_relation |
http://www.osti.gov/servlets/purl/1570252 https://www.osti.gov/biblio/1570252 https://doi.org/10.1007/s13253-019-00353-7 doi:10.1007/s13253-019-00353-7 |
op_doi |
https://doi.org/10.1007/s13253-019-00353-7 |
container_title |
Journal of Agricultural, Biological and Environmental Statistics |
container_volume |
24 |
container_issue |
3 |
container_start_page |
444 |
op_container_end_page |
463 |
_version_ |
1772812519328448512 |