Data accompanying the article "Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020" ...
The .zip file contains temporal-spatial averaged metrics for evaluating simulations against observed ice thickness, concentration, volume, and drift. These quantities are presented in the manuscript "Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian se...
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Zenodo
2023
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| Online Access: | https://dx.doi.org/10.5281/zenodo.7847752 https://zenodo.org/record/7847752 |
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ftdatacite:10.5281/zenodo.7847752 |
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openpolar |
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ftdatacite:10.5281/zenodo.7847752 2023-06-11T04:09:03+02:00 Data accompanying the article "Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020" ... Sukun Cheng 2023 https://dx.doi.org/10.5281/zenodo.7847752 https://zenodo.org/record/7847752 unknown Zenodo https://dx.doi.org/10.5281/zenodo.7847751 Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess Dataset dataset 2023 ftdatacite https://doi.org/10.5281/zenodo.784775210.5281/zenodo.7847751 2023-05-02T10:25:30Z The .zip file contains temporal-spatial averaged metrics for evaluating simulations against observed ice thickness, concentration, volume, and drift. These quantities are presented in the manuscript "Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020" Subfolders are named by the experiment IDs, including metrics obtained from the relevant experimental results and observations. In case information is missing, do not hesitate to contact chengsukun@hotmail.com We thank Pavel Sakov for helpful discussions and improvement regarding the EnKF-C code and Jiping Xie for contributing the TOPAZ interface to sea ice observations. We are grateful for the support from Timothy Williams and Anton Korosov regarding the environments of neXtSIM and its analysis tools. The work is funded by the DASIM-II grant from ONR (grant nos. N00014-18-1-2493 and N00014-18-1-2204). Alberto Carrassi, Christopher K. R. T. Jones, Ali Aydo ̆gdu, and Pierre Rampal ... Dataset Arctic Sea ice DataCite Metadata Store (German National Library of Science and Technology) Arctic |
| institution |
Open Polar |
| collection |
DataCite Metadata Store (German National Library of Science and Technology) |
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ftdatacite |
| language |
unknown |
| description |
The .zip file contains temporal-spatial averaged metrics for evaluating simulations against observed ice thickness, concentration, volume, and drift. These quantities are presented in the manuscript "Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020" Subfolders are named by the experiment IDs, including metrics obtained from the relevant experimental results and observations. In case information is missing, do not hesitate to contact chengsukun@hotmail.com We thank Pavel Sakov for helpful discussions and improvement regarding the EnKF-C code and Jiping Xie for contributing the TOPAZ interface to sea ice observations. We are grateful for the support from Timothy Williams and Anton Korosov regarding the environments of neXtSIM and its analysis tools. The work is funded by the DASIM-II grant from ONR (grant nos. N00014-18-1-2493 and N00014-18-1-2204). Alberto Carrassi, Christopher K. R. T. Jones, Ali Aydo ̆gdu, and Pierre Rampal ... |
| format |
Dataset |
| author |
Sukun Cheng |
| spellingShingle |
Sukun Cheng Data accompanying the article "Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020" ... |
| author_facet |
Sukun Cheng |
| author_sort |
Sukun Cheng |
| title |
Data accompanying the article "Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020" ... |
| title_short |
Data accompanying the article "Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020" ... |
| title_full |
Data accompanying the article "Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020" ... |
| title_fullStr |
Data accompanying the article "Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020" ... |
| title_full_unstemmed |
Data accompanying the article "Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020" ... |
| title_sort |
data accompanying the article "arctic sea ice data assimilation combining an ensemble kalman filter with a novel lagrangian sea ice model for the winter 2019–2020" ... |
| publisher |
Zenodo |
| publishDate |
2023 |
| url |
https://dx.doi.org/10.5281/zenodo.7847752 https://zenodo.org/record/7847752 |
| geographic |
Arctic |
| geographic_facet |
Arctic |
| genre |
Arctic Sea ice |
| genre_facet |
Arctic Sea ice |
| op_relation |
https://dx.doi.org/10.5281/zenodo.7847751 |
| op_rights |
Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess |
| op_doi |
https://doi.org/10.5281/zenodo.784775210.5281/zenodo.7847751 |
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1768382753608302592 |