Data fusion and data assimilation of ice thickness observations using a regularisation framework
Accurate estimates of sharp features in the sea ice cover, such as leads and ridges, are critical for shipping activities, ice operations and weather forecasting. These sharp features can be difficult to preserve in data fusion and data assimilation due to the spatial correlations in the background...
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ftdoajarticles:oai:doaj.org/article:bbb79a51d78b4ac790aa1a0d03da356e 2023-05-15T18:17:48+02:00 Data fusion and data assimilation of ice thickness observations using a regularisation framework Nazanin Asadi K. Andrea Scott David A. Clausi 2019-01-01T00:00:00Z https://doi.org/10.1080/16000870.2018.1564487 https://doaj.org/article/bbb79a51d78b4ac790aa1a0d03da356e EN eng Stockholm University Press http://dx.doi.org/10.1080/16000870.2018.1564487 https://doaj.org/toc/1600-0870 1600-0870 doi:10.1080/16000870.2018.1564487 https://doaj.org/article/bbb79a51d78b4ac790aa1a0d03da356e Tellus: Series A, Dynamic Meteorology and Oceanography, Vol 71, Iss 1 (2019) sea ice data assimilation ice thickness regularisation 3d-var sparsity Oceanography GC1-1581 Meteorology. Climatology QC851-999 article 2019 ftdoajarticles https://doi.org/10.1080/16000870.2018.1564487 2022-12-30T22:23:44Z Accurate estimates of sharp features in the sea ice cover, such as leads and ridges, are critical for shipping activities, ice operations and weather forecasting. These sharp features can be difficult to preserve in data fusion and data assimilation due to the spatial correlations in the background error covariance matrices. In this article, a set of data fusion and data assimilation experiments are carried out comparing two objective functions, one with a conventional l2-norm and one that imposes an additional l1-norm on the derivative of the ice thickness state estimate. The latter is motivated by analysis of high resolution ice thickness observations derived from an airborne electromagnetic sensor demonstrating the sparsity of the ice thickness in the derivative domain. Data fusion and data assimilation experiments (using a 1 D toy sea-ice model) are carried out over a wide range of background and observation error correlation length scales. Results show the superiority of using an l1–l2 regularisation framework. For the data fusion experiments it was found when both background and observation error correlation length scales are zero, the ice thickness root mean squared error for the l1–l2 method was 0.16 m as compared to 0.20 m for the l2 method. The differences between the methods were greater when the background error correlation length scale was relatively short (approximately five times the analysis grid spacing), and were not significant for larger background error correlation length scales (e.g. 10 times the analysis grid spacing). For data assimilation experiments it was found that openings in the ice cover were captured better with the l1–l2 regularisation, with reduced errors in ice thickness, concentration and velocity. In addition, the ice thickness derivatives in the analyses were found to be more sparse when the l1–l2 method was used and are closer to the those from the true model run. Article in Journal/Newspaper Sea ice Directory of Open Access Journals: DOAJ Articles Tellus A: Dynamic Meteorology and Oceanography 71 1 1564487 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
sea ice data assimilation ice thickness regularisation 3d-var sparsity Oceanography GC1-1581 Meteorology. Climatology QC851-999 |
spellingShingle |
sea ice data assimilation ice thickness regularisation 3d-var sparsity Oceanography GC1-1581 Meteorology. Climatology QC851-999 Nazanin Asadi K. Andrea Scott David A. Clausi Data fusion and data assimilation of ice thickness observations using a regularisation framework |
topic_facet |
sea ice data assimilation ice thickness regularisation 3d-var sparsity Oceanography GC1-1581 Meteorology. Climatology QC851-999 |
description |
Accurate estimates of sharp features in the sea ice cover, such as leads and ridges, are critical for shipping activities, ice operations and weather forecasting. These sharp features can be difficult to preserve in data fusion and data assimilation due to the spatial correlations in the background error covariance matrices. In this article, a set of data fusion and data assimilation experiments are carried out comparing two objective functions, one with a conventional l2-norm and one that imposes an additional l1-norm on the derivative of the ice thickness state estimate. The latter is motivated by analysis of high resolution ice thickness observations derived from an airborne electromagnetic sensor demonstrating the sparsity of the ice thickness in the derivative domain. Data fusion and data assimilation experiments (using a 1 D toy sea-ice model) are carried out over a wide range of background and observation error correlation length scales. Results show the superiority of using an l1–l2 regularisation framework. For the data fusion experiments it was found when both background and observation error correlation length scales are zero, the ice thickness root mean squared error for the l1–l2 method was 0.16 m as compared to 0.20 m for the l2 method. The differences between the methods were greater when the background error correlation length scale was relatively short (approximately five times the analysis grid spacing), and were not significant for larger background error correlation length scales (e.g. 10 times the analysis grid spacing). For data assimilation experiments it was found that openings in the ice cover were captured better with the l1–l2 regularisation, with reduced errors in ice thickness, concentration and velocity. In addition, the ice thickness derivatives in the analyses were found to be more sparse when the l1–l2 method was used and are closer to the those from the true model run. |
format |
Article in Journal/Newspaper |
author |
Nazanin Asadi K. Andrea Scott David A. Clausi |
author_facet |
Nazanin Asadi K. Andrea Scott David A. Clausi |
author_sort |
Nazanin Asadi |
title |
Data fusion and data assimilation of ice thickness observations using a regularisation framework |
title_short |
Data fusion and data assimilation of ice thickness observations using a regularisation framework |
title_full |
Data fusion and data assimilation of ice thickness observations using a regularisation framework |
title_fullStr |
Data fusion and data assimilation of ice thickness observations using a regularisation framework |
title_full_unstemmed |
Data fusion and data assimilation of ice thickness observations using a regularisation framework |
title_sort |
data fusion and data assimilation of ice thickness observations using a regularisation framework |
publisher |
Stockholm University Press |
publishDate |
2019 |
url |
https://doi.org/10.1080/16000870.2018.1564487 https://doaj.org/article/bbb79a51d78b4ac790aa1a0d03da356e |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
Tellus: Series A, Dynamic Meteorology and Oceanography, Vol 71, Iss 1 (2019) |
op_relation |
http://dx.doi.org/10.1080/16000870.2018.1564487 https://doaj.org/toc/1600-0870 1600-0870 doi:10.1080/16000870.2018.1564487 https://doaj.org/article/bbb79a51d78b4ac790aa1a0d03da356e |
op_doi |
https://doi.org/10.1080/16000870.2018.1564487 |
container_title |
Tellus A: Dynamic Meteorology and Oceanography |
container_volume |
71 |
container_issue |
1 |
container_start_page |
1564487 |
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1766193106019942400 |