Impacts on sea ice analyses from the assumption of uncorrelated ice thickness observation errors: Experiments using a 1D toy model
Sea ice prediction centres are moving toward the assimilation of ice thickness observations under the simplifying assumption that the observation errors are uncorrelated. The assumption of uncorrelated observation errors is attractive because the errors can be represented by a diagonal observation e...
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ftdoajarticles:oai:doaj.org/article:f4535ec66e164a17968a06d5251d6052 2023-05-15T18:17:25+02:00 Impacts on sea ice analyses from the assumption of uncorrelated ice thickness observation errors: Experiments using a 1D toy model Graham Stonebridge K. Andrea Scott Mark Buehner 2018-01-01T00:00:00Z https://doi.org/10.1080/16000870.2018.1445379 https://doaj.org/article/f4535ec66e164a17968a06d5251d6052 EN eng Stockholm University Press http://dx.doi.org/10.1080/16000870.2018.1445379 https://doaj.org/toc/1600-0870 1600-0870 doi:10.1080/16000870.2018.1445379 https://doaj.org/article/f4535ec66e164a17968a06d5251d6052 Tellus: Series A, Dynamic Meteorology and Oceanography, Vol 70, Iss 1, Pp 1-13 (2018) data assimilation sea ice ice thickness correlated errors Oceanography GC1-1581 Meteorology. Climatology QC851-999 article 2018 ftdoajarticles https://doi.org/10.1080/16000870.2018.1445379 2022-12-30T23:52:42Z Sea ice prediction centres are moving toward the assimilation of ice thickness observations under the simplifying assumption that the observation errors are uncorrelated. The assumption of uncorrelated observation errors is attractive because the errors can be represented by a diagonal observation error covariance matrix, which is inexpensive to invert. In this paper a set of idealized experiments are carried out to investigate the impact of this assumption on sea ice analyses. A background error covariance matrix is generated using a 1D toy model for sea, i.e. forced with idealized models of the ocean and atmosphere. Analysis error covariance matrices are then calculated using this $ \boldsymbol{ \mathrm B } $ matrix for both correlated and uncorrelated observation error covariance matrices, $ \boldsymbol{ \mathrm R } $. The results indicate when the true $ \boldsymbol{ \mathrm R } $ is correlated, using a diagonal approximation results in an analysis that is overconfident at the large scales, in that the analysis error standard deviation at the large scales is underestimated. It is also shown that for the largest observation error correlation length scale tested, 150 km, the analysis error standard deviation for ice thickness is reduced by 10.8% relative to the background error standard deviation when $ \boldsymbol{ \mathrm R } $ has the correct correlation length scale of 150 km, whereas when a diagonal approximation to $ \boldsymbol{ \mathrm R } $ is used in combination with an inflation factor, the reduction is to 6.3%. Article in Journal/Newspaper Sea ice Directory of Open Access Journals: DOAJ Articles Tellus A: Dynamic Meteorology and Oceanography 70 1 1 13 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
data assimilation sea ice ice thickness correlated errors Oceanography GC1-1581 Meteorology. Climatology QC851-999 |
spellingShingle |
data assimilation sea ice ice thickness correlated errors Oceanography GC1-1581 Meteorology. Climatology QC851-999 Graham Stonebridge K. Andrea Scott Mark Buehner Impacts on sea ice analyses from the assumption of uncorrelated ice thickness observation errors: Experiments using a 1D toy model |
topic_facet |
data assimilation sea ice ice thickness correlated errors Oceanography GC1-1581 Meteorology. Climatology QC851-999 |
description |
Sea ice prediction centres are moving toward the assimilation of ice thickness observations under the simplifying assumption that the observation errors are uncorrelated. The assumption of uncorrelated observation errors is attractive because the errors can be represented by a diagonal observation error covariance matrix, which is inexpensive to invert. In this paper a set of idealized experiments are carried out to investigate the impact of this assumption on sea ice analyses. A background error covariance matrix is generated using a 1D toy model for sea, i.e. forced with idealized models of the ocean and atmosphere. Analysis error covariance matrices are then calculated using this $ \boldsymbol{ \mathrm B } $ matrix for both correlated and uncorrelated observation error covariance matrices, $ \boldsymbol{ \mathrm R } $. The results indicate when the true $ \boldsymbol{ \mathrm R } $ is correlated, using a diagonal approximation results in an analysis that is overconfident at the large scales, in that the analysis error standard deviation at the large scales is underestimated. It is also shown that for the largest observation error correlation length scale tested, 150 km, the analysis error standard deviation for ice thickness is reduced by 10.8% relative to the background error standard deviation when $ \boldsymbol{ \mathrm R } $ has the correct correlation length scale of 150 km, whereas when a diagonal approximation to $ \boldsymbol{ \mathrm R } $ is used in combination with an inflation factor, the reduction is to 6.3%. |
format |
Article in Journal/Newspaper |
author |
Graham Stonebridge K. Andrea Scott Mark Buehner |
author_facet |
Graham Stonebridge K. Andrea Scott Mark Buehner |
author_sort |
Graham Stonebridge |
title |
Impacts on sea ice analyses from the assumption of uncorrelated ice thickness observation errors: Experiments using a 1D toy model |
title_short |
Impacts on sea ice analyses from the assumption of uncorrelated ice thickness observation errors: Experiments using a 1D toy model |
title_full |
Impacts on sea ice analyses from the assumption of uncorrelated ice thickness observation errors: Experiments using a 1D toy model |
title_fullStr |
Impacts on sea ice analyses from the assumption of uncorrelated ice thickness observation errors: Experiments using a 1D toy model |
title_full_unstemmed |
Impacts on sea ice analyses from the assumption of uncorrelated ice thickness observation errors: Experiments using a 1D toy model |
title_sort |
impacts on sea ice analyses from the assumption of uncorrelated ice thickness observation errors: experiments using a 1d toy model |
publisher |
Stockholm University Press |
publishDate |
2018 |
url |
https://doi.org/10.1080/16000870.2018.1445379 https://doaj.org/article/f4535ec66e164a17968a06d5251d6052 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
Tellus: Series A, Dynamic Meteorology and Oceanography, Vol 70, Iss 1, Pp 1-13 (2018) |
op_relation |
http://dx.doi.org/10.1080/16000870.2018.1445379 https://doaj.org/toc/1600-0870 1600-0870 doi:10.1080/16000870.2018.1445379 https://doaj.org/article/f4535ec66e164a17968a06d5251d6052 |
op_doi |
https://doi.org/10.1080/16000870.2018.1445379 |
container_title |
Tellus A: Dynamic Meteorology and Oceanography |
container_volume |
70 |
container_issue |
1 |
container_start_page |
1 |
op_container_end_page |
13 |
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1766191616721158144 |