Application of 3-D ensemble variational data assimilation to a Baltic Sea reanalysis 1989–2013
A 3-D ensemble variational (3DEnVar) data assimilation method has been implemented and tested for oceanographic data assimilation of sea surface temperature (SST), sea surface salinity (SSS), sea ice concentration (SIC), and salinity and temperature profiles. To damp spurious long-range correlations...
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ftdoajarticles:oai:doaj.org/article:fcf3c34410134d9db857851f4e99dae2 2023-05-15T18:18:28+02:00 Application of 3-D ensemble variational data assimilation to a Baltic Sea reanalysis 1989–2013 Lars Axell Ye Liu 2016-03-01T00:00:00Z https://doi.org/10.3402/tellusa.v68.24220 https://doaj.org/article/fcf3c34410134d9db857851f4e99dae2 EN eng Stockholm University Press http://www.tellusa.net/index.php/tellusa/article/view/24220/45528 https://doaj.org/toc/1600-0870 1600-0870 doi:10.3402/tellusa.v68.24220 https://doaj.org/article/fcf3c34410134d9db857851f4e99dae2 Tellus: Series A, Dynamic Meteorology and Oceanography, Vol 68, Iss 0, Pp 1-20 (2016) data assimilation physical oceanography Baltic Sea reanalysis Oceanography GC1-1581 Meteorology. Climatology QC851-999 article 2016 ftdoajarticles https://doi.org/10.3402/tellusa.v68.24220 2022-12-30T21:45:22Z A 3-D ensemble variational (3DEnVar) data assimilation method has been implemented and tested for oceanographic data assimilation of sea surface temperature (SST), sea surface salinity (SSS), sea ice concentration (SIC), and salinity and temperature profiles. To damp spurious long-range correlations in the ensemble statistics, horizontal and vertical localisation was implemented using empirical orthogonal functions. The results show that the 3DEnVar method is indeed possible to use in oceanographic data assimilation. So far, only a seasonally dependent ensemble has been used, based on historical model simulations. Near-surface experiments showed that the ensemble statistics gave inhomogeneous and anisotropic horizontal structure functions, and assimilation of real SST and SIC fields gave smooth, realistic increment fields. The implementation was multivariate, and results showed that the cross-correlations between variables work in an intuitive way, for example, decreasing SST where SIC was increased and vice versa. The profile data assimilation also gave good results. The results from a 25-year reanalysis showed that the vertical salinity and temperature structure were significantly improved, compared to both dependent and independent data. Article in Journal/Newspaper Sea ice Directory of Open Access Journals: DOAJ Articles Tellus A: Dynamic Meteorology and Oceanography 68 1 24220 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
data assimilation physical oceanography Baltic Sea reanalysis Oceanography GC1-1581 Meteorology. Climatology QC851-999 |
spellingShingle |
data assimilation physical oceanography Baltic Sea reanalysis Oceanography GC1-1581 Meteorology. Climatology QC851-999 Lars Axell Ye Liu Application of 3-D ensemble variational data assimilation to a Baltic Sea reanalysis 1989–2013 |
topic_facet |
data assimilation physical oceanography Baltic Sea reanalysis Oceanography GC1-1581 Meteorology. Climatology QC851-999 |
description |
A 3-D ensemble variational (3DEnVar) data assimilation method has been implemented and tested for oceanographic data assimilation of sea surface temperature (SST), sea surface salinity (SSS), sea ice concentration (SIC), and salinity and temperature profiles. To damp spurious long-range correlations in the ensemble statistics, horizontal and vertical localisation was implemented using empirical orthogonal functions. The results show that the 3DEnVar method is indeed possible to use in oceanographic data assimilation. So far, only a seasonally dependent ensemble has been used, based on historical model simulations. Near-surface experiments showed that the ensemble statistics gave inhomogeneous and anisotropic horizontal structure functions, and assimilation of real SST and SIC fields gave smooth, realistic increment fields. The implementation was multivariate, and results showed that the cross-correlations between variables work in an intuitive way, for example, decreasing SST where SIC was increased and vice versa. The profile data assimilation also gave good results. The results from a 25-year reanalysis showed that the vertical salinity and temperature structure were significantly improved, compared to both dependent and independent data. |
format |
Article in Journal/Newspaper |
author |
Lars Axell Ye Liu |
author_facet |
Lars Axell Ye Liu |
author_sort |
Lars Axell |
title |
Application of 3-D ensemble variational data assimilation to a Baltic Sea reanalysis 1989–2013 |
title_short |
Application of 3-D ensemble variational data assimilation to a Baltic Sea reanalysis 1989–2013 |
title_full |
Application of 3-D ensemble variational data assimilation to a Baltic Sea reanalysis 1989–2013 |
title_fullStr |
Application of 3-D ensemble variational data assimilation to a Baltic Sea reanalysis 1989–2013 |
title_full_unstemmed |
Application of 3-D ensemble variational data assimilation to a Baltic Sea reanalysis 1989–2013 |
title_sort |
application of 3-d ensemble variational data assimilation to a baltic sea reanalysis 1989–2013 |
publisher |
Stockholm University Press |
publishDate |
2016 |
url |
https://doi.org/10.3402/tellusa.v68.24220 https://doaj.org/article/fcf3c34410134d9db857851f4e99dae2 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
Tellus: Series A, Dynamic Meteorology and Oceanography, Vol 68, Iss 0, Pp 1-20 (2016) |
op_relation |
http://www.tellusa.net/index.php/tellusa/article/view/24220/45528 https://doaj.org/toc/1600-0870 1600-0870 doi:10.3402/tellusa.v68.24220 https://doaj.org/article/fcf3c34410134d9db857851f4e99dae2 |
op_doi |
https://doi.org/10.3402/tellusa.v68.24220 |
container_title |
Tellus A: Dynamic Meteorology and Oceanography |
container_volume |
68 |
container_issue |
1 |
container_start_page |
24220 |
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1766195044992155648 |