Assimilating high-resolution sea surface temperature data improves the ocean forecast potential in the Baltic Sea
We assess the impact of assimilating the satellite sea surface temperature (SST) data on the Baltic forecast, particularly on the forecast of ocean variables related to SST. For this purpose, a multivariable data assimilation (DA) system has been developed based on a Nordic version of the Nucleus fo...
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ftdoajarticles:oai:doaj.org/article:b7bbe10dc9d440df96b8e09332c8bf69 2023-05-15T18:17:37+02:00 Assimilating high-resolution sea surface temperature data improves the ocean forecast potential in the Baltic Sea Y. Liu W. Fu 2018-06-01T00:00:00Z https://doi.org/10.5194/os-14-525-2018 https://doaj.org/article/b7bbe10dc9d440df96b8e09332c8bf69 EN eng Copernicus Publications https://www.ocean-sci.net/14/525/2018/os-14-525-2018.pdf https://doaj.org/toc/1812-0784 https://doaj.org/toc/1812-0792 doi:10.5194/os-14-525-2018 1812-0784 1812-0792 https://doaj.org/article/b7bbe10dc9d440df96b8e09332c8bf69 Ocean Science, Vol 14, Pp 525-541 (2018) Geography. Anthropology. Recreation G Environmental sciences GE1-350 article 2018 ftdoajarticles https://doi.org/10.5194/os-14-525-2018 2022-12-31T01:55:31Z We assess the impact of assimilating the satellite sea surface temperature (SST) data on the Baltic forecast, particularly on the forecast of ocean variables related to SST. For this purpose, a multivariable data assimilation (DA) system has been developed based on a Nordic version of the Nucleus for European Modelling of the Ocean (NEMO-Nordic). We use Kalman-type filtering to assimilate the observations in the coastal regions. Further, a low-rank approximation of the stationary background error covariance metrics is used at the analysis steps. High-resolution SST from the Ocean and Sea Ice Satellite Application Facility (OSISAF) is assimilated to verify the performance of the DA system. The assimilation run shows very stable improvements of the model simulation as compared with both independent and dependent observations. The SST prediction of NEMO-Nordic is significantly enhanced by the DA forecast. Temperatures are also closer to observations in the DA forecast than the model results in the water above 100 m in the Baltic Sea. In the deeper layers, salinity is also slightly improved. In addition, we find that sea level anomaly (SLA) is improved with the SST assimilation. Comparisons with independent tide gauge data show that the overall root mean square error (RMSE) is reduced by 1.8 % and the overall correlation coefficient is slightly increased. Moreover, the sea-ice concentration forecast is improved considerably in the Baltic Proper, the Gulf of Finland and the Bothnian Sea during the sea-ice formation period, respectively. Article in Journal/Newspaper Sea ice Directory of Open Access Journals: DOAJ Articles Ocean Science 14 3 525 541 |
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Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Geography. Anthropology. Recreation G Environmental sciences GE1-350 |
spellingShingle |
Geography. Anthropology. Recreation G Environmental sciences GE1-350 Y. Liu W. Fu Assimilating high-resolution sea surface temperature data improves the ocean forecast potential in the Baltic Sea |
topic_facet |
Geography. Anthropology. Recreation G Environmental sciences GE1-350 |
description |
We assess the impact of assimilating the satellite sea surface temperature (SST) data on the Baltic forecast, particularly on the forecast of ocean variables related to SST. For this purpose, a multivariable data assimilation (DA) system has been developed based on a Nordic version of the Nucleus for European Modelling of the Ocean (NEMO-Nordic). We use Kalman-type filtering to assimilate the observations in the coastal regions. Further, a low-rank approximation of the stationary background error covariance metrics is used at the analysis steps. High-resolution SST from the Ocean and Sea Ice Satellite Application Facility (OSISAF) is assimilated to verify the performance of the DA system. The assimilation run shows very stable improvements of the model simulation as compared with both independent and dependent observations. The SST prediction of NEMO-Nordic is significantly enhanced by the DA forecast. Temperatures are also closer to observations in the DA forecast than the model results in the water above 100 m in the Baltic Sea. In the deeper layers, salinity is also slightly improved. In addition, we find that sea level anomaly (SLA) is improved with the SST assimilation. Comparisons with independent tide gauge data show that the overall root mean square error (RMSE) is reduced by 1.8 % and the overall correlation coefficient is slightly increased. Moreover, the sea-ice concentration forecast is improved considerably in the Baltic Proper, the Gulf of Finland and the Bothnian Sea during the sea-ice formation period, respectively. |
format |
Article in Journal/Newspaper |
author |
Y. Liu W. Fu |
author_facet |
Y. Liu W. Fu |
author_sort |
Y. Liu |
title |
Assimilating high-resolution sea surface temperature data improves the ocean forecast potential in the Baltic Sea |
title_short |
Assimilating high-resolution sea surface temperature data improves the ocean forecast potential in the Baltic Sea |
title_full |
Assimilating high-resolution sea surface temperature data improves the ocean forecast potential in the Baltic Sea |
title_fullStr |
Assimilating high-resolution sea surface temperature data improves the ocean forecast potential in the Baltic Sea |
title_full_unstemmed |
Assimilating high-resolution sea surface temperature data improves the ocean forecast potential in the Baltic Sea |
title_sort |
assimilating high-resolution sea surface temperature data improves the ocean forecast potential in the baltic sea |
publisher |
Copernicus Publications |
publishDate |
2018 |
url |
https://doi.org/10.5194/os-14-525-2018 https://doaj.org/article/b7bbe10dc9d440df96b8e09332c8bf69 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
Ocean Science, Vol 14, Pp 525-541 (2018) |
op_relation |
https://www.ocean-sci.net/14/525/2018/os-14-525-2018.pdf https://doaj.org/toc/1812-0784 https://doaj.org/toc/1812-0792 doi:10.5194/os-14-525-2018 1812-0784 1812-0792 https://doaj.org/article/b7bbe10dc9d440df96b8e09332c8bf69 |
op_doi |
https://doi.org/10.5194/os-14-525-2018 |
container_title |
Ocean Science |
container_volume |
14 |
container_issue |
3 |
container_start_page |
525 |
op_container_end_page |
541 |
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1766192203558813696 |