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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2018
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Online Access: | https://doi.org/10.5194/os-14-525-2018 https://www.ocean-sci.net/14/525/2018/os-14-525-2018.pdf https://doaj.org/article/b7bbe10dc9d440df96b8e09332c8bf69 |
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fttriple:oai:gotriple.eu:oai:doaj.org/article:b7bbe10dc9d440df96b8e09332c8bf69 2023-05-15T18:17:35+02:00 Assimilating high-resolution sea surface temperature data improves the ocean forecast potential in the Baltic Sea Y. Liu W. Fu 2018-06-01 https://doi.org/10.5194/os-14-525-2018 https://www.ocean-sci.net/14/525/2018/os-14-525-2018.pdf https://doaj.org/article/b7bbe10dc9d440df96b8e09332c8bf69 en eng Copernicus Publications doi:10.5194/os-14-525-2018 1812-0784 1812-0792 https://www.ocean-sci.net/14/525/2018/os-14-525-2018.pdf https://doaj.org/article/b7bbe10dc9d440df96b8e09332c8bf69 undefined Ocean Science, Vol 14, Pp 525-541 (2018) geo envir Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2018 fttriple https://doi.org/10.5194/os-14-525-2018 2023-01-22T17:53:26Z 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 Unknown Ocean Science 14 3 525 541 |
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geo envir Y. Liu W. Fu Assimilating high-resolution sea surface temperature data improves the ocean forecast potential in the Baltic Sea |
topic_facet |
geo envir |
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://www.ocean-sci.net/14/525/2018/os-14-525-2018.pdf https://doaj.org/article/b7bbe10dc9d440df96b8e09332c8bf69 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
Ocean Science, Vol 14, Pp 525-541 (2018) |
op_relation |
doi:10.5194/os-14-525-2018 1812-0784 1812-0792 https://www.ocean-sci.net/14/525/2018/os-14-525-2018.pdf https://doaj.org/article/b7bbe10dc9d440df96b8e09332c8bf69 |
op_rights |
undefined |
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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1766191946440638464 |