Articles | Volume 14, issue 3
https://doi.org/10.5194/os-14-525-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-14-525-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Jun 2018
Research article |
| 25 Jun 2018
| 25 Jun 2018
Assimilating high-resolution sea surface temperature data improves the ocean forecast potential in the Baltic Sea
Ye Liu
CORRESPONDING AUTHOR
Swedish Meteorological and Hydrological Institute, Norrköping 60176, Sweden
Weiwei Fu
Department of Earth System Science, University of California Irvine, Irvine, CA 92697, USA
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Cited
17 citations as recorded by crossref.
- Optimal Assimilation of Daytime SST Retrievals from SEVIRI in a Regional Ocean Prediction System A. Storto & P. Oddo 10.3390/rs11232776
- Adaptive graph neural network based South China Sea seawater temperature prediction and multivariate uncertainty correlation analysis J. Pan et al. 10.1007/s00477-022-02371-3
- Assessing impacts of observations on ocean circulation models with examples from coastal, shelf, and marginal seas C. Edwards et al. 10.3389/fmars.2024.1458036
- Spatiotemporal Prediction of Monthly Coastal Upwelling Scenario in SST Fields Using Deep-Learning-Based Models M. Snoussi et al. 10.1109/LGRS.2024.3381438
- Satellite-Observed Spatial and Temporal Sea Surface Temperature Trends of the Baltic Sea between 1982 and 2021 S. Jamali et al. 10.3390/rs15010102
- A spatiotemporal deep learning model for sea surface temperature field prediction using time-series satellite data C. Xiao et al. 10.1016/j.envsoft.2019.104502
- Baltic Sea freshwater content U. Raudsepp et al. 10.5194/sp-1-osr7-7-2023
- HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model Q. Tang et al. 10.5194/gmd-17-3559-2024
- HE-DFNETS: A Novel Hybrid Deep Learning Architecture for the Prediction of Potential Fishing Zone Areas in Indian Ocean Using Remote Sensing Images M. Sivasankari et al. 10.1155/2022/5081541
- Effective ensemble learning approach for SST field prediction using attention-based PredRNN B. Qiao et al. 10.1007/s11704-021-1080-7
- Data assimilation of sea surface temperature and salinity using basin-scale reconstruction from empirical orthogonal functions: a feasibility study in the northeastern Baltic Sea M. Zujev et al. 10.5194/os-17-91-2021
- Temperature assimilation into a coastal ocean-biogeochemical model: assessment of weakly and strongly coupled data assimilation M. Goodliff et al. 10.1007/s10236-019-01299-7
- Landsat 8 Data as a Source of High Resolution Sea Surface Temperature Maps in the Baltic Sea K. Bradtke 10.3390/rs13224619
- Applying satellite sea surface temperature as Dirichlet-type surface thermal boundary condition in an ocean model T. Zhang et al. 10.1016/j.ocemod.2024.102423
- Sea Surface Temperature and High Water Temperature Occurrence Prediction Using a Long Short-Term Memory Model M. Kim et al. 10.3390/rs12213654
- Super-resolution reconstruction of subsurface temperature field in South China Sea using satellite observations H. Wu et al. 10.1063/5.0253807
- Use of Infrared Satellite Observations for the Surface Temperature Retrieval over Land in a NWP Context M. Sassi et al. 10.3390/rs11202371
16 citations as recorded by crossref.
- Optimal Assimilation of Daytime SST Retrievals from SEVIRI in a Regional Ocean Prediction System A. Storto & P. Oddo 10.3390/rs11232776
- Adaptive graph neural network based South China Sea seawater temperature prediction and multivariate uncertainty correlation analysis J. Pan et al. 10.1007/s00477-022-02371-3
- Assessing impacts of observations on ocean circulation models with examples from coastal, shelf, and marginal seas C. Edwards et al. 10.3389/fmars.2024.1458036
- Spatiotemporal Prediction of Monthly Coastal Upwelling Scenario in SST Fields Using Deep-Learning-Based Models M. Snoussi et al. 10.1109/LGRS.2024.3381438
- Satellite-Observed Spatial and Temporal Sea Surface Temperature Trends of the Baltic Sea between 1982 and 2021 S. Jamali et al. 10.3390/rs15010102
- A spatiotemporal deep learning model for sea surface temperature field prediction using time-series satellite data C. Xiao et al. 10.1016/j.envsoft.2019.104502
- Baltic Sea freshwater content U. Raudsepp et al. 10.5194/sp-1-osr7-7-2023
- HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model Q. Tang et al. 10.5194/gmd-17-3559-2024
- HE-DFNETS: A Novel Hybrid Deep Learning Architecture for the Prediction of Potential Fishing Zone Areas in Indian Ocean Using Remote Sensing Images M. Sivasankari et al. 10.1155/2022/5081541
- Effective ensemble learning approach for SST field prediction using attention-based PredRNN B. Qiao et al. 10.1007/s11704-021-1080-7
- Data assimilation of sea surface temperature and salinity using basin-scale reconstruction from empirical orthogonal functions: a feasibility study in the northeastern Baltic Sea M. Zujev et al. 10.5194/os-17-91-2021
- Temperature assimilation into a coastal ocean-biogeochemical model: assessment of weakly and strongly coupled data assimilation M. Goodliff et al. 10.1007/s10236-019-01299-7
- Landsat 8 Data as a Source of High Resolution Sea Surface Temperature Maps in the Baltic Sea K. Bradtke 10.3390/rs13224619
- Applying satellite sea surface temperature as Dirichlet-type surface thermal boundary condition in an ocean model T. Zhang et al. 10.1016/j.ocemod.2024.102423
- Sea Surface Temperature and High Water Temperature Occurrence Prediction Using a Long Short-Term Memory Model M. Kim et al. 10.3390/rs12213654
- Super-resolution reconstruction of subsurface temperature field in South China Sea using satellite observations H. Wu et al. 10.1063/5.0253807
1 citations as recorded by crossref.
Latest update: 23 Apr 2025
Short summary
We assess the impact of assimilating the SST data on the Baltic forecast potential. By assimilating SST, we find the quality of SST forecast is significantly enhanced. The temperature in water above 100 m and salinity in the deep layers have been also largely and slightly improved, respectively. In comparison with independent data, the SLA is better predicted because of assimilating SST. Besides, the forecast of sea-ice concentration is improved considerably during the sea-ice formation period.
We assess the impact of assimilating the SST data on the Baltic forecast potential. By...
