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Ocean Science An interactive open-access journal of the European Geosciences Union
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Volume 14, issue 3 | Copyright
Ocean Sci., 14, 525-541, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 25 Jun 2018

Research article | 25 Jun 2018

Assimilating high-resolution sea surface temperature data improves the ocean forecast potential in the Baltic Sea

Ye Liu1 and Weiwei Fu2 Ye Liu and Weiwei Fu
  • 1Swedish Meteorological and Hydrological Institute, Norrköping 60176, Sweden
  • 2Department of Earth System Science, University of California Irvine, Irvine, CA 92697, USA

Abstract. 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 100m 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.

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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...