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 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.
High Resolution Discharge Simulations Over Europe and the Baltic Sea Catchment