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This paper describes the implementation and evaluation of a pre-operational three dimensional variational (3DVAR) data assimilation system for the North/Baltic Sea. The univariate analysis for temperature and salinity is applied in a 3DVAR scheme in which the horizontal component of the background error covariance is modeled by an isotropic recursive filter (IRF) and the vertical component is represented by dominant Empirical Orthogonal Functions (EOFs) of the background error. Observations of temperature and salinity (T/S) profiles in the North/Baltic Sea are assimilated in the year of 2005. Effectiveness of the data assimilation scheme is assessed by comparison with the control run that no assimilation is done. The statistical analysis indicates that the model simulation is significantly improved with the 3DVAR scheme. On average, the root mean square error (RMSE) of temperature and salinity is reduced by 0.2 °C and 0.25 psu in the North/Baltic Sea. In addition, the bias of temperature and salinity is also decreased by 0.1 °C and 0.2 psu, respectively. Starting from an analyzed initial state, one month simulation without assimilation is carried out with the aim of examining the persistence of the initial impact. It is shown that the assimilated initial state can impact the model simulation for nearly two weeks. The influence on salinity is more pronounced than temperature. [ABSTRACT FROM AUTHOR]

The spatial averaged correlations are presented in 1.5° x 1.5° bins for the North and Baltic Sea region. The averaged correlations are computed based on the proxy ocean data generated by the operational forecast model of Danish Meteorology Institute (DMI). It is shown that the spatial distribution of the averaged correlations could reflect the overall influence of the local atmospheric forcing, complex topography, coastlines, boundary and bottom effect, etc. Comparisons with the satellite SST data demonstrate that the proxy ocean data reproduce realistic results at the surface. Based on the spatial bin-averaged correlations, a general correlation model is assumed to approximate the spatial and temporal correlation structure. Parameters of the correlation model are obtained on the standard Levitus levels. It is found that the correlation model is not the typical Guaussian-type function. For instance, the exponents of the correlation model vary in the longitudinal direction from 0.75 at the surface to 1.33 at the depth of 250 m for temperature. For salinity, the temporal correlation can be approximated with an exponential function. Two complementary quality-indicators, effective coverage rate and "explained" variance, are defined based on the correlation models obtained above. The two indicators are able to identify the "influence area" of the information content in a given observation network and the relative importance of observations at different locations. By these indicators, the 3-D temperature and salinity observational networks are assessed in the Baltic Sea and North Sea for the period 2004-2006. It is found that the surface level is more effectively covered than the deep waters with existing networks. In addition, the Belt Sea and the Baltic Proper also show good coverage for both temperature and salinity. However, more observations are required in the Norwegian Trench and Kattegat. In the vertical, the two indicators show smaller values from 50 m to 125 m in this region, indicating the need for more observations. [ABSTRACT FROM AUTHOR]

The spatial averaged correlations are presented in 1.5° x1.5° bins for the North and Baltic Sea region. The averaged correlations are computed based on the proxy ocean data generated by the operational forecat model of Danish Meteorology Institute (DMI). It is shown that the spatial distribution of the averaged correlations could reflect the overall influence of the local atmospheric forcing, complex topography, coastlines, boundary and bottom effect, etc. Comparisons with the satellite SST data demonstrate that the proxy ocean data reproduces quite realistic results at the surface. Based on the spatial bin-averaged correlations, a general correlation model is assumed to approximate the spatial and temporal correlation structure. Parameters of the correlation model are obtained on the standard Levitus levels. It is found that the correlation model is not the typical Guaussian-type function. For instance, the exponents of the correlation model vary in the longitudinal direction from 0.75 at the surface to 1.33 at the depth of 250m for temperature. For salinity, the temporal correlation can be approximated with an exponential function. Two complementary quality-indicators, effective coverage rate and "explained" variance, are defined based on the correlation models obtained above. The two indicators are able to identify the "influence area" of the information content in a given observation network and the relative importance of observations at different locations. By these indicators, the 3-D temperature and salinty observational networks are assessed in the Baltic Sea and North Sea for the period 2004-2006. It is found that the surface level is more effectively covered with existing netwroks. In addition, the Belt Sea and the Baltic Proper also have good observation coverage for both temperature and salinity. However, more observations are required in the Norwegian Trench and Kattegat. In the vertical, the two indicators show smaller values from 50m to 125m in this region, indicating the need for more observations. [ABSTRACT FROM AUTHOR]