Abstract
Abstract. This paper describes the implementation and evaluation of a pre-operational three dimensional variational (3DVAR) data assimilation system for the North/Baltic Sea. Univariate analysis for both 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). Observations of temperature and salinity (T/S) profiles in the North/Baltic Sea are assimilated in the year of 2005. Effect of the 3DVAR scheme is assessed by a comparison between data assimilation run and control run. The statistical analysis indicates that the model simulation is significantly improved with the 3DVAR scheme. On average, the root mean square errors (RMSE) of temperature and salinity are 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.
Highlights
In coastal and shelf seas, operational forecasting systems become feasible in recent years due to several reasons: increasing maturity of numerical models, advances in systematic and real-time monitoring, and progresses in data assimilation techniques and applications
One major problem is the lack of real time insitu observations in an operational sense; Secondly, quality of satellite observations such as sea surface temperature (SST) and sea surface height (SSH) is relatively poor in coastal waters than open seas because the data is more influenced by the cloud cover in the coastal regions
The root mean square errors (RMSE) of temperature and salinity is reduced by 0.2 ◦C and 0.25 psu
Summary
In coastal and shelf seas, operational forecasting systems become feasible in recent years due to several reasons: increasing maturity of numerical models, advances in systematic and real-time monitoring, and progresses in data assimilation techniques and applications. One major problem is the lack of real time insitu observations in an operational sense; Secondly, quality of satellite observations such as SST and SSH is relatively poor in coastal waters than open seas because the data is more influenced by the cloud cover in the coastal regions This renders the assimilation more dependent on in-situ observations; Thirdly, complex topography and coastlines impose some technical constraints on coastal-shelf data assimilation schemes. ARF will be adopted and tested in the step for future practical applications In this implementation, design for scalability of observation operators is taken into account so that the increasingly expanded ocean measurements from various platforms can be more added into the data assimilation system.
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