Impact of temperature error models in a univariate ocean data assimilation system

Ocean data assimilation systems combine observations with information from prediction models to produce an analysis or estimate of the ocean state. Statistical interpolation assimilation methods use observations to correct a model-based first guess and require specification of first-guess and observation error statistics. Often the first-guess error covariance (FGEC) is described by an analytical covariance function whose structure is not directly related to ocean dynamics. On the other hand, ensemble and reduced-space methods represent the FGEC by a low-rank approximation coming from the dynamical model. Here we examine the impact of adding a low-rank FGEC component to an operational univariate ocean data assimilation (ODA) system. Smallscale structures are eliminated from the mean temperature correction and positive impact is seen in the zonal currents.