Assimilation of glider and mooring data into a coastal ocean model

We have applied an ensemble optimal interpolation (EnOI) data assimilation system to a high resolution coastal ocean model of south-east Tasmania, Australia. The region is characterised by a complex coastline with water masses influenced by riverine input and the interaction between two offshore current systems. Using a large static ensemble to estimate the systems background error covariance, data from a coastal observing network of fixed moorings and a Slocum glider are assimilated into the model at daily intervals. We demonstrate that the EnOI algorithm can successfully correct a biased high resolution coastal model. In areas with dense observations, the assimilation scheme reduces the RMS difference between the model and independent GHRSST observations by 90%, while the domain-wide RMS difference is reduced by a more modest 40%. Our findings show that errors introduced by surface forcing and boundary conditions can be identified and reduced by a relatively sparse observing array using an inexpensive ensemble-based data assimilation system.

► We describe the assimilation system used in a operational coastal ocean model.
► Glider and mooring data are assimilated to improve the baroclinic structure.
► Temperature RMS errors are reduced by between 40% and 90%.
► The DA algorithm corrects for errors introduced by forcing and parametrisations.