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.

E.M. Jones, P.R. Oke, F. Rizwi and L.M. Murray (2012). <a href="https://indii.org/research/assimilation-of-glider-and-moorning-data-into-a-coastal-ocean-model/">Assimilation of glider and mooring data into a coastal ocean model</a>. <em>Ocean Modelling</em>. <strong>47</strong>:1-13.

@Article{Jones2012,,
  title = {Assimilation of glider and mooring data into a coastal ocean model},
  author = {Emlyn M. Jones and Peter R. Oke and Farhan Rizwi and Lawrence M. Murray},
  journal = {Ocean Modelling},
  year = {2012},
  volume = {47},
  pages = {1-13},
  doi = {10.1016/j.ocemod.2011.12.009}
}