Assimilation of altimeter data into a quasigeostrophic ocean model using optimal interpolation and eofs

An ensemble of fraternal-twin experiments is used to assess the utility of optimal interpolation and model-based vertical empirical orthogonal functions (eofs) of streamfunction variability to assimilate satellite altimeter data into ocean models. Simulated altimeter data are assimilated into a basin-wide 3-layer quasi-geostrophic model with a horizontal grid spacing of 15 km. The effects of bottom topography are included and the model is forced by a wind stress curl distribution which is constant in time. The simulated data are extracted, along altimeter tracks with spatial and temporal characteristics of Geosat, from a reference model ocean with a slightly different climatology from that generated by the model used for assimilation. The use of vertical eofs determined from the model-generated streamfunction variability is shown to be effective in aiding the modelʹs dynamical extrapolation of the surface information throughout the rest of the water column. After a single repeat cycle (17 days), the analysis errors are reduced markedly from the initial level, by 52% in the surface layer, 41% in the second layer and 11% in the bottom layer. The largest differences between the assimilation analysis and the reference ocean are found in the nonlinear regime of the mid-latitude jet in all layers. After 100 days of assimilation, the error in the upper two layers has been reduced by over 50% and that in the bottom layer by 38%. The essence of the method is that the eofs capture the statistics of the dynamical balances in the model and ensure that this balance is not inappropriately disturbed during the assimilation process. This statistical balance includes any potential vorticity homogeneity which may be associated with the eddy stirring by mid-latitude surface jets.