A Reduced-Order Information Filter for Multilayer Shallow-Water Models: Profiling and Assimilation of Sea Surface Height

Abstract

A reduced-order information filter (ROIF) for the Miami Isopycnal Coordinate Ocean Model (MICOM) is implemented for assimilation of the TOPEX/Poseidon sea surface height (SSH) data. ROIF is an approximate Kalman filter that compactly parameterizes the covariance matrix using a Gaussian?Markov random field. Performance of the assimilation system is investigated through observation system simulation experiments in an identical twin scenario. An adiabatic and eddy-resolving (20-km horizontal resolution) configuration for double-gyre simulation, as well as a more realistic North Atlantic model with thermodynamics, are considered. In each case, a 180-day assimilation window is found sufficient to reconstruct the surface layer topography by assimilating the SSH data sampled under the satellite tracks. The reconstructed geometric features, such as jet meanders, are found to be qualitatively accurate. A subsequent forecast run (without data assimilation) has also remained stable and accurate. An important profiling parameter in SSH assimilation is a strong positive correlation between SSH and the surface pressure (layer thickness).