Enhanced Ocean Prediction using Ensemble Kalman Filter Techniques

LONG-TERM GOALS. To apply optimal data assimilation techniques to ocean circulation models in order to improve shortrange prediction of mesoscale circulation. OBJECTIVES. The immediate scientific objective of this research project is to develop a data assimilation system, based on ensemble Kalman filter EnKF techniques, and to apply this system to a realistic eddyresolving ocean circulation model. APPROACH. The basic elements of an ensemble-based data assimilation system include a system for collating and preparing observations, combining observations with a model now-cast, initializing and running an ensemble of forecasts and estimating model and observation errors. Each of these components are currently under development and are being systematically tested on a suite of models ranging from a simple linear model Evensen 2004, to a small highly non-linear model Lorenz and Emmanuel 1998, and finally to an idealised and realistic configuration of an ocean general circulation model MOM4.0 Griffies et al. 2004. Under this project, we have explored the rationale for different ensemble-based assimilation algorithms and techniques and compared the performance of different filters for a suite of small models Oke et al. 2006 Sakov et al. 2006. Subsequently, we have developed a new formulation for the EnKF that we refer to as the deterministic EnKF DEnKF Sakov and Oke 2006.