The GMAO’s Ensemble Kalman Filter Ocean Data Assimilation System

INTRODUCTION The ensemble Kalman filter (EnKF) was introduced to the ocean and atmospheric assimilation communities by Evensen (1994). The main attractiveness of ensemble techniques stems from their accounting for the temporal evolution of background-error covariances at small computational cost and from the ease of their implementation for complex systems. Ensemble filters have now been implemented in several real applications with state-of the art ocean (e.g., Keppenne et al., 2005; Zhang et al., 2007), atmosphere (e.g., Whitaker and Hamill, 2002; Hunt et al., 2007; Houtekamer and Mitchell, 2005) and land surface (e.g., Reichle et al., 2007) models. The Global Modeling and Assimilation Office (GMAO) developed an EnKF ocean data assimilation system to initialize a coupled model for seasonal forecasts (e.g., Keppenne et al., 2002, 2005). This paper describes the current system and its performance relative to that for the GMAO system based on optimal interpolation.

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