Abstract
Ensemble-based methods have become very popular for data assimilation in numerical models of oceanic or atmospheric flows. Unlike the deterministic Extended Kalman Filter which explicitly describes the evolution of the best estimate of the system state and the associated error covariance, ensemble filters rely on the stochastic integration of an ensemble of model trajectories that are intermittently updated according to data, using the forecast error covariance represented by the ensemble spread. In this chapter, we present an overview of recent developments of ensemble-based assimilation methods that were motivated by the need for cost-effective algorithms in operational oceanography. We finally discuss a number of standing issues related to temporal assimilation strategies.
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