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A hybrid scheme obtained by combining 3DVar with the Assimilation in theUnstable Subspace (3DVar-AUS) is tested in a QG model, under perfect modelconditions, with a fixed observational network, with and without observationalnoise. The AUS scheme, originally formulated to assimilate adaptiveobservations, is used here to assimilate the fixed observations that are foundin the region of local maxima of BDAS vectors (Bred vectors subject toassimilation), while the remaining observations are assimilated by 3DVar.  The performance of the hybrid scheme is compared with that of 3DVar and of anEnKF. The improvement gained by 3DVar-AUS and the EnKF with respect to 3DVaralone is similar in the present model and observational configuration, while3DVar-AUS outperforms the EnKF during the forecast stage. The 3DVar-AUSalgorithm is easy to implement and the results obtained in the idealizedconditions of this study encourage further investigation toward animplementation in more realistic contexts.

Controlling instabilities along a 3DVar analysis cycle by assimilating in the unstable subspace: a comparison with the EnKF