  This paper presents a comparison of two reduced-order, sequential and
variational data assimilation methods: the SEEK filter and the R-4D-Var. A
hybridization of the two, combining the variational framework and the
sequential evolution of covariance matrices, is also preliminarily investigated
and assessed in the same experimental conditions. The comparison is performed
using the twin-experiment approach on a model of the Tropical Pacific domain.
The assimilated data are simulated temperature profiles at the locations of the
TAO/TRITON array moorings. It is shown that, in a quasi-linear regime, both
methods produce similarly good results. However the hybrid approach provides
slightly better results and thus appears as potentially fruitful. In a more
non-linear regime, when Tropical Instability Waves develop, the global nature
of the variational approach helps control model dynamics better than the
sequential approach of the SEEK filter. This aspect is probably enhanced by the
context of the experiments in that there is a limited amount of assimilated
data and no model error.

Geophysics;Analysis of PDEs;Optimization and Control