Reduced order approaches for variational data assimilation in oceanography
Résumé
A reduced order approach for 4D-Var data assimilation is presented in the context of a tropical Pacific ocean model. The control space is defined as the span of a few vectors representing a significant part of the system variability, while the model itself is not reduced. It is shown that such an approach can lead to significant improvements, both in terms of the quality of the solution and of the computational efficiency, with regard to data assimilation with a full control vector. However several limitations of the approach will also be discussed, as well as a first step towards an hybrid variational-sequential algorithm.