Didier Auroux, Samira Amraoui, Jacques Blum, Blaise Faugeras

Nudging-based observers for geophysical data assimilation and joint state-parameters estimation

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Oceans and the atmosphere are governed by the general equations of fluid dynamics. Data assimilation consists of estimating the state of a system by combining, via numerical methods, two different sources of information: models and observations. The Back and Forth Nudging (BFN) algorithm is a prototype of a new class of data assimilation methods. The nudging technique consists in adding a feedback term in the model equations, measuring the difference between the observations and the corresponding space states. The BFN algorithm is an iterative sequence of forward and backward resolutions, all of them being performed with an additional nudging feedback term in the model equations. These nudging-based algorithms can be extended with the aim of correcting non-observed variables. This particularly concerns model parameter identification , with the potential of improving the quality and the confidence in the model state for future data assimilation processes.

Keywords : geophysical applications, data model coupling, model forecast, Parameter estimation

### Citation

Didier Auroux, Samira Amraoui, Jacques Blum, Blaise Faugeras, « Nudging-based observers for geophysical data assimilation and joint state-parameters estimation », paru dans

*Proceedings of the Complex Systems Academy of Excellence*, Proceedings. URL : https://hal.archives-ouvertes.fr/hal-02006637