Written with several of my key collaborators, this paper gives an overview on data assimilation for chaotic models with an emphasis on dynamical systems aspects. This overview also features two new contribitions, one on a couple ocean-atmosphere model and another with the particle filter, both leveraging our knowledge on the cahotic dynamics. It can also be seen as an extended retrospective on our past seven years on dynamics-knowledge-based data assimilation.

The paper is published in the Springer book Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. IV), a collection of papers directed by Seon Ki Park and Liang Xu. Our chapter can be found here, or as a preprint .