Ensemble Data Assimilation and Prediction

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This research includes the fundamental development of probabilistic data assimilation methodologies, currently focusing on ensemble data assimilation. In particular, we are interested in improving our understanding of the impact of the limited number of degrees of freedom, dynamic localization, nonlinearity, non-differentiability of model and observation operators, probability density function assumption, interaction between scales, etc.

We are also interested in code development issues dealing with parallel code optimization, user-friendly script development, high-dimensionality, evaluation of the efficiency of ensemble data assimilation on super-computers, and code transferability.


This research topic is inherently connected with all other research topics and projects.

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