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The basic structure of the Maximum Likelihood Ensemble Filter (MLEF) is an iterative minimization algorithm. The nonlinear analysis solution is obtained by an iterative minimization of the cost function. This is one of the main differences between the MLEF and other ensemble data assimilation algorithms. It also provides a link with variational data assimilation methods.
We typically use the nonlinear conjugate gradient and the quasi-Newton unconstrained minimization algorithms, but we are also exploring other algorithms. Development of non-differentiable iterative minimization algorithms is also in focus of our research.
The MLEF includes an implicit Hessian preconditioning. We are interested in further development of Hessian preconditioning which allows better assimilation of highly nonlinear cost functions that may arise in a non-Gaussian PDF framework.
This research topic is a component of other research topics
- Parameter Estimation
- Dimension Reduction in Dynamical Systems
- Basic Development of Ensemble Data Assimilation
- Non-Gaussian Framework
and research projects
- Ensemble data assimilation system based on control theory
- Impact of fundamental assumptions of probabilistic data assimilation/ensemble forecasting: condition mode vs. conditional mean
- Ensemble Kalman filtering for Army-scale meteorology