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Implicit particle methods and their connection with variational data assimilation
Implicit particle methods variational data assimilation Data Analysis Statistics and Probability
2012/5/8
The implicit particle filter is a sequential Monte Carlo method for data assimilation that guides the particles to the high-probability regions via a sequence of steps that includes minimizations. We ...
Evaluating Data Assimilation Algorithms
Evaluate Data Assimilation Algorithms assimilation refers
2011/6/20
Abstract: Data assimilation refers to methodologies for the incorporation of noisy observations of a physical system into an underlying model in order to infer the properties of the state of the syste...
Nonlinear data-assimilation using implicit models
Nonlinear data-assimilation implicit models
2009/11/9
We show how the traditional 4D-Var method can be adapted for implicit time-integration and extended for multi-parameter estimation. We present the algorithm for this new method, which we call I4D-Var,...
On deterministic error analysis in variational data assimilation
deterministic error analysis variational data assimilation
2009/11/9
The problem of variational data assimilation for a nonlinear evolution model is considered to identify the initial condition. The equation for the error of the optimal initial-value function through t...
Merging particle filter for sequential data assimilation
particle filter sequential data assimilation
2009/11/3
A new filtering technique for sequential data assimilation, the merging particle filter (MPF), is proposed. The MPF is devised to avoid the degeneration problem, which is inevitable in the particle fi...
Model error estimation in ensemble data assimilation
Model error estimation ensemble data assimilation
2009/11/2
A new methodology is proposed to estimate and account for systematic model error in linear filtering as well as in nonlinear ensemble based filtering. Our results extend the work of Dee and Todling (2...