Add to ORKGThe strong nonlinearity and non-Gaussian statistics of an ocean mixed layer model, which is based on the second-moment closure of turbulence, render traditional filtering techniques (e.g., Kalman filter) impractical for data assimilation. To overcome this problem, the sampling-importance resampling filter is introduced in this study. This filter represents the required (non-Gaussian) probability density function as a set of samples for implementing recursive Bayesian inference. It is not restricted by the assumption of linearity or Gaussain statistics. The numerical experiments using real life data clearly demonstrate the validity of this filter for the estimation problem of the ocean mixed layer process
Generally, in most applied fields, the dynamic state space models are of nonlinearity with non-Gauss...
A hybrid algorithm that combines the ensemble transform Kalman filter (ETKF) and the importance samp...
We consider likelihood inference and state estimation by means of importance sampling for state spac...
Data assimilation in high-resolution atmosphere or ocean models is complicated because of the nonlin...
Data assimilation in high-resolution atmosphere or ocean models is complicated because of the nonlin...
A data-assimilation method is introduced for large-scale applications in the ocean and the atmospher...
Data assimilation combines information from models, measurements, and priors to obtain improved esti...
The problem of estimating parameters of nonlinear dynamical systems based on incomplete noisy measur...
The weak-constraint inverse for nonlinear dynamical models is discussed and derived in terms of a pr...
This paper presents a fully non-Gaussian filter for sequential data assimilation. The filter is name...
We introduce a conditional Gaussian framework for data assimilation and prediction of nonlinear turb...
The purpose of this dissertation is to develop nonlinear filters and demonstrate their applications....
This review discusses recent advances in geophysical data assimilation beyond Gaussian statistical m...
In principle, general approaches to optimal nonlinear filtering can be described in a unified way fr...
With very few exceptions, data assimilation methods which have been used or proposed for use with oc...
Generally, in most applied fields, the dynamic state space models are of nonlinearity with non-Gauss...
A hybrid algorithm that combines the ensemble transform Kalman filter (ETKF) and the importance samp...
We consider likelihood inference and state estimation by means of importance sampling for state spac...
Data assimilation in high-resolution atmosphere or ocean models is complicated because of the nonlin...
Data assimilation in high-resolution atmosphere or ocean models is complicated because of the nonlin...
A data-assimilation method is introduced for large-scale applications in the ocean and the atmospher...
Data assimilation combines information from models, measurements, and priors to obtain improved esti...
The problem of estimating parameters of nonlinear dynamical systems based on incomplete noisy measur...
The weak-constraint inverse for nonlinear dynamical models is discussed and derived in terms of a pr...
This paper presents a fully non-Gaussian filter for sequential data assimilation. The filter is name...
We introduce a conditional Gaussian framework for data assimilation and prediction of nonlinear turb...
The purpose of this dissertation is to develop nonlinear filters and demonstrate their applications....
This review discusses recent advances in geophysical data assimilation beyond Gaussian statistical m...
In principle, general approaches to optimal nonlinear filtering can be described in a unified way fr...
With very few exceptions, data assimilation methods which have been used or proposed for use with oc...
Generally, in most applied fields, the dynamic state space models are of nonlinearity with non-Gauss...
A hybrid algorithm that combines the ensemble transform Kalman filter (ETKF) and the importance samp...
We consider likelihood inference and state estimation by means of importance sampling for state spac...