The use of perturbed observations in the traditional ensemble Kalman filter (EnKF) results in a suboptimal filter behaviour, particularly for small ensembles. In this work, we propose a simple modification to the traditional EnKF that results in matching the analysed error covariance given by Kalman filter in cases when the correction is small; without perturbed observations. The proposed filte...

A 4-dimensional ensemble Kalman filter method (4DEnKF), which adapts ensemble Kalman filtering to the assimilation of observations that are asynchronous with the analysis cycle, is discussed. In the ideal case of linear dynamics between consecutive analyses, the algorithm is equivalent to Kalman filtering assimilation at each observation time. Tests of 4DEnKF on the Lorenz 40 variable model are...

Numerical weather prediction is characterized by high-dimensional, nonlinear systems and poses difficult challenges for real-time data assimilation (updating) and forecasting. The goal of this work is to build on the ensemble Kalman filter (EnsKF) to produce ensemble filtering techniques applicable to non-Gaussian densities in high dimensions. Two filtering algorithms are presented which extend...

Combined state and parameter estimation of dynamical systems plays a crucial role in extracting system response from noisy measurements. A wide variety of methods have been developed to deal with the joint state-parameter estimation of nonlinear dynamical systems. The Extended Kalman Filter method is a popular approach for the joint systemparameter estimation of nonlinear systems. This method c...

The time has come when ensemble-based Kalman filter data assimilation schemes can be considered for implementation on operational weather forecast systems in the foreseeable future. For the first time, an ensemble Kalman filter has been reported to break even with a sophisticated operational 3DVar system (Houtekamer et al 2004), to outperform the NCEP 3D-Var in reconstructing the state of the m...

This paper deals with the state estimation of a strongly nonlinear system. In a noisy state space representation setting, Central Difference Kalman Filter, Ensemble Kalman Filter and Particle Filter are tested on a second order system. The choice of estimators parameters is then discussed, and their behaviour in relation to noise is studied, in order to compare estimation quality according to n...