Add to ORKGAbstract. In this paper we examine the links between Ensemble Kalman Filters (EnKF) and Particle Filters (PF). EnKF can be seen as a Mean-Field process with a PF approximation. We explore the problem of dimensionality on a toy model. To by-pass this difficulty, we suggest using Local Particle Filters (LPF) to catch non-lineartity and feed larger scale EnKF. To go one step forward we conclude with a real application and present the filtering of perturbed measurements of atmospheric wind in the domain of turbulence. This example is the cornerstone of the LPF for the assimilation of atmospheric turbulent wind. These local representation tech-niques will be use in further works to assimilate singular data of turbulence linked parameters in non-...
State space models are powerful modeling tools for stochastic dynamical systems and have been an imp...
Data assimilation in high-resolution atmosphere or ocean models is complicated because of the nonlin...
[1] The performance of the ensemble Kalman filter (EnKF) under imperfect model conditions is investi...
The ensemble Kalman filter (EnKF) is a reliable data assimilation tool for high-dimensional meteorol...
Ensemble methods such as the Ensemble Kalman Filter (EnKF) are widely used for data assimilation in ...
Nonlinear data assimilation methods like particle filters aim to improve the numerical weather predi...
The Ensemble Kalman filter (EnKF) is a standard algorithm in oceanography and meteorology, where it ...
Rainfall-runoff models play a very important role in flood forecasting. However, these models contai...
Abstract. The ensemble Kalman filter (EnKF) is a recursive filter suitable for problems with a large...
Non-linear filtering of local turbulent fluid measurements was an unexplored domain, in this paper w...
Data assimilation methods that work in high dimensional systems are crucial to many areas of the geo...
The ensemble Kalman filter (EnKF) and sequential importance resampling (SIR) are two Monte Carlo-bas...
The localized particle filter (LPF) is a recent advance in ensemble data assimilation for numerical ...
The ensemble Kalman filter (EnKF) and sequential importance resampling (SIR) are two Monte Carlo-bas...
Data assimilation (DA) has recently received growing interest by the hydrological modeling community...
State space models are powerful modeling tools for stochastic dynamical systems and have been an imp...
Data assimilation in high-resolution atmosphere or ocean models is complicated because of the nonlin...
[1] The performance of the ensemble Kalman filter (EnKF) under imperfect model conditions is investi...
The ensemble Kalman filter (EnKF) is a reliable data assimilation tool for high-dimensional meteorol...
Ensemble methods such as the Ensemble Kalman Filter (EnKF) are widely used for data assimilation in ...
Nonlinear data assimilation methods like particle filters aim to improve the numerical weather predi...
The Ensemble Kalman filter (EnKF) is a standard algorithm in oceanography and meteorology, where it ...
Rainfall-runoff models play a very important role in flood forecasting. However, these models contai...
Abstract. The ensemble Kalman filter (EnKF) is a recursive filter suitable for problems with a large...
Non-linear filtering of local turbulent fluid measurements was an unexplored domain, in this paper w...
Data assimilation methods that work in high dimensional systems are crucial to many areas of the geo...
The ensemble Kalman filter (EnKF) and sequential importance resampling (SIR) are two Monte Carlo-bas...
The localized particle filter (LPF) is a recent advance in ensemble data assimilation for numerical ...
The ensemble Kalman filter (EnKF) and sequential importance resampling (SIR) are two Monte Carlo-bas...
Data assimilation (DA) has recently received growing interest by the hydrological modeling community...
State space models are powerful modeling tools for stochastic dynamical systems and have been an imp...
Data assimilation in high-resolution atmosphere or ocean models is complicated because of the nonlin...
[1] The performance of the ensemble Kalman filter (EnKF) under imperfect model conditions is investi...