The ensemble Kalman filter (EnKF) is a reliable data assimilation tool for high-dimensional meteorological problems. On the other hand, the EnKF can be interpreted as a particle filter, and particle filters (PF) collapse in high-dimensional problems. We explain that these seemingly contradictory statements offer insights about how PF function in certain high-dimensional problems, and in particular support recent efforts in meteorology to 'localize' particle filters, i.e. to restrict the influence of an observation to its neighbourhood
Data assimilation methods that work in high dimensional systems are crucial to many areas of the geo...
The data assimilation problem consists in finding a way to use observations within a model to improv...
Data assimilation in high-resolution atmosphere or ocean models is complicated because of the nonlin...
The ensemble Kalman filter (EnKF) is a reliable data assimilation tool for high-dimensional meteorol...
Abstract. In this paper we examine the links between Ensemble Kalman Filters (EnKF) and Particle Fil...
The Ensemble Kalman filter (EnKF) is a standard algorithm in oceanography and meteorology, where it ...
Abstract. The ensemble Kalman filter (EnKF) is a recursive filter suitable for problems with a large...
Rainfall-runoff models play a very important role in flood forecasting. However, these models contai...
This book contains two review articles on nonlinear data assimilation that deal with closely related...
Ensemble methods such as the Ensemble Kalman Filter (EnKF) are widely used for data assimilation in ...
The aim of this contribution is to provide a description of the difference between Kalman filter and...
Particle filters contain the promise of fully nonlinear data assimilation. They have been applied in...
The ensemble Kalman filter (EnKF) is a Monte Carlo-based implementation of the Kalman filter (KF) fo...
A novel particle filter proposed recently, the particle flow filter (PFF), avoids the long-existing...
Particle Filters are Monte-Carlo methods used for Bayesian Inference. Bayesian Inference is based on...
Data assimilation methods that work in high dimensional systems are crucial to many areas of the geo...
The data assimilation problem consists in finding a way to use observations within a model to improv...
Data assimilation in high-resolution atmosphere or ocean models is complicated because of the nonlin...
The ensemble Kalman filter (EnKF) is a reliable data assimilation tool for high-dimensional meteorol...
Abstract. In this paper we examine the links between Ensemble Kalman Filters (EnKF) and Particle Fil...
The Ensemble Kalman filter (EnKF) is a standard algorithm in oceanography and meteorology, where it ...
Abstract. The ensemble Kalman filter (EnKF) is a recursive filter suitable for problems with a large...
Rainfall-runoff models play a very important role in flood forecasting. However, these models contai...
This book contains two review articles on nonlinear data assimilation that deal with closely related...
Ensemble methods such as the Ensemble Kalman Filter (EnKF) are widely used for data assimilation in ...
The aim of this contribution is to provide a description of the difference between Kalman filter and...
Particle filters contain the promise of fully nonlinear data assimilation. They have been applied in...
The ensemble Kalman filter (EnKF) is a Monte Carlo-based implementation of the Kalman filter (KF) fo...
A novel particle filter proposed recently, the particle flow filter (PFF), avoids the long-existing...
Particle Filters are Monte-Carlo methods used for Bayesian Inference. Bayesian Inference is based on...
Data assimilation methods that work in high dimensional systems are crucial to many areas of the geo...
The data assimilation problem consists in finding a way to use observations within a model to improv...
Data assimilation in high-resolution atmosphere or ocean models is complicated because of the nonlin...