2006•Unpublished venueRequires access

What is the ensemble Kalman filter and how well does it work?

Steven Gillijns, O.B. Mendoza, J. Chandrasekar, Bart L. R. De Moor, Dennis S. Bernstein, A. J. Ridley

Open publisher page 197 citations

Abstract

In this paper we described the ensemble Kalman filter algorithm. This approach to nonlinear Kalman filtering is a Monte Carlo procedure, which has been widely used in weather forecasting applications. Our goal was to apply the ensemble Kalman filter to representative examples to quantify the tradeoff between estimation accuracy and ensemble size. For all of the linear and nonlinear examples that we considered, the ensemble Kalman filter worked successfully once a threshold ensemble size was reached. In future work we will investigate the factors that determine this threshold value.

About this research paper

What this paper is about

In this paper we described the ensemble Kalman filter algorithm. This approach to nonlinear Kalman filtering is a Monte Carlo procedure, which has been widely used in weather forecasting applications. Our goal was to apply the ensemble Kalman filter to representative examples to quantify the tradeoff between estimation accuracy and ensemble size. For all of the linear and nonlinear examples that we considered, the ensemble Kalman filter worked successfully once a threshold ensemble size was reached. In future work we will investigate the factors that determine this threshold value.

Why it matters

OpenAlex reports 197 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

In this paper we described the ensemble Kalman filter algorithm. This approach to nonlinear Kalman filtering is a Monte Carlo procedure, which has been widely used in weather forecasting applications. Our goal was to apply the ensemble Kalman filter to representative examples to quantify the tradeoff between estimation accuracy and ensemble size. For all of the linear and nonlinear examples that we considered, the ensemble Kalman filter worked successfully once a threshold ensemble size was reached. In future work we will investigate the factors that determine this threshold value.

Key concepts: Ensemble Kalman filter, Kalman filter, Fast Kalman filter, Extended Kalman filter, Invariant extended Kalman filter, Computer science, Alpha beta filter, Monte Carlo method

Related papers

Back to paper searchBrowse research topicsOriginal source
What is the ensemble Kalman filter and how well does it work? — Research Paper | ScholarLens