Bridging the ensemble Kalman and particle filters

• 成果类型: Article
• 署名作者: Frei, M.; Kuensch, H. R.
• 署名单位: Swiss Federal Institutes of Technology Domain; ETH Zurich
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/ast020
• 发表日期: 2013
• 页码: 781800
• 关键词: atmospheric data assimilation; State estimation; MODEL; simulation; statistics

摘要

In many applications of Monte Carlo nonlinear filtering, the propagation step is computationally expensive, and hence the sample size is limited. With small sample sizes, the update step becomes crucial. Particle filtering suffers from the well-known problem of sample degeneracy. Ensemble Kalman filtering avoids this, at the expense of treating non-Gaussian features of the forecast distribution incorrectly. Here we introduce a procedure that makes a continuous transition indexed by gamma[0,1] between the ensemble and the particle filter update. We propose automatic choices of the parameter gamma such that the update stays as close as possible to the particle filter update subject to avoiding degeneracy. In various examples, we show that this procedure leads to updates that are able to handle non-Gaussian features of the forecast sample even in high-dimensional situations.