首页    期刊浏览 2024年12月12日 星期四
登录注册

文章基本信息

  • 标题:A comparison of error subspace Kalman filters
  • 本地全文:下载
  • 作者:Lars Nerger ; Wolfgang Hiller ; Jens Schröter
  • 期刊名称:Tellus A: Dynamic Meteorology and Oceanography
  • 电子版ISSN:1600-0870
  • 出版年度:2005
  • 卷号:57
  • 期号:5
  • 页码:715-735
  • DOI:10.3402/tellusa.v57i5.14732
  • 摘要:Three advanced filter algorithms based on the Kalman filter are reviewed and presented in a unified notation. They are the well-known ensemble Kalman filter (EnKF), the singular evolutive extended Kalman (SEEK) filter, and the less common singular evolutive interpolated Kalman (SEIK) filter. For comparison, the mathematical formulations of the filters are reviewed in relation to the extended Kalman filter as error subspace Kalman filters. The algorithms are presented in their original form and possible variations are discussed. A comparison of the algorithms shows their theoretical capabilities for efficient data assimilation with large-scale non-linear systems. In particular, problems of the analysis equations are apparent in the original EnKF algorithm due to the Monte Carlo sampling of ensembles. Theoretically, the SEIK filter appears to be a numerically very efficient algorithm with high potential for use with non-linear models. The superiority of the SEIK filter is demonstrated on the basis of identical twin experiments using a shallow-water model with non-linear evolution. Identical initial conditions for all three filters allow for a consistent comparison of the data assimilation results. These show how choices of particular state ensembles and assimilation schemes lead to significant variations of the filter performance. This is related to different qualities of the predicted error subspaces, as is demonstrated in an examination of the predicted state covariance matrices.
国家哲学社会科学文献中心版权所有