• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Nonlinear processes in geophysics >Review article: Comparison of local particle filters and new implementations
【24h】

Review article: Comparison of local particle filters and new implementations

机译：评论文章：本地粒子过滤器与新实现的比较

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Particle filtering is a generic weighted ensemble data assimilation method based on sequential importance sampling, suited for nonlinear and non-Gaussian filtering problems. Unless the number of ensemble members scales exponentially with the problem size, particle filter (PF) algorithms experience weight degeneracy. This phenomenon is a manifestation of the curse of dimensionality that prevents the use of PF methods for high-dimensional data assimilation. The use of local analyses to counteract the curse of dimensionality was suggested early in the development of PF algorithms. However, implementing localisation in the PF is a challenge, because there is no simple and yet consistent way of gluing together locally updated particles across domains. In this article, we review the ideas related to localisation and the PF in the geosciences. We introduce a generic and theoretical classification of local particle filter (LPF) algorithms, with an emphasis on the advantages and drawbacks of each category. Alongside the classification, we suggest practical solutions to the difficulties of local particle filtering, which lead to new implementations and improvements in the design of LPF algorithms. The LPF algorithms are systematically tested and compared using twin experiments with the one-dimensional Lorenz 40-variables model and with a two-dimensional barotropic vorticity model. The results illustrate the advantages of using the optimal transport theory to design the local analysis. With reasonable ensemble sizes, the best LPF algorithms yield data assimilation scores comparable to those of typical ensemble Kalman filter algorithms, even for a mildly nonlinear system.
机译：粒子滤波是一种基于顺序重要性采样的通用加权集成数据同化方法，适用于非线性和非高斯滤波问题。除非合奏成员的数量与问题大小成指数比例缩放，否则粒子过滤器（PF）算法将经历权重退化。这种现象是维数诅咒的一种表现，它阻止了将PF方法用于高维数据同化。在PF算法的开发早期，建议使用局部分析来抵消维数的诅咒。但是，在PF中实现本地化是一个挑战，因为没有简单而一致的方法将跨域的本地更新粒子粘合在一起。在本文中，我们将回顾与地球科学中的定位和PF相关的思想。我们介绍了局部粒子滤波（LPF）算法的一般和理论分类，重点介绍了每种类别的优缺点。除了分类，我们还建议解决局部粒子滤波困难的实际解决方案，这将导致LPF算法设计的新实现和改进。 LPF算法经过系统测试，并通过双实验与一维Lorenz 40变量模型和二维正压涡度模型进行了比较。结果说明了使用最佳输运理论设计局部分析的优势。在合理的总体大小下，即使对于轻度非线性系统，最佳的LPF算法产生的数据同化分数也可与典型的整体Kalman滤波算法相比。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号