A Hybrid Ensemble Transform Particle Filter for Nonlinear and Spatially Extended Dynamical Systems
A Hybrid Ensemble Transform Particle Filter for Nonlinear and Spatially Extended Dynamical Systems
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DOI：
10.1137/15m1040967
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发表时间：
2016-01-01
影响因子：
2
通讯作者：
Reinhardt, Maria
中科院分区：
文献类型：
--
作者：
Chustagulprom, Nawinda;Reich, Sebastian;Reinhardt, Maria
数据同化是将演变模型和观测数据相结合的任务，以便产生可靠的预测。在本文中，我们专注于基于集合的递归数据同化问题。我们的主要贡献是一个混合滤波器，允许一个自适应混合合奏卡尔曼和粒子滤波器。虽然集合卡尔曼滤波器是强大的，适用于强非线性系统，即使小和中等的合奏大小，粒子滤波器是渐近一致的大合奏大小限制。我们证明了数值计算，我们的混合方法可以提高性能的卡尔曼和粒子滤波器在适度的合奏大小。我们还展示了如何实现本地化的概念到一个混合过滤器，这是其适用性的空间扩展系统的关键。
Data assimilation is the task of combining evolution models and observational data in order to produce reliable predictions. In this paper, we focus on ensemble-based recursive data assimilation problems. Our main contribution is a hybrid filter that allows one to adaptively blend ensemble Kalman and particle filters. While ensemble Kalman filters are robust and applicable to strongly nonlinear systems even with small and moderate ensemble sizes, particle filters are asymptotically consistent in the large ensemble size limit. We demonstrate numerically that our hybrid approach can improve the performance of both Kalman and particle filters at moderate ensemble sizes. We also show how to implement the concept of localization into a hybrid filter, which is key to its applicability for spatially extended systems.