A random map implementation of implicit filters

被引：75

作者：

Morzfeld, Matthias ^{[1
]}

Tu, Xuemin ^{[2
]}

Atkins, Ethan ^{[1
,3
]}

Chorin, Alexandre J. ^{[1
,3
]}

机构：

[1] Univ Calif Berkeley, Lawrence Berkeley Natl Lab, Berkeley, CA 94720 USA

[2] Univ Kansas, Dept Math, Lawrence, KS 66045 USA

[3] Univ Calif Berkeley, Dept Math, Berkeley, CA 94720 USA

来源：

JOURNAL OF COMPUTATIONAL PHYSICS | 2012年 / 231卷 / 04期

基金：

美国国家科学基金会;

关键词：

Data assimilation; Implicit sampling; Particle filters; Sequential Monte Carlo; KURAMOTO-SIVASHINSKY EQUATION; DATA ASSIMILATION; PARTICLE FILTERS; DIFFERENTIAL-EQUATIONS; SYSTEMS; NOISE; PDES;

D O I：

10.1016/j.jcp.2011.11.022

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Implicit particle filters for data assimilation generate high-probability samples by representing each particle location as a separate function of a common reference variable. This representation requires that a certain underdetermined equation be solved for each particle and at each time an observation becomes available. We present a new implementation of implicit filters in which we find the solution of the equation via a random map. As examples, we assimilate data for a stochastically driven Lorenz system with sparse observations and for a stochastic Kuramoto-Sivashinsky equation with observations that are sparse in both space and time. Published by Elsevier Inc.

引用

页码：2049 / 2066

页数：18

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