A random map implementation of implicit filters
Numerical Analysis
2015-05-27 v5 Mathematical Physics
math.MP
Probability
Abstract
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-Sivashinski equation with observations that are sparse in both space and time.
Keywords
Cite
@article{arxiv.1102.4375,
title = {A random map implementation of implicit filters},
author = {Matthias Morzfeld and Xuemin Tu and Ethan Atkins and Alexandre J. Chorin},
journal= {arXiv preprint arXiv:1102.4375},
year = {2015}
}