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arXiv · 1102.4375

A random map implementation of implicit filters

Matthias Morzfeld·Xuemin Tu·Ethan Atkins·Alexandre J. Chorin
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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.

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Matthias Morzfeld, Xuemin Tu, Ethan Atkins, Alexandre J. Chorin. 2011-02-22. A random map implementation of implicit filters. https://doi.org/10.1016/j.jcp.2011.11.022

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