A random map implementation of implicit filters

作者：Morzfeld Matthias^*; Tu Xuemin; Atkins Ethan; Chorin Alexandre J

来源：Journal of Computational Physics, 2012, 231(4): 2049-2066.

DOI：10.1016/j.jcp.2011.11.022

• Data assimilation
• Implicit sampling
• Particle filters
• Sequential Monte Carlo

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.

• 出版日期2012-2-20