Journal
JOURNAL OF COMPUTATIONAL PHYSICS
Volume 231, Issue 4, Pages 2049-2066Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jcp.2011.11.022
Keywords
Data assimilation; Implicit sampling; Particle filters; Sequential Monte Carlo
Funding
- Office of Science, Computational and Technology Research, US Department of Energy [DE-AC02-05CH11231]
- National Science Foundation [DMS-0705910, OCE-0934298]
- Division Of Ocean Sciences
- Directorate For Geosciences [0934298] Funding Source: National Science Foundation
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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.
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