NDLI: Concrete ensemble Kalman filters with rigorous catastrophic filter divergence

Content Provider	PubMed Central
Author	Kelly, David Majda, Andrew J. Tong, Xin T.
Abstract	Understanding ensemble-based data assimilation methods, including their performance when applied to high-dimensional nonlinear models with low ensemble size, is a crucial problem in science and engineering. Catastrophic filter divergence is a well-documented but mechanistically mysterious phenomenon whereby ensemble-state estimates explode to machine infinity despite the true state remaining in a bounded region. We provide breakthrough insight into the phenomenon by proposing a simple forecast model that experiences catastrophic filter divergence under all ensemble-based methods. This is the first instance to our knowledge of a forecast model that plainly and rigorously illustrates that simple mechanisms can lead to such a drastic filter malfunction and thereby sheds light on when catastrophic filter divergence should be expected and how it can be avoided.
Related Links	http://dx.doi.org/10.1073/pnas.1511063112
Ending Page	10594
Page Count	6
Starting Page	10589
File Format	PDF
ISSN	00278424
e-ISSN	10916490
Journal	Proceedings of the National Academy of Sciences of the United States of America
Issue Number	34
Volume Number	112
Language	English
Publisher	National Academy of Sciences
Publisher Date	2015-08-25
Access Restriction	Open
Rights Holder	National Academy of Sciences
Subject Keyword	General Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Multidisciplinary

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Concrete ensemble Kalman filters with rigorous catastrophic filter divergence