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Type: Journal article
Title: A shadowing-based inflation scheme for ensemble data assimilation
Author: Bellsky, T.
Mitchell, L.
Citation: Physica D: Nonlinear Phenomena, 2018; 380-381:1-7
Publisher: Elsevier
Issue Date: 2018
ISSN: 0167-2789
Statement of
Thomas Bellsky, Lewis Mitchell
Abstract: Artificial ensemble inflation is a common technique in ensemble data assimilation, whereby the ensemble covariance is periodically increased in order to prevent deviation of the ensemble from the observations and possible ensemble collapse. This manuscript introduces a new form of covariance inflation for ensemble data assimilation based upon shadowing ideas from dynamical systems theory. We present results from a low order nonlinear chaotic system that support using shadowing inflation, demonstrating that shadowing inflation is more robust to parameter tuning than standard multiplicative covariance inflation, often leading to longer forecast shadowing times.
Keywords: Data assimilation; shadowing; covariance inflation; chaotic dynamics; ensemble methods
Rights: © 2018 Elsevier B.V. All rights reserved.
DOI: 10.1016/j.physd.2018.05.002
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