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| Titel |
A non-Gaussian analysis scheme using rank histograms for ensemble data assimilation |
| VerfasserIn |
S. Metref, E. Cosme, C. Snyder, P. Brasseur |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 21, no. 4 ; Nr. 21, no. 4 (2014-08-25), S.869-885 |
| Datensatznummer |
250120935
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| Publikation (Nr.) |
 copernicus.org/npg-21-869-2014.pdf |
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| Zusammenfassung |
| One challenge of geophysical data assimilation is to address the issue of
non-Gaussianities in the distributions of the physical variables ensuing, in
many cases, from nonlinear dynamical models. Non-Gaussian ensemble analysis
methods fall into two categories, those remapping the ensemble particles by
approximating the best linear unbiased estimate, for example, the ensemble Kalman
filter (EnKF), and those resampling the particles by directly applying Bayes'
rule, like particle filters. In this article, it is suggested that the most
common remapping methods can only handle weakly non-Gaussian distributions,
while the others suffer from sampling issues. In between those two
categories, a new remapping method directly applying Bayes' rule, the
multivariate rank histogram filter (MRHF), is introduced as an extension of
the rank histogram filter (RHF) first introduced by Anderson (2010). Its
performance is evaluated and compared with several data assimilation methods,
on different levels of non-Gaussianity with the Lorenz 63 model. The method's
behavior is then illustrated on a simple density estimation problem using
ensemble simulations from a coupled physical–biogeochemical model of the
North Atlantic ocean. The MRHF performs well with low-dimensional systems in
strongly non-Gaussian regimes. |
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