A non-Gaussian analysis scheme using rank histograms for ensemble data assimilation
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...
Published in: | Nonlinear Processes in Geophysics |
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ftdoajarticles:oai:doaj.org/article:e64b69a2e7014f7d82af187b93b4935a 2023-05-15T17:33:31+02:00 A non-Gaussian analysis scheme using rank histograms for ensemble data assimilation S. Metref E. Cosme C. Snyder P. Brasseur 2014-08-01T00:00:00Z https://doi.org/10.5194/npg-21-869-2014 https://doaj.org/article/e64b69a2e7014f7d82af187b93b4935a EN eng Copernicus Publications http://www.nonlin-processes-geophys.net/21/869/2014/npg-21-869-2014.pdf https://doaj.org/toc/1023-5809 https://doaj.org/toc/1607-7946 1023-5809 1607-7946 doi:10.5194/npg-21-869-2014 https://doaj.org/article/e64b69a2e7014f7d82af187b93b4935a Nonlinear Processes in Geophysics, Vol 21, Iss 4, Pp 869-885 (2014) Science Q Physics QC1-999 Geophysics. Cosmic physics QC801-809 article 2014 ftdoajarticles https://doi.org/10.5194/npg-21-869-2014 2022-12-31T05:24:08Z 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. Article in Journal/Newspaper North Atlantic Directory of Open Access Journals: DOAJ Articles Nonlinear Processes in Geophysics 21 4 869 885 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Science Q Physics QC1-999 Geophysics. Cosmic physics QC801-809 |
spellingShingle |
Science Q Physics QC1-999 Geophysics. Cosmic physics QC801-809 S. Metref E. Cosme C. Snyder P. Brasseur A non-Gaussian analysis scheme using rank histograms for ensemble data assimilation |
topic_facet |
Science Q Physics QC1-999 Geophysics. Cosmic physics QC801-809 |
description |
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. |
format |
Article in Journal/Newspaper |
author |
S. Metref E. Cosme C. Snyder P. Brasseur |
author_facet |
S. Metref E. Cosme C. Snyder P. Brasseur |
author_sort |
S. Metref |
title |
A non-Gaussian analysis scheme using rank histograms for ensemble data assimilation |
title_short |
A non-Gaussian analysis scheme using rank histograms for ensemble data assimilation |
title_full |
A non-Gaussian analysis scheme using rank histograms for ensemble data assimilation |
title_fullStr |
A non-Gaussian analysis scheme using rank histograms for ensemble data assimilation |
title_full_unstemmed |
A non-Gaussian analysis scheme using rank histograms for ensemble data assimilation |
title_sort |
non-gaussian analysis scheme using rank histograms for ensemble data assimilation |
publisher |
Copernicus Publications |
publishDate |
2014 |
url |
https://doi.org/10.5194/npg-21-869-2014 https://doaj.org/article/e64b69a2e7014f7d82af187b93b4935a |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
Nonlinear Processes in Geophysics, Vol 21, Iss 4, Pp 869-885 (2014) |
op_relation |
http://www.nonlin-processes-geophys.net/21/869/2014/npg-21-869-2014.pdf https://doaj.org/toc/1023-5809 https://doaj.org/toc/1607-7946 1023-5809 1607-7946 doi:10.5194/npg-21-869-2014 https://doaj.org/article/e64b69a2e7014f7d82af187b93b4935a |
op_doi |
https://doi.org/10.5194/npg-21-869-2014 |
container_title |
Nonlinear Processes in Geophysics |
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21 |
container_issue |
4 |
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869 |
op_container_end_page |
885 |
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1766132046950825984 |