Linear versus Nonlinear Filtering with Scale-Selective Corrections for Balanced Dynamics in a Simple Atmospheric Model

Handle URI:
http://hdl.handle.net/10754/552779
Title:
Linear versus Nonlinear Filtering with Scale-Selective Corrections for Balanced Dynamics in a Simple Atmospheric Model
Authors:
Subramanian, Aneesh C.; Hoteit, Ibrahim ( 0000-0002-3751-4393 ) ; Cornuelle, Bruce; Miller, Arthur J.; Song, Hajoon
Abstract:
This paper investigates the role of the linear analysis step of the ensemble Kalman filters (EnKF) in disrupting the balanced dynamics in a simple atmospheric model and compares it to a fully nonlinear particle-based filter (PF). The filters have a very similar forecast step but the analysis step of the PF solves the full Bayesian filtering problem while the EnKF analysis only applies to Gaussian distributions. The EnKF is compared to two flavors of the particle filter with different sampling strategies, the sequential importance resampling filter (SIRF) and the sequential kernel resampling filter (SKRF). The model admits a chaotic vortical mode coupled to a comparatively fast gravity wave mode. It can also be configured either to evolve on a so-called slow manifold, where the fast motion is suppressed, or such that the fast-varying variables are diagnosed from the slow-varying variables as slaved modes. Identical twin experiments show that EnKF and PF capture the variables on the slow manifold well as the dynamics is very stable. PFs, especially the SKRF, capture slaved modes better than the EnKF, implying that a full Bayesian analysis estimates the nonlinear model variables better. The PFs perform significantly better in the fully coupled nonlinear model where fast and slow variables modulate each other. This suggests that the analysis step in the PFs maintains the balance in both variables much better than the EnKF. It is also shown that increasing the ensemble size generally improves the performance of the PFs but has less impact on the EnKF after a sufficient number of members have been used.
KAUST Department:
Physical Sciences and Engineering (PSE) Division
Citation:
Linear versus Nonlinear Filtering with Scale-Selective Corrections for Balanced Dynamics in a Simple Atmospheric Model 2012, 69 (11):3405 Journal of the Atmospheric Sciences
Publisher:
American Meteorological Society
Journal:
Journal of the Atmospheric Sciences
Issue Date:
Nov-2012
DOI:
10.1175/JAS-D-11-0332.1
Type:
Article
ISSN:
0022-4928; 1520-0469
Additional Links:
http://journals.ametsoc.org/doi/abs/10.1175/JAS-D-11-0332.1
Appears in Collections:
Articles; Physical Sciences and Engineering (PSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorSubramanian, Aneesh C.en
dc.contributor.authorHoteit, Ibrahimen
dc.contributor.authorCornuelle, Bruceen
dc.contributor.authorMiller, Arthur J.en
dc.contributor.authorSong, Hajoonen
dc.date.accessioned2015-05-14T07:06:24Zen
dc.date.available2015-05-14T07:06:24Zen
dc.date.issued2012-11en
dc.identifier.citationLinear versus Nonlinear Filtering with Scale-Selective Corrections for Balanced Dynamics in a Simple Atmospheric Model 2012, 69 (11):3405 Journal of the Atmospheric Sciencesen
dc.identifier.issn0022-4928en
dc.identifier.issn1520-0469en
dc.identifier.doi10.1175/JAS-D-11-0332.1en
dc.identifier.urihttp://hdl.handle.net/10754/552779en
dc.description.abstractThis paper investigates the role of the linear analysis step of the ensemble Kalman filters (EnKF) in disrupting the balanced dynamics in a simple atmospheric model and compares it to a fully nonlinear particle-based filter (PF). The filters have a very similar forecast step but the analysis step of the PF solves the full Bayesian filtering problem while the EnKF analysis only applies to Gaussian distributions. The EnKF is compared to two flavors of the particle filter with different sampling strategies, the sequential importance resampling filter (SIRF) and the sequential kernel resampling filter (SKRF). The model admits a chaotic vortical mode coupled to a comparatively fast gravity wave mode. It can also be configured either to evolve on a so-called slow manifold, where the fast motion is suppressed, or such that the fast-varying variables are diagnosed from the slow-varying variables as slaved modes. Identical twin experiments show that EnKF and PF capture the variables on the slow manifold well as the dynamics is very stable. PFs, especially the SKRF, capture slaved modes better than the EnKF, implying that a full Bayesian analysis estimates the nonlinear model variables better. The PFs perform significantly better in the fully coupled nonlinear model where fast and slow variables modulate each other. This suggests that the analysis step in the PFs maintains the balance in both variables much better than the EnKF. It is also shown that increasing the ensemble size generally improves the performance of the PFs but has less impact on the EnKF after a sufficient number of members have been used.en
dc.publisherAmerican Meteorological Societyen
dc.relation.urlhttp://journals.ametsoc.org/doi/abs/10.1175/JAS-D-11-0332.1en
dc.rights© Copyright 2012 American Meteorological Society (AMS). Permission to use figures, tables, and brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act September 2010 Page 2 or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC §108, as revised by P.L. 94-553) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a web site or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. Additional details are provided in the AMS Copyright Policy, available on the AMS Web site located at (http://www.ametsoc.org/) or from the AMS at 617-227-2425 or copyrights@ametsoc.org.en
dc.subjectNonlinear dynamicsen
dc.subjectStabilityen
dc.subjectBayesian methodsen
dc.subjectFiltering techniquesen
dc.subjectKalman filtersen
dc.subjectData assimilationen
dc.titleLinear versus Nonlinear Filtering with Scale-Selective Corrections for Balanced Dynamics in a Simple Atmospheric Modelen
dc.typeArticleen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.identifier.journalJournal of the Atmospheric Sciencesen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionScripps Institution of Oceanography, University of California, San Diego, San Diego, Californiaen
dc.contributor.institutionUniversity of California, Santa Cruz, Santa Cruz, Californiaen
kaust.authorHoteit, Ibrahimen
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