Observation Quality Control with a Robust Ensemble Kalman Filter

Handle URI:
http://hdl.handle.net/10754/552743
Title:
Observation Quality Control with a Robust Ensemble Kalman Filter
Authors:
Roh, Soojin; Genton, Marc G. ( 0000-0001-6467-2998 ) ; Jun, Mikyoung; Szunyogh, Istvan; Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Abstract:
Current ensemble-based Kalman filter (EnKF) algorithms are not robust to gross observation errors caused by technical or human errors during the data collection process. In this paper, the authors consider two types of gross observational errors, additive statistical outliers and innovation outliers, and introduce a method to make EnKF robust to gross observation errors. Using both a one-dimensional linear system of dynamics and a 40-variable Lorenz model, the performance of the proposed robust ensemble Kalman filter (REnKF) was tested and it was found that the new approach greatly improves the performance of the filter in the presence of gross observation errors and leads to only a modest loss of accuracy with clean, outlier-free, observations.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Observation Quality Control with a Robust Ensemble Kalman Filter 2013, 141 (12):4414 Monthly Weather Review
Journal:
Monthly Weather Review
Issue Date:
Dec-2013
DOI:
10.1175/MWR-D-13-00091.1
Type:
Article
ISSN:
0027-0644; 1520-0493
Additional Links:
http://journals.ametsoc.org/doi/abs/10.1175/MWR-D-13-00091.1
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorRoh, Soojinen
dc.contributor.authorGenton, Marc G.en
dc.contributor.authorJun, Mikyoungen
dc.contributor.authorSzunyogh, Istvanen
dc.contributor.authorHoteit, Ibrahimen
dc.date.accessioned2015-05-14T06:33:59Zen
dc.date.available2015-05-14T06:33:59Zen
dc.date.issued2013-12en
dc.identifier.citationObservation Quality Control with a Robust Ensemble Kalman Filter 2013, 141 (12):4414 Monthly Weather Reviewen
dc.identifier.issn0027-0644en
dc.identifier.issn1520-0493en
dc.identifier.doi10.1175/MWR-D-13-00091.1en
dc.identifier.urihttp://hdl.handle.net/10754/552743en
dc.description.abstractCurrent ensemble-based Kalman filter (EnKF) algorithms are not robust to gross observation errors caused by technical or human errors during the data collection process. In this paper, the authors consider two types of gross observational errors, additive statistical outliers and innovation outliers, and introduce a method to make EnKF robust to gross observation errors. Using both a one-dimensional linear system of dynamics and a 40-variable Lorenz model, the performance of the proposed robust ensemble Kalman filter (REnKF) was tested and it was found that the new approach greatly improves the performance of the filter in the presence of gross observation errors and leads to only a modest loss of accuracy with clean, outlier-free, observations.en
dc.relation.urlhttp://journals.ametsoc.org/doi/abs/10.1175/MWR-D-13-00091.1en
dc.rights© Copyright 2013 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.subjectEnsemblesen
dc.subjectStatisticsen
dc.subjectKalman filtersen
dc.subjectData quality controlen
dc.titleObservation Quality Control with a Robust Ensemble Kalman Filteren
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalMonthly Weather Reviewen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionDepartment of Statistics, Texas A&M University, College Station, Texasen
dc.contributor.institutionDepartment of Atmospheric Sciences, Texas A&M University, College Station, Texasen
kaust.authorGenton, Marc G.en
kaust.authorHoteit, Ibrahimen
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