Identification

Title

Optimized localization and hybridization to filter ensemble-based covariances

Abstract

Localization and hybridization are two methods used in ensemble data assimilation to improve the accuracy of sample covariances. It is shown in this paper that it is beneficial to consider them jointly in the framework of linear filtering of sample covariances. Following previous work on localization, an objective method is provided to optimize both localization and hybridization coefficients simultaneously. Theoretical and experimental evidence shows that if optimal weights are used, localized-hybridized sample covariances are always more accurate than their localized-only counterparts, whatever the static covariance matrix specified for the hybridization. Experimental results obtained using a 1000-member ensemble as a reference show that the method developed in this paper can efficiently provide localization and hybridization coefficients consistent with the variable, vertical level, and ensemble size. Spatially heterogeneous optimization is shown to improve the accuracy of the filtered covariances, and consideration of both vertical and horizontal covariances is proven to have an impact on the hybridization coefficients.

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document

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http://n2t.net/ark:/85065/d7ws8vgs

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Dataset language

eng

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Classification of spatial data and services

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geoscientificInformation

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title

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reference date

date type

publication

effective date

2016-01-01T00:00:00Z

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publication

effective date

2015-10-01T00:00:00Z

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Copyright 2015 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 or that satisfies the conditions specified in Section 108 of the U.S. Copyright Law (17 USC, as revised by P.L. 94-553) does not require the Society's permission. Republication, systematic reproduction, posting in electronic form on servers, or other uses of this material, except as exempted by the above statements, requires written permission or license from the AMS. Additional details are provided in the AMS Copyright Policies, available from the AMS at 617-227-2425 or amspubs@ametsoc.org. Permission to place a copy of this work on this server has been provided by the AMS. The AMS does not guarantee that the copy provided here is an accurate copy of the published work.

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None

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OpenSky Support

organisation name

UCAR/NCAR - Library

full postal address

PO Box 3000

Boulder

80307-3000

email address

opensky@ucar.edu

web address

http://opensky.ucar.edu/

name: homepage

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pointOfContact

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