Identification

Title

Reducing correlation sampling error in ensemble Kalman filter data assimilation

Abstract

Ensemble Kalman filters are widely used for data assimilation in large geophysical models. Good results with affordable ensemble sizes require enhancements to the basic algorithms to deal with insufficient ensemble variance and spurious ensemble correlations between observations and state variables. These challenges are often dealt with by using inflation and localization algorithms. A new method for understanding and reducing some ensemble filter errors is introduced and tested. The method assumes that sampling error due to small ensemble size is the primary source of error. Sampling error in the ensemble correlations between observations and state variables is reduced by estimating the distribution of correlations as part of the ensemble filter algorithm. This correlation error reduction (CER) algorithm can produce high-quality ensemble assimilations in low-order models without using any a priori localization like a specified localization function. The method is also applied in an observing system simulation experiment with a very coarse resolution dry atmospheric general circulation model. This demonstrates that the algorithm provides insight into the need for localization in large geophysical applications, suggesting that sampling error may be a primary cause in some cases.

Resource type

document

Resource locator

Unique resource identifier

code

http://n2t.net/ark:/85065/d7028t4k

codeSpace

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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date type

publication

effective date

2016-03-01T00:00:00Z

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Copyright 2016 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

Responsible organisations

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contact position

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

responsible party role

pointOfContact

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Metadata point of contact