Identification

Title

An Ensemble approach for the estimation of observational error illustrated for a nominal 1° global ocean model

Abstract

Least squares algorithms for data assimilation require estimates of both background error covariances and observational error covariances. The specification of these errors is an essential part of designing an assimilation system; the relative sizes of these uncertainties determine the extent to which the state variables are drawn toward the observational information. Observational error covariances are typically computed as the sum of measurement/instrumental errors and "representativeness error." In a coarse-resolution ocean general circulation model the errors of representation are the dominant contribution to observational error covariance over large portions of the globe, and the size of these errors will vary by the type of observation and the geographic region. They may also vary from model to model. A straightforward approach for estimating model-dependent, spatially varying observational error variances that are suitable for least squares ocean data assimilating systems is presented here. The author proposes an ensemble-based estimator of the true observational error variance and outlines the assumptions necessary for the estimator to be unbiased. The author also presents the variance (or uncertainty) associated with the estimator under certain conditions. The analytic expressions for the expected value and variance of the estimator are validated with a simple autoregressive model and illustrated for the nominal 1° resolution POP2 global ocean general circulation model.

Resource type

document

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Unique resource identifier

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http://n2t.net/ark:/85065/d7pk0hr8

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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publication

effective date

2016-05-01T00:00:00Z

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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

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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

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pointOfContact

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