Identification

Title

Data assimilation: A fully nonlinear approach to ensemble formation using indistinguishable states

Abstract

Operational forecasting with simulation models involves the melding of observations and model dynamics to determine a set of initial conditions for each forecast. The Kalman filter (KF) provides the optimal closed-form solution to a general linear stochastic (perfect model) case, while the target of the problem has not even been defined in the case of imperfect models. Data assimilation in a nonlinear, perfect-model scenario is considered. It is shown that a new fully nonlinear approach based upon the indistinguishable states (IS) systematically outperforms the ensemble Kalman filter (EnKF). The IS provides an ensemble of initial conditions, consistent with (i) the model dynamics, (ii) the observational noise model, and (iii) the particular observations over a window. It is argued that this is the relevant limit to consider in data assimilation, when the desire is to place high probability density in the vicinity of the target state. The advantages of the IS approach come in part from its ability to provide attractor-balanced ensembles near any attracting manifold the system may evolve on. The use of an EnKF, provides a computationally cheaper alternative that place points in the general vicinity of the target. A low (i.e., 2) dimensional example is used to provide easily visualized evidence for these claims, which are then tested in a higher (i.e., 12) dimensional system. Inasmuch as the IS approach is shown to outperform the EnKF systematically in these perfect-model experiments, it provides an interesting alternative approach when informative ensembles are desired.

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document

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http://n2t.net/ark:/85065/d7zw1mff

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eng

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geoscientificInformation

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title

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publication

effective date

2016-01-01T00:00:00Z

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publication

effective date

2011-07-01T00:00:00Z

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Copyright 2011 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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