Forward Sensitivity Approach to Dynamic Data Assimilation

The least squares fit of observations with known error covariance to a strong-constraint dynamical model has been developed through use of the time evolution of sensitivity functions—the derivatives of model output with respect to the elements of control (initial conditions, boundary conditions, and...

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Main Authors: S. Lakshmivarahan, J. M. Lewis
Format: Article
Language:English
Published: Hindawi Limited 2010-01-01
Series:Advances in Meteorology
Online Access:http://dx.doi.org/10.1155/2010/375615
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spelling doaj-b32e8b5d65b147c4b168efe8a50528992020-11-24T23:01:59ZengHindawi LimitedAdvances in Meteorology1687-93091687-93172010-01-01201010.1155/2010/375615375615Forward Sensitivity Approach to Dynamic Data AssimilationS. Lakshmivarahan0J. M. Lewis1School of Computer Sciences, University of Oklahoma, Norman, OK 73072, USAForecast R&D, National Severe Storms Laboratory, Norman, OK 73072, USAThe least squares fit of observations with known error covariance to a strong-constraint dynamical model has been developed through use of the time evolution of sensitivity functions—the derivatives of model output with respect to the elements of control (initial conditions, boundary conditions, and physical/empirical parameters). Model error is assumed to stem from incorrect specification of the control elements. The optimal corrections to control are found through solution to an inverse problem. Duality between this method and the standard 4D-Var assimilation using adjoint equations has been proved. The paper ends with an illustrative example based on a simplified version of turbulent heat transfer at the sea/air interface.http://dx.doi.org/10.1155/2010/375615
collection DOAJ
language English
format Article
sources DOAJ
author S. Lakshmivarahan
J. M. Lewis
spellingShingle S. Lakshmivarahan
J. M. Lewis
Forward Sensitivity Approach to Dynamic Data Assimilation
Advances in Meteorology
author_facet S. Lakshmivarahan
J. M. Lewis
author_sort S. Lakshmivarahan
title Forward Sensitivity Approach to Dynamic Data Assimilation
title_short Forward Sensitivity Approach to Dynamic Data Assimilation
title_full Forward Sensitivity Approach to Dynamic Data Assimilation
title_fullStr Forward Sensitivity Approach to Dynamic Data Assimilation
title_full_unstemmed Forward Sensitivity Approach to Dynamic Data Assimilation
title_sort forward sensitivity approach to dynamic data assimilation
publisher Hindawi Limited
series Advances in Meteorology
issn 1687-9309
1687-9317
publishDate 2010-01-01
description The least squares fit of observations with known error covariance to a strong-constraint dynamical model has been developed through use of the time evolution of sensitivity functions—the derivatives of model output with respect to the elements of control (initial conditions, boundary conditions, and physical/empirical parameters). Model error is assumed to stem from incorrect specification of the control elements. The optimal corrections to control are found through solution to an inverse problem. Duality between this method and the standard 4D-Var assimilation using adjoint equations has been proved. The paper ends with an illustrative example based on a simplified version of turbulent heat transfer at the sea/air interface.
url http://dx.doi.org/10.1155/2010/375615
work_keys_str_mv AT slakshmivarahan forwardsensitivityapproachtodynamicdataassimilation
AT jmlewis forwardsensitivityapproachtodynamicdataassimilation
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