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Advances in Meteorology
Volume 2010 (2010), Article ID 375615, 12 pages
http://dx.doi.org/10.1155/2010/375615
Research Article

Forward Sensitivity Approach to Dynamic Data Assimilation

1School of Computer Sciences, University of Oklahoma, Norman, OK 73072, USA
2Forecast R&D, National Severe Storms Laboratory, Norman, OK 73072, USA
3Division of Atmospheric Sciences, Desert Research Institute, Reno, NV 89512, USA

Received 3 December 2009; Accepted 15 February 2010

Academic Editor: Zhaoxia Pu

Copyright © 2010 S. Lakshmivarahan and J. M. Lewis. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.