Part B: Sensitivity Analysis in Nonlinear Variational Data Assimilation: Theoretical Aspects and Applications
- Authors: Dacian N. Daescu1, Ionel M. Navon2
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View Affiliations Hide Affiliations1 Portland State University, Portland, Oregon, USA 2 Florida State University, Tallahassee, Florida, USA
- Source: Advanced Numerical Methods for Complex Environmental Models: Needs and Availability , pp 276-300
- Publication Date: December 2013
- Language: English
Part B: Sensitivity Analysis in Nonlinear Variational Data Assimilation: Theoretical Aspects and Applications, Page 1 of 1
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This chapter presents the mathematical framework to evaluate the sensitivity of a model forecast aspect to the input parameters of a nonlinear four-dimensional variational data assimilation system (4D-Var DAS): observations, prior state (background) estimate, and the error covariance specification. A fundamental relationship is established between the forecast sensitivity with respect to the information vector and the sensitivity with respect to the DAS representation of the information error covariance. Adjoint modeling is used to obtain first- and second-order derivative information and a reduced-order approach is formulated to alleviate the computational cost associated with the sensitivity estimation. Numerical results from idealized 4D-Var experiments performed with a global shallow water model are used to illustrate the theoretical concepts.
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