A Hessian-based method for uncertainty quantification in global ocean state estimation.
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Kalmikov, Alexander G.
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Derivative-based methods are developed for uncertainty quantification (UQ) in largescale ocean state estimation. The estimation system is based on the adjoint method for solving a least-squares optimization problem, whereby the state-of-the-art MIT general circulation model (MITgcm) is fit to observations. The UQ framework is applied to quantify Drake Passage transport uncertainties in a global idealized barotropic configuration of the MITgcm. Large error covariance matrices are evaluated by inverting the Hessian of the misfit function using matrix-free numerical linear algebra algorithms. The covariances are projected onto target output quantities of the model (here Drake Passage transport) by Jacobian transformations. First and second derivative codes of the MITgcm are generated by means of algorithmic differentiation (AD). Transpose of the chain rule product of Jacobians of elementary forward model operations implements a computationally efficient adjoint code. Computationa.....
JournalSIAM Journal of Scientific Computing
Page Rangepp. S267–S295
Sustainable Development Goals (SDG)14.A
Best Practice TypeManual (incl. handbook, guide, cookbook etc)
CitationKalmikov, A.G. and Heimbach, P. (2014) A Hessian-based method for uncertainty quantificiation in gllobal ocean state estimation. SIAM Journal of Scientific Computing, 36(5), pp. S267–S295. DOI: 10.1137/130925311
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