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- W2950996645 abstract "Quantifying uncertainty and error bounds is a keyoutstanding challenge in ocean state estimation and climateresearch. It is particularly difficult due to the largedimensionality of this nonlinear estimation problem and the numberof uncertain variables involved. The Estimating the Circulationand Climate of the Oceans (ECCO) consortium has developed ascalable system for dynamically consistent estimation of globaltime-evolving ocean state by optimal combination of ocean generalcirculation model (GCM) with diverse ocean observations. Theestimation system is based on the solution of anunconstrained least-squares optimization problem formulated withthe method of Lagrange multipliers for fitting the dynamical oceanmodel to observations. The dynamical consistency requirement ofocean state estimation necessitates this approach over sequentialdata assimilation and reanalysis smoothing techniques. In addition,it is computationally advantageous because calculation and storageof large covariance matrices is not required. However, this is alsoa drawback of the adjoint method, which lacks a native formalismfor error propagation and quantification of assimilateduncertainty. The objective of this dissertation is to resolve thatlimitation by developing a feasible computational methodology foruncertainty analysis in dynamically consistent state estimation,applicable to the large dimensionality of global ocean models.Hessian (second derivative-based) methodology is developed forUncertainty Quantification (UQ) in large-scale ocean stateestimation, extending the gradient-based adjoint method to employthe second order geometry information of the model-data misfitfunction in a high-dimensional control space. Large errorcovariance matrices are evaluated by inverting the Hessian matrixwith the developed scalable matrix-free numerical linear algebraalgorithms. Hessian-vector product and Jacobian derivative codes ofthe MIT general circulation model (MITgcm) are generated by meansof algorithmic differentiation (AD). Computational complexity ofthe Hessian code is reduced by tangent linear differentiation ofthe adjoint code, which preserves the speedup of adjointcheckpointing schemes in the second derivative calculation. ALanczos algorithm is applied for extracting the leading rankeigenvectors and eigenvalues of the Hessian matrix. Theeigenvectors represent the constrained uncertainty patterns. Theinverse eigenvalues are the corresponding uncertainties. Thedimensionality of UQ calculations is reduced by eliminating theuncertainty null-space unconstrained by the supplied observations.Inverse and forward uncertainty propagation schemes are designedfor assimilating observation and control variable uncertainties,and for projecting these uncertainties onto oceanographic targetquantities. Two versions of these schemes are developed: oneevaluates reduction of prior uncertainties, while another does notrequire prior assumptions. The analysis of uncertainty propagationin the ocean model is…" @default.
- W2950996645 created "2019-06-27" @default.
- W2950996645 creator A5007411474 @default.
- W2950996645 date "2013-01-01" @default.
- W2950996645 modified "2023-09-24" @default.
- W2950996645 title "Uncertainty Quantification in ocean stateestimation; UQ in ocean state estimation" @default.
- W2950996645 hasPublicationYear "2013" @default.
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