Portland State University. Department of Mathematics and Statistics
Date of Publication
Doctor of Philosophy (Ph.D.) in Mathematical Sciences
Mathematics and Statistics
Algorithms, Numerical analysis, Error analysis (Mathematics)
1 online resource (x, 110 pages)
Four-dimensional variational data assimilation (4D-Var) provides an estimate to the state of a dynamical system through the minimization of a cost functional that measures the distance to a prior state (background) estimate and observations over a time window. The analysis fit to each information input component is determined by the specification of the error covariance matrices in the data assimilation system (DAS). Weak-constraint 4D-Var (w4D-Var) provides a theoretical framework to account for modeling errors in the analysis scheme. In addition to the specification of the background error covariance matrix, the w4D-Var formulation requires information on the model error statistics and specification of the model error covariance. Up to now, the increased computational cost associated with w4D-Var has prevented its practical implementation. Various simplifications to reduce the computational burden have been considered, including writing the model error covariance as a scalar multiple of the background error covariance and modeling the model error.
In this thesis, the main objective is the development of computationally feasible techniques for the improved representation of the model error statistics in a data assimilation system. Three new approaches are considered.
- A Monte Carlo method that uses an ensemble of w4D-Var systems to obtain flow-dependent estimates to the model error statistics.
- The evaluation of statistical diagnostic equations involving observation residuals to estimate the model error covariance matrix.
- An adaptive tuning procedure based on the sensitivity of a short-range forecast error measure to the model error DAS parametrization.
The validity and benefits of these approaches are shown in two stages of numerical experiments. A proof-of-concept is shown using the Lorenz multi-scale model and the shallow water equations for a one-dimensional domain. The results show the potential of these methodologies to produce improved state estimates, as compared to other approaches in data assimilation. It is expected that the techniques presented will find an extended range of applications to assess and improve the performance of a w4D-Var system.
Shaw, Jeremy A., "Computational Algorithms for Improved Representation of the Model Error Covariance in Weak-Constraint 4D-Var" (2017). Dissertations and Theses. Paper 3473.