2 research outputs found
A scalable space-time domain decomposition approach for solving large-scale nonlinear regularized inverse ill-posed problems in 4D variational data assimilation
We develop innovative algorithms for solving the strong-constraint
formulation of four-dimensional variational data assimilation in large-scale
applications. We present a space-time decomposition approach that employs
domain decomposition along both the spatial and temporal directions in the
overlapping case and involves partitioning of both the solution and the
operators. Starting from the global functional defined on the entire domain, we
obtain a type of regularized local functionals on the set of subdomains
providing the order reduction of both the predictive and the data assimilation
models. We analyze the algorithm convergence and its performance in terms of
reduction of time complexity and algorithmic scalability. The numerical
experiments are carried out on the shallow water equation on the sphere
according to the setup available at the Ocean Synthesis/Reanalysis Directory
provided by Hamburg University.Comment: Received: 10 March 2020 / Revised: 29 November 2021 / Accepted: 7
March 202