Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
519577 | Journal of Computational Physics | 2016 | 11 Pages |
Abstract
A parallel-in-time algorithm based on an augmented Lagrangian approach is proposed to solve four-dimensional variational (4D-Var) data assimilation problems. The assimilation window is divided into multiple sub-intervals that allows parallelization of cost function and gradient computations. The solutions to the continuity equations across interval boundaries are added as constraints. The augmented Lagrangian approach leads to a different formulation of the variational data assimilation problem than the weakly constrained 4D-Var. A combination of serial and parallel 4D-Vars to increase performance is also explored. The methodology is illustrated on data assimilation problems involving the Lorenz-96 and the shallow water models.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Vishwas Rao, Adrian Sandu,