A Time-parallel Approach to Strong-constraint Four-dimensional Variational Data Assimilation
arXiv:1505.04515 · doi:10.1016/j.jcp.2016.02.040
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 to parallelize cost function and gradient computations. Solution 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 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 with Lorenz-96 and the shallow water models.
22 Pages
References in corpus (3)
Cited by in corpus (4)
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