A time-parallel approach to strong-constraint four-dimensional variational data assimilation
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 sol...
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| Veröffentlicht in: | Journal of computational physics 2016-05, Vol.313, p.583-593 |
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| container_title | Journal of computational physics |
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| creator | Rao, Vishwas Sandu, Adrian |
| description | 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. |
| doi_str_mv | 10.1016/j.jcp.2016.02.040 |
| format | Article |
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| ispartof | Journal of computational physics, 2016-05, Vol.313, p.583-593 |
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| source | Access via ScienceDirect (Elsevier) |
| subjects | Adjoint sensitivity analysis Assimilation Augmented Lagrangian Boundaries Computation Continuity equation Data assimilation Mathematical models Serials Shallow water Time parallel variational data assimilation Variational data assimilation |
| title | A time-parallel approach to strong-constraint four-dimensional variational data assimilation |
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