Rao, V.Sandu, Adrian2017-03-062017-03-062016-05-150021-9991http://hdl.handle.net/10919/75266A 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.583 - 593 (11) page(s)application/pdfenIn CopyrightTechnologyComputer Science, Interdisciplinary ApplicationsPhysics, MathematicalComputer SciencePhysicsVariational data assimilationTime parallel variational data assimilationAdjoint sensitivity analysisAugmented LagrangianINFERENCE PROBLEMSOPTIMIZATIONADJOINTMODELSINTEGRATIONFRAMEWORKArticle - Refereedhttps://doi.org/10.1016/j.jcp.2016.02.040313