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- W583624554 abstract "Ensemble filter algorithms can be implemented in a generic waysuch that they can be applied with various models with only a minimum amount of recoding. This is possible due to the fact that ensemble filters can operateon abstract state vectors and require only limited information aboutthe numerical model and the observational data used for a data assimilation application.To build an assimilation system, the analysis step of a filteralgorithm needs to be connected to the numerical model. Furthermore,ensemble integrations have to be enabled. The Parallel Data Assimilation Framework PDAF has been developed toprovide these features: It is a generic framework that allows toextend a numerical model with a filter to build an ensemble dataassimilation system with minimal changes to the model code. PDAF also provides a selection of common ensembleKalman filter algorithms. As the computational cost of ensemble dataassimilation is a multiple of that of a pure forward model, theframework and the filter algorithms are parallelized and supportparallelized models. Thus, data assimilation with high-dimensionalnumerical models is feasible. PDAF is coded in Fortran andavailable as free software (http://pdaf.awi.de). We discuss thefeatures of PDAF and the parallel computing performance of dataassimilation systems based on PDAF on the example of data assimilationwith the finite element ocean model FEOM." @default.
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- W583624554 date "2012-04-26" @default.
- W583624554 modified "2023-09-24" @default.
- W583624554 title "The Parallel Data Assimilation Framework PDAF - a flexible softwareframework for ensemble data assimilation" @default.
- W583624554 hasPublicationYear "2012" @default.
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