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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-167
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-167
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: development and technical paper 01 Aug 2019

Submitted as: development and technical paper | 01 Aug 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: Example of AWI-CM

Lars Nerger, Qi Tang, and Longjiang Mu Lars Nerger et al.
  • Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany

Abstract. Data assimilation integrates information from observational measurements with numerical models. When used with coupled models of Earth system compartments, e.g. the atmosphere and the ocean, consistent joint states can be estimated. A common approach for data assimilation are ensemble-based methods which use an ensemble of state realizations to estimate the state and its uncertainty. These methods are far more costly to compute than a single coupled model because of the required integration of the ensemble. However, with uncoupled models, the methods also have been shown to exhibit a particularly good scaling behavior. This study discusses an approach to augment a coupled model with data assimilation functionality provided by the Parallel Data Assimilation Framework (PDAF). Using only minimal changes in the codes of the different compartment models, a particularly efficient data assimilation system is generated that utilizes parallelization and in-memory data transfers between the models and the data assimilation functions and hence avoids most of the filter reading and writing and also model restarts during the data assimilation process. The study explains the required modifications of the programs on the example of the coupled atmosphere-sea ice-ocean model AWI-CM. Using the case of the assimilation of oceanic observations shows that the data assimilation leads only small overheads in computing time of about 15 % compared to the model without data assimilation and a very good parallel scalability. The model-agnostic structure of the assimilation software ensures a separation of concerns in that the development of data assimilation methods and be separated from the model application.

Lars Nerger et al.
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Lars Nerger et al.
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Short summary
Data Assimilation combines observations with numerical models to get an improved estimate of the model state. This work discusses the technical aspects of how a coupled model that simulates the ocean and the atmosphere can be augmented by data assimilation functionality provided in generic form by the open-source software PDAF (Parallel Data Assimilation Framework). Demonstrated is that a very efficient program is obtained that can be executed on high-performance computers.
Data Assimilation combines observations with numerical models to get an improved estimate of the...
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