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The Parallel Data Assimilation Framework - PDAF - is a software framework that simplifies the implementation of data assimilation systems using existing numerical models. Reducing the work required for the implementation will enable users to obtain a data assimilation system with less work. Thus, they can focus on applying data assimilation, rather than implementing it. PDAF provides a well-defined interface that separates the model from the assimilation routines. This allows to continue the development of the assimilation methods and the model independently. New developments on the algorithmic side can be readily made available through the interface such that they can be tested immediately with existing implementations.
PDAF provides fully implemented and optimized data assimilation algorithms. The Framework is optimized for the application with large-scale models that usually run on big parallel computers. However, it is also well suited for smaller models and even toy models.
PDAF is an open-source project. Its functionality will be further extended by input from research projects. In addition, users are welcome to contribute to the further enhancement of PDAF, e.g. by contributing further assimilation methods or interface routines for different numerical models.
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* [FeaturesofPdaf Features and requirements of PDAF]
* [GeneralImplementationConcept Implementation Concept of PDAF]
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* Implementation Guides
* [wiki:ImplementationGuide Implementation Guide for the online assimilation system]
* [wiki:OfflineImplementationGuide Implementation Guide for the offline assimilation program]
* [PublicationsandPresentations Publications and Presentations involving and about PDAF]