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The Parallel Data Assimilation Framework - PDAF - is a software environment for ensemble data assimilation. PDAF simplifies the implementation of the data assimilation system with existing numerical models. With this, users can obtain a data assimilation system with less work and can focus on applying data assimilation.
PDAF provides fully implemented and optimized data assimilation algorithms, like LETKF and LSEIK and allows users to easily test different assimilation algorithms and observations. The Framework is optimized for the application with large-scale models that usually run on big parallel computers and is applicable for operational applications. However, it is also well suited for smaller models and even toy models.
PDAF provides a standardized interface that separates the numerical model from the assimilation routines. This allows to perform the further 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 immediately applied with existing implementations. The test suite of PDAF provides small models for easy testing of algorithmic developments and for teaching data assimilation.
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]