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PDAF - the Parallel Data Assimilation Framework

PDAF is developed,
hosted and maintained at the
Computing Center of the Alfred Wegener Institute.
News
Feb 12, 2012
Release of Version 1.8 (release notes)
Jan 22, 2012
Introducing a new filter algorithm: The Error Subspace Transform Kalman Filter
Sep 16, 2011
Release of Version 1.7 (release notes)

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, in particular ensemble-based Kalman filters like LETKF and LSEIK. It 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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