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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
May 6, 2015
New paper "On serial observation processing in localized ensemble Kalman filters" published the Monthly Weather Review (see publications)
February 28, 2015
Release of Version 1.11.1 (release notes)
(Bug fix release for compilation with Cray compilers)
December 22, 2014
Release of Version 1.11 (release notes)
(New: Improved screen output, OpenMP-parallelization of local filters, improved ensemble communication)
October 4, 2013
Release of Version 1.10 (release notes)
(New: Simplified coupling method)

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. PDAF 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 algorithmic developments 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 additional assimilation methods or interface routines for different numerical models.

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Last modified 4 months ago Last modified on 05/10/15 10:43:49