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PDAF - the Parallel Data Assimilation Framework

PDAF is developed,
hosted and maintained by the
Data Assimilation Team in the Computing Center of the Alfred Wegener Institute.
ISDA-Online
We co-organize the International Symposium on Data Assimilation - Online. For more information see the ISDA-Online website at University of Vienna
News
August 21, 2024
Webinar on HGS-PDAF We will showcase the new assimilation system HGS-PDAF which add data assimilation with PDAF to the model Hydrogeosphere in a webinar hosted by Aquanty Inc.
April 29, 2024
Release of Version 2.2.1 - with performance improvements for local filter algorimths; adding a 2D+1D factorized localization; some bug-fixes in OMI release notes
April 15-19, 2024
PDAF at the EGU General Assembly 2024 There will be several presentations of research in which PDAF is used. In addition, there will be a short course on data assimilaation on Friday.
March 8, 2024
Release of Version 2.2 - adding e.g. a non-isotropic localization, exclusion of potential observation outliers, and a new way to set the offline mode release notes
July 11, 2023
New model binding to run PDAF with the NEMO ocean model. Available on Github: github.com/PDAF/NEMO-PDAF.

The Parallel Data Assimilation Framework - PDAF - is a software environment for 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 LESTKF and nonlinear filters as well of variational methods (3D-Var and 3D ensemble Var). 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 and can be used to teach data assimilation.

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. PDAF makes new algorithmic developments readily available through the interface such that they can be immediately applied with existing implementations. The PDAF release package provides small models for easy testing of algorithmic developments and for teaching data assimilation.

PDAF is a community 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 6 weeks ago Last modified on Jun 11, 2024, 5:12:15 PM