PDAF - the Parallel Data Assimilation Framework

PDAF is developed, hosted and maintained at the Computing Center of the Alfred Wegener Institute.

News February 20, 2018

Short course 'Data assimilation in the geosciences - Practical data assimilation with the Parallel Data Assimilation Framework' At the upcoming EGU General Assembly in Vienna, Austria we will convene this short course on Thursday, April 11, from 14-15:15. September 3, 2018
Release of Version 1.13.2 (release notes)
(Revised the PDAF example with the Lorenz-96 model; added documentation on the Lorenz-96 model case; 3 bug fixes) June 24, 2018
New documentation: First Steps with PDAF April 5, 2018
Update of implementation tutorials. They now describe the new features of PDAF 1.13 and include hints on how to extend the tutorial implementations to more complex cases March 12, 2018
Release of Version 1.13.1 (release notes)
(added routine to retrieve assimilation flag and two bug corrections) February 11, 2018
Slides for PDAF tutorial at Ocean Sciences Meeting are now online (see Publications and Presentations)

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.

Content:

• Features and requirements of PDAF
• Implementation Concept of PDAF
• Software download
• Basic Documentation
  • First Steps with PDAF
  • Assimilation with the Lorenz-96 model and PDAF
  • Description of the software package
  • Implementation tutorials
  • Which filter should one use?
  • Generating ensembles
• Full Documentation
  • Implementation Guide for the online assimilation system
  • Implementation Guide for the offline assimilation program
  • Diagnostic routines
  • Overview of filter-specific options
  • Adding a filter algorithm to PDAF
  • Lists for direct access to documentation on particular routines

• Publications and Presentations involving and about PDAF