PDAF - the Parallel Data Assimilation Framework

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

News 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) February 6, 2018 Release of Version 1.13 (release notes) (added model binding for MITgcm, new routines to simplify implementation of parallelized local analysis step)

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
• Documentation
  • First Steps with PDAF
  • Documentation on the downloadable software package
  • Implementation Tutorials
  • Implementation Guide for the online assimilation system
  • Implementation Guide for the offline assimilation program
  • Generating ensembles
  • Diagnostic routines
  • Overview of filter-specific options
  • Which filter should one use?
  • Adding a filter algorithm to PDAF
  • Lists for direct access to documentation on particular routines

• Publications and Presentations involving and about PDAF