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 December 17, 2021 Release of Version 2.0 - major release including 3D variational methods release notes December 10, 2021 New paper applying PDAF for strongly-coupled data assimilation: Strongly Coupled Data Assimilation of Ocean Observations into an Ocean-Atmosphere Model by Q. Tang, L. Mu, H. Goessling, T. Semmler, L. Nerger. Geophys. Res. Lett. (2021) 48(24), e2021GL094941 November 30, 2020 Release of Version 1.16 - including the new PDAF-OMI (Observation Module Infrastructure) 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 LESTKF and nonlinear filters. 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.

Content:

• Features and requirements of PDAF
• List of models connected to PDAF
• Implementation Concept of PDAF
• Software download
• Basic Documentation
  • First Steps with PDAF
  • Assimilation with the Lorenz-96 model and PDAF
  • Assimilation with the Lorenz-63 model and PDAF
  • Description of the software package
  • Implementation tutorials
  • Which filter should one use?
  • Generating ensembles
• Full Documentation
  • Compiling the PDAF library
  • Implementation Guide for the online assimilation system
  • Implementation Guide for the offline assimilation program
  • Diagnostic functions
  • Observation handling
  • Generate synthetic observations for twin experiments
  • Overview of filter-specific options
  • Adding a filter algorithm to PDAF
  • Lists for direct access to documentation on particular routines
  • Auxiliary and advanced functionality
  • FAQ

• Publications and Presentations involving and about PDAF