PDAF - the Parallel Data Assimilation Framework

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

News November 19, 2020 Job announcement: Postdoc position on Biogeochemical Modeling and Data Assimilation (closing date December 16, 2020) September 15, 2020 New paper describing the use of PDAF with coupled models: Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: Example of AWI-CM. by L. Nerger, Q. Tang, and L. Mu. Geosci. Model Dev., 13, 4305–4321, 2020 March 12, 2020 Release of Version 1.15.1 (release notes) December 9, 2019 Release of Version 1.15 (release notes) July 4, 2019 Release of Version 1.14 (release notes) April 25, 2019 New paper: Particle filters for high-dimensional geoscience applications: a review by PJ van Leeuwen, HR Künsch, L Nerger, R Potthast, S Reich, Q. J. Royal. Meteorol. Soc., 145, 2335-2365 doi:10.1002/qj.3551

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
• 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
  • Implementation Guide for the online assimilation system
  • Implementation Guide for the offline assimilation program
  • Diagnostic functions
  • 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

• Publications and Presentations involving and about PDAF