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PDAF

PDAF - the Parallel Data Assimilation Framework is a software framework for interdisciplinary data assimilation. PDAF provides support for ensemble modeling and fully implemented data assimilation methods to merge models and observational data.

230
mentions
3
contributors

Cite this software

DOI:

10.5281/zenodo.7861812

What PDAF can do for you

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 is also 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.

Logo of PDAF
Keywords
Atmospheric science
data assimilation
Earth & Environment
Earth System Modelling
Ensemble Kalman Filter
High performance computing
hydrological modeling
Inversion
Modeling
ocean modelling
uncertainty quantification
Variational methods
Programming languages
  • Fortran 92%
  • Shell 4%
  • Makefile 2%
  • C 1%
  • MATLAB 1%
  • Python 1%
License
  • LGPL-3.0-or-later
</>Source code
Packages
git

Participating organisations

Alfred Wegener Institute for Polar and Marine Research (AWI)

Reference papers

Mentions

Contributors

LN
Lars Nerger
Lead Developer
Alfred-Wegener-Institute
GG
Gernot Geppert
Contributor
University of Reading, UK (now at German Weather Service DWD)
YC
Yumeng Chen
Contributor
National Center for Earth Observation; University of Reading, UK