PDAF - the Parallel Data Assimilation Framework
|
|
|
|
| News |
|---|
May 30, 2025
Release of Version 3.0beta - Major code revision and modernization including new functionality (release notes) |
April 27-May 2, 2025
PDAF at the EGU General Assembly 2025 There will be several presentations of research in which PDAF is used. In addition, there will be a short course on data assimilation on Wednesday. |
April 3, 2025
New model coupling to PDAF: application to SCHISM ocean model by Yu et al. published in Ocean Modling, doi:10.1016/j.ocemod.2025.102546
| December 9, 2024
pyPDAF is a Python interface to PDAF, developed by Yumeng Chen, University of Reading. For now, please see the pyPDAF Github repository for further information. The preprint at GMD describes pyPDAF. |
November 25, 2024
Release of Version 2.3.1 - bug fix for parallelized 3D-Var convergence check and additional initialization routines for PDAF-OMI (release notes) |
July 11, 2023
New model coupling to run PDAF with the NEMO ocean model. Available on Github: github.com/PDAF/NEMO-PDAF |
|
The Parallel Data Assimilation Framework - PDAF - is a software environment for data assimilation. PDAF simplifies the implementation of data assimilation systems 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, EnKF and LESTKF as well as nonlinear filters and variational methods (3D-Var and 3D ensemble Var). 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 high-performance 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 and the observation handling. This allows independent further development of the assimilation methods and the model, as well as the implementation of further observations. PDAF makes new algorithmic developments readily available through the interface such that they can be immediately applied with existing implementations. The PDAF release provides small models for easy testing of algorithmic developments and for teaching data assimilation. There are also existing coupling codes for different models of Earth system components.
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 coupling routines for different numerical models, or observation operators.
Information on PDAF V3.0 for users of PDAF2:
PDAF's release V3.0 is a major code update and not fully compatible with previous releases. There are some small adaptions required for existing codes, but there is also an ample amount of new functionality and a new universal calling interface:
|
| Revision of documentation in progress: We are in the process of revising the documentation for PDAF V3.0. Please excuse if some links do not work properly.
|
