wiki:WikiStart

PDAF - the Parallel Data Assimilation Framework

PDAF is developed,
hosted and maintained at the
Computing Center of the Alfred Wegener Institute.
News
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)
May 30, 2017
New paper "The smoother extension of the nonlinear ensemble transform filter" published in Tellus A (see publications)
December 21, 2016
Release of Version 1.12 (release notes)
(3 new filter methods, diagnostic and ensemble generation tools)

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:

Last modified 6 days ago Last modified on 02/12/18 06:00:16