= PDAF - the Parallel Data Assimilation Framework = {{{ #!html
PDAF is developed,
hosted and maintained at the
Computing Center of the Alfred Wegener Institute.
News
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)
January 1, 2016
New paper "Assessment of a Nonlinear Ensemble Transform Filter for High-Dimensional Data Assimilation" published in Monthly Weather Review (see publications)
May 6, 2015
New paper "On serial observation processing in localized ensemble Kalman filters" published in Monthly Weather Review (see publications)
}}} 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:[[BR]] * [FeaturesofPdaf Features and requirements of PDAF] * [GeneralImplementationConcept Implementation Concept of PDAF] {{{ #!html
  • Software download
    • }}} * [wiki:SoftwarePackage Documentation on the downloadable software package] * Documentation * [wiki:PdafTutorial Implementation Tutorials] * [wiki:ImplementationGuide Implementation Guide for the online assimilation system] * [wiki:OfflineImplementationGuide Implementation Guide for the offline assimilation program] * [wiki:EnsembleGeneration Generating ensembles] * [wiki:DataAssimilationDiagnostics Diagnostic routines] * [wiki:AvailableOptionsforInitPDAF Overview of filter-specific options] * [wiki:WhichFiltertouse Which filter should one use?] * [wiki:AddFilterAlgorithm Adding a filter algorithm to PDAF] * [wiki:RoutineOverviews Lists for direct access to documentation on particular routines] * [PublicationsandPresentations Publications and Presentations involving and about PDAF]