= PDAF - the Parallel Data Assimilation Framework = {{{ #!html
PDAF is developed,
hosted and maintained at the
Computing Center of the Alfred Wegener Institute.
News
February 20, 2018
Short course Data assimilation in the geosciences - Practical data assimilation with the Parallel Data Assimilation Framework At the upcoming EGU General Assembly in Vienna, Austria we will convene this short course on Thursday, April 11, from 14-15:15.
September 3, 2018
Release of Version 1.13.2 (release notes)
(Revised the PDAF example with the Lorenz-96 model; added documentation on the Lorenz-96 model case; 3 bug fixes)
June 24, 2018
New documentation: First Steps with PDAF
April 5, 2018
Update of implementation tutorials. They now describe the new features of PDAF 1.13 and include hints on how to extend the tutorial implementations to more complex cases
March 12, 2018
Release of Version 1.13.1 (release notes)
(added routine to retrieve assimilation flag and two bug corrections)
February 11, 2018
Slides for PDAF tutorial at Ocean Sciences Meeting are now online (see Publications and Presentations)
}}} 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
    • }}} * Basic Documentation * [wiki:FirstSteps First Steps with PDAF] * [wiki:Lorenz_96_model Assimilation with the Lorenz-96 model and PDAF] * [wiki:SoftwarePackage Description of the software package] * [wiki:PdafTutorial Implementation tutorials] * [wiki:WhichFiltertouse Which filter should one use?] * [wiki:EnsembleGeneration Generating ensembles] * Full Documentation * [wiki:ImplementationGuide Implementation Guide for the online assimilation system] * [wiki:OfflineImplementationGuide Implementation Guide for the offline assimilation program] * [wiki:DataAssimilationDiagnostics Diagnostic routines] * [wiki:AvailableOptionsforInitPDAF Overview of filter-specific options] * [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]