= PDAF - the Parallel Data Assimilation Framework = {{{ #!html

PDAF is developed,
hosted and maintained at the
Computing Center of the Alfred Wegener Institute.

News
August 1, 2019 GMD discussion paper: Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: Example of AWI-CM. Discussion is open until September 26.
July 4, 2019 Release of Version 1.14 (release notes) (Adding functionality to generate synthetic observations; adding a particle filter; adding data assimilation for the Lorenz-63 model example; 3 bug fixes)
April 25, 2019 New paper: Particle filters for high-dimensional geoscience applications: a review by PJ van Leeuwen, HR Künsch, L Nerger, R Potthast, S Reich, accepted for Q. J. Royal. Meteorol. Soc. doi:10.1002/qj.3551
April 5, 2019 PDAF at the EGU 2019 The PDAF developers are present at the EGU assembly 2019 with a Short course and several presentations... (more)
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

}}} 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 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. 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:Lorenz_63_model Assimilation with the Lorenz-63 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:ImplementGenerateObs Generate synthetic observations for twin experiments] * [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]