= PDAF - the Parallel Data Assimilation Framework = {{{ #!html
PDAF is developed,
hosted and maintained at the
Computing Center of the Alfred Wegener Institute.
ISDA-Online
We co-organize the International Symposium on Data Assimilation - Online. For more information see the ISDA-Online website at University of Vienna
News
April 19-30, 2021
PDAF at the EGU General Assembly 2021 PDAF is present at the virtual EGU 2021 with a short course and different presentations by the PDAF developers and PDAF users
November 30, 2020
Release of Version 1.16 - including the new PDAF-OMI (Observation Module Infrastructure) release notes
November 30, 2020
PDAF-OMI (Observation Module Infrastructure) - the new efficient way to implement observation handling with PDAF
September 15, 2020
New paper describing the use of PDAF with coupled models: Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: Example of AWI-CM. by L. Nerger, Q. Tang, and L. Mu. Geosci. Model Dev., 13, 4305–4321, 2020
}}} 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 LESTKF and nonlinear filters. 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 and can be used to teach data assimilation. 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. PDAF makes new algorithmic developments readily available through the interface such that they can be immediately applied with existing implementations. The PDAF release package 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 functions] * [wiki:PDAF_OMI_Overview Observation handling] * [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] * [wiki:FAQ_PDAF FAQ] * [PublicationsandPresentations Publications and Presentations involving and about PDAF]