= PDAF - the Parallel Data Assimilation Framework =

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<table style="margin: 0 0 0 0; border: 1px outset #ddc; background: #ccf;" cellpadding=5><tr><td align="center">PDAF is developed, <br>hosted and maintained by the<br><a href="https://www.awi.de/en/science/special-groups/scientific-computing/data-assimilation.html" target="_blank">Data Assimilation Team in the Computing Center of the Alfred Wegener Institute</a>.</td></tr></table>

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<table style="margin: 0 0 0 0; border: 1px outset #ddc; background: #ccf;" cellpadding=5><tr><th align="center"><b>ISDA-Online</b></th></tr>
<tr><td>We co-organize the International Symposium on Data Assimilation - Online. For more information see the <a target="_blank" href="https://isda-online.univie.ac.at/">ISDA-Online website at University of Vienna</a></td></tr>
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<table align="right" style="border: 1px outset #ddc; background: #ccf;" cellpadding=5><tr><th align="center"><b>News</b></th></tr>
<tr><td><u>April 29, 2024</u><br>
<b>Release of Version 2.2.1</b> - with performance improvements for local filter algorimths; adding a 2D+1D factorized localization; some bug-fixes in OMI <a href="/trac/wiki/ReleaseNotes">release notes</a></td></tr>
<tr><td><u>April 15-19, 2024</u><br>
<b><a href="/trac/wiki/EGU2024">PDAF at the EGU General Assembly 2024</a></b> There will be several presentations of research in which PDAF is used. In addition, there will be a <b>short course</b> on data assimilaation on Friday.</td></tr>
<tr><td><u>March 8, 2024</u><br>
<b>Release of Version 2.2</b> - adding e.g. a non-isotropic localization, exclusion of potential observation outliers, and a new way to set the offline mode <a href="/trac/wiki/ReleaseNotes">release notes</a></td></tr><tr><td><u>July 11, 2023</u><br>
New model binding to run PDAF with the NEMO ocean model. Available on Github: <b><a target="_blank" href="https://github.com/PDAF/NEMO-PDAF">github.com/PDAF/NEMO-PDAF</a></b>.
<!--</td></tr><tr><td><u>July 11, 2023</u><br>
PDAF is now also available on Github: <b><a target="_blank" href="https://github.com/PDAF/PDAF">github.com/PDAF/PDAF</a></b>. Also see <a target="_blank" href="https://doi.org/10.5281/zenodo.7861829">Zenodo for release packages</a>.</td></tr>
<tr><td><u>April 24-28, 2023</u><br>//-->
<!--<tr><td><u>November 19, 2020</u><br>
Job announcement: <a href="https://recruitingapp-5442.de.umantis.com/Vacancies/698/Description/2"><b>Postdoc position on Biogeochemical Modeling and Data Assimilation</b></a> (closing date December 16, 2020)</td></tr>//-->
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The Parallel Data Assimilation Framework - PDAF - is a software environment for 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 as well of variational methods (3D-Var and 3D ensemble Var). 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]
 * [wiki:ModelsConnectedToPDAF List of models connected to PDAF]
 * [GeneralImplementationConcept Implementation Concept of PDAF]
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 * 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:CompilingPdaf Compiling the PDAF library]
   * [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:AuxiliaryRoutines Auxiliary and advanced functionality]
   * [wiki:FAQ_PDAF FAQ]


 * [PublicationsandPresentations Publications and Presentations involving and about PDAF]