Changes between Version 181 and Version 182 of WikiStart


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May 30, 2025, 5:17:58 PM (2 days ago)
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  • WikiStart

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    1616
    1717<table align="right" style="border: 1px outset #ddc; background: #ccf;" cellpadding=5><tr><th align="center"><b>News</b></th></tr>
    18 <tr><td><u>May 18, 2025</u><br>
    19 <b>Revision of documentation in progress:</b> We started to revise the documentation on this website in preparation for the upcoming release of PDAF V3.0
    20 </td></tr>
     18<tr><td><u>May 30, 2025</u><br>
     19<b>Release of Version 3.0beta</b> - Major code revision and modernization including new functionality (<a href="/trac/wiki/ReleaseNotes">release notes</a>)</td></tr>
    2120<tr><td><u>April 27-May 2, 2025</u><br>
    2221<b><a href="/trac/wiki/EGU2025">PDAF at the EGU General Assembly 2025</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 assimilation on Wednesday.</td></tr>
    2322<tr><td><u>April 3, 2025</u><br>
    24 <b>New model-binding</b> to PDAF: application to <b>SCHISM ocean model</b> by Yu et al. published in Ocean Modling, <a href="https://doi.org/10.1016/j.ocemod.2025.102546">doi:10.1016/j.ocemod.2025.102546</a>
     23<b>New model coupling</b> to PDAF: application to <b>SCHISM ocean model</b> by Yu et al. published in Ocean Modling, <a href="https://doi.org/10.1016/j.ocemod.2025.102546">doi:10.1016/j.ocemod.2025.102546</a>
    2524<tr><td><u>December 9, 2024</u><br>
    2625<b>pyPDAF</b> is a Python interface to PDAF, developed by Yumeng Chen, University of Reading. For now, please see <a href="https://github.com/yumengch/pyPDAF">the pyPDAF Github repository</a> for further information. The <a href="https://doi.org/10.5194/egusphere-2024-1078">preprint at GMD</a> describes pyPDAF.</td></tr>
    … …  
    2827<b>Release of Version 2.3.1</b> - bug fix for parallelized 3D-Var convergence check and additional initialization routines for PDAF-OMI (<a href="/trac/wiki/ReleaseNotes">release notes</a>)</td></tr>
    2928<tr><td><u>July 11, 2023</u><br>
    30 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>
     29New model coupling to run PDAF with the <b>NEMO ocean model</b>. Available on Github: <a target="_blank" href="https://github.com/PDAF/NEMO-PDAF">github.com/PDAF/NEMO-PDAF</a></td></tr>
    3130<!--<tr><td><u>April 15-19, 2024</u><br>
    3231<b><a href="/trac/wiki/EGU2025">PDAF at the EGU General Assembly 2025</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 assimilation on Wednesday.</td></tr>//-->
    … …  
    4241
    4342
    44 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.
     43The Parallel Data Assimilation Framework - PDAF - is a software environment for data assimilation. PDAF simplifies the implementation of data assimilation systems with existing numerical models. With this, users can obtain a data assimilation system with less work and can focus on applying data assimilation.
    4544
    46 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.
     45PDAF provides fully implemented and optimized data assimilation algorithms, in particular ensemble-based Kalman filters like LETKF, EnKF and LESTKF as well as nonlinear filters and 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 high-performance 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.
    4746
    48 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.
     47PDAF provides a standardized interface that separates the numerical model from the assimilation routines and the observation handling. This allows independent further development of the assimilation methods and the model, as well as the implementation of further observations. PDAF makes new algorithmic developments readily available through the interface such that they can be immediately applied with existing implementations. The PDAF release provides small models for easy testing of algorithmic developments and for teaching data assimilation. There are also existing coupling codes for different models of Earth system components.
    4948
    50 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.
     49PDAF 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 coupling routines for different numerical models, or observation operators.
    5150
    5251
     52{{{
     53#!html
     54<table class="wiki">
     55<tr><td><b>Information on PDAF V3.0 for users of PDAF2:</b><br>
     56PDAF's release V3.0 is a major code update and not fully compatible with previous releases. There are some small adaptions required for existing codes, but there is also an ample amount of new functionality and a new universal calling interface:
     57<ul>
     58<li><a href="https://pdaf.awi.de/trac/wiki/PortingToPDAF3">Information on adaptions for using PDAF V3.0 with previous implementations</a></li>
     59<li><a href="https://pdaf.awi.de/trac/wiki/PDAF3_new_functionality">New functionality in PDAF V3.0</a></li>
     60<li><a href="https://pdaf.awi.de/trac/wiki/">The new universal PDAF3 assimilation interface</a></li>
     61</ul>
     62</td></tr></table>
     63}}}
    5364
    54 Content:[[BR]]
     65=== Information, documentation, and tutorials ===
     66
     67
     68{{{
     69#!html
     70<table class="wiki">
     71<tr><td><b>Revision of documentation in progress:</b> We are in the process of revising the documentation for PDAF V3.0. Please excuse if some links do not work properly.
     72</td></tr></table>
     73}}}
     74
    5575 * [FeaturesofPdaf Features and requirements of PDAF]
    5676 * [wiki:ModelsConnectedToPDAF List of models connected to PDAF]
    … …  
    7090 * Full Documentation
    7191   * [wiki:CompilingPdaf Compiling the PDAF library]
    72    * [wiki:ImplementationGuide_PDAF23 Implementation Guide for the online assimilation system]
    73    * [wiki:OfflineImplementationGuide_PDAF23 Implementation Guide for the offline assimilation program]
     92   * [wiki:ImplementationGuide_PDAF3 Implementation Guide for the online assimilation system]
     93   * [wiki:OfflineImplementationGuide_PDAF3 Implementation Guide for the offline assimilation program]
    7494   * [wiki:DataAssimilationDiagnostics Diagnostic functions]
    75    * [wiki:PDAF_OMI_Overview Observation handling]
     95   * [wiki:PDAF_OMI_Overview Observation handling and diagnostics]
    7696   * [wiki:ImplementGenerateObs Generate synthetic observations for twin experiments]
    7797   * [wiki:AvailableOptionsforInitPDAF Overview of filter-specific options]