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142 changes: 142 additions & 0 deletions joss.05150/10.21105.joss.05150.crossref.xml
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<volume_title>Data assimilation: Methods, algorithms, and
applications</volume_title>
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<doi>10.1137/1.9781611974546</doi>
<cYear>2016</cYear>
<unstructured_citation>Asch, M., Bocquet, M., &amp; Nodet,
M. (2016). Data assimilation: Methods, algorithms, and applications (p.
xvii+295). SIAM.
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<article_title>An iterative ensemble Kalman
smoother</article_title>
<author>Bocquet</author>
<journal_title>Quarterly Journal of the Royal Meteorological
Society</journal_title>
<issue>682</issue>
<volume>140</volume>
<doi>10.1002/qj.2236</doi>
<cYear>2014</cYear>
<unstructured_citation>Bocquet, M., &amp; Sakov, P. (2014).
An iterative ensemble Kalman smoother. Quarterly Journal of the Royal
Meteorological Society, 140(682), 1521–1535.
https://doi.org/10.1002/qj.2236</unstructured_citation>
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<article_title>A non-Gaussian ensemble filter update for
data assimilation.</article_title>
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<journal_title>Monthly Weather Review</journal_title>
<issue>11</issue>
<volume>138</volume>
<doi>10.1175/2010MWR3253.1</doi>
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<unstructured_citation>Anderson, J. L. (2010). A
non-Gaussian ensemble filter update for data assimilation. Monthly
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233 changes: 233 additions & 0 deletions joss.05150/10.21105.joss.05150.jats
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<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2 20190208//EN"
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<front>
<journal-meta>
<journal-id></journal-id>
<journal-title-group>
<journal-title>Journal of Open Source Software</journal-title>
<abbrev-journal-title>JOSS</abbrev-journal-title>
</journal-title-group>
<issn publication-format="electronic">2475-9066</issn>
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<publisher-name>Open Journals</publisher-name>
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<article-id pub-id-type="publisher-id">5150</article-id>
<article-id pub-id-type="doi">10.21105/joss.05150</article-id>
<title-group>
<article-title>DAPPER: Data Assimilation with Python: a Package for
Experimental Research</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-5073-6786</contrib-id>
<name>
<surname>Raanes</surname>
<given-names>Patrick N.</given-names>
</name>
<xref ref-type="aff" rid="aff-1"/>
<xref ref-type="aff" rid="aff-2"/>
<xref ref-type="corresp" rid="cor-1"><sup>*</sup></xref>
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<contrib contrib-type="author">
<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-2319-6937</contrib-id>
<name>
<surname>Chen</surname>
<given-names>Yumeng</given-names>
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<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-3084-3178</contrib-id>
<name>
<surname>Grudzien</surname>
<given-names>Colin</given-names>
</name>
<xref ref-type="aff" rid="aff-4"/>
<xref ref-type="aff" rid="aff-5"/>
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<aff id="aff-1">
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<institution>NORCE, Bergen, Norway</institution>
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<aff id="aff-2">
<institution-wrap>
<institution>NERSC, Bergen, Norway</institution>
</institution-wrap>
</aff>
<aff id="aff-3">
<institution-wrap>
<institution>Department of Meteorology and NCEO, University of Reading,
Reading, UK</institution>
</institution-wrap>
</aff>
<aff id="aff-4">
<institution-wrap>
<institution>CW3E - Scripps Institution of Oceanography, University of
California, San Diego, USA</institution>
</institution-wrap>
</aff>
<aff id="aff-5">
<institution-wrap>
<institution>Department of Mathematics and Statistics, University of
Nevada, Reno, USA</institution>
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<corresp id="cor-1">* E-mail: <email></email></corresp>
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<pub-date date-type="pub" publication-format="electronic" iso-8601-date="2023-01-10">
<day>10</day>
<month>1</month>
<year>2023</year>
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<volume>9</volume>
<issue>94</issue>
<fpage>5150</fpage>
<permissions>
<copyright-statement>Authors of papers retain copyright and release the
work under a Creative Commons Attribution 4.0 International License (CC
BY 4.0)</copyright-statement>
<copyright-year>2022</copyright-year>
<copyright-holder>The article authors</copyright-holder>
<license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/">
<license-p>Authors of papers retain copyright and release the work under
a Creative Commons Attribution 4.0 International License (CC BY
4.0)</license-p>
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<kwd-group kwd-group-type="author">
<kwd>data-assimilation</kwd>
<kwd>state-estimation</kwd>
<kwd>bayesian-filter</kwd>
<kwd>kalman-filtering</kwd>
<kwd>enkf</kwd>
<kwd>particle-filter</kwd>
<kwd>chaos</kwd>
<kwd>bayesian-methods</kwd>
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<body>
<sec id="summary">
<title>Summary</title>
<p>Data assimilation (DA) is the science of optimally combining sparse
data and computational models, both of which are typically large and
varied. Based on classical statistics, signal processing, and control
systems theory, and increasingly, machine learning, DA was primarily
developed in weather, climate, and oceanographic forecasting but is
presently used all across the geosciences and beyond. DAPPER is a set
of templates for benchmarking the performance of DA methods.</p>
<fig>
<caption><p>Screenshot from “liveplotting” in DAPPER</p></caption>
<graphic mimetype="image" mime-subtype="png" xlink:href="media/dapper-L63.png" />
</fig>
</sec>
<sec id="statement-of-need">
<title>Statement of need</title>
<p>DAPPER provides experimental support and guidance for new
developments in DA by facilitating numerical investigations through a
variety of typical test cases and statistics. It reproduces numerical
benchmarks results reported in the literature and facilitates
comparative studies, thus promoting the reliability and relevance of
the results. DAPPER is open source, written in Python, and focuses on
readability; this promotes the reproduction and dissemination of the
underlying science, and makes it easy to adapt and extend.</p>
</sec>
<sec id="state-of-the-field">
<title>State of the field</title>
<p>The README contains a comprehensive list of related projects. Among
projects aimed at research and teaching, DAPPER is probably the most
mature, while it is small is size and complexity compared to those
targeting real-world applications, such as
<ext-link ext-link-type="uri" xlink:href="https://dart.ucar.edu/">DART</ext-link>,
<ext-link ext-link-type="uri" xlink:href="https://pdaf.awi.de/trac/wiki">PDAF</ext-link>,
<ext-link ext-link-type="uri" xlink:href="https://www.jcsda.org/jcsda-project-jedi">JEDI</ext-link>,
and
<ext-link ext-link-type="uri" xlink:href="https://openda.org/">OpenDA</ext-link>.
The README also lists 5 publications (to date) that used DAPPER, and
those results published in the literature that have been reproduced
with DAPPER, of which we mention
(<xref alt="Anderson, 2010" rid="ref-anderson2010non" ref-type="bibr">Anderson,
2010</xref>;
<xref alt="Asch et al., 2016" rid="ref-asch2016data" ref-type="bibr">Asch
et al., 2016</xref>;
<xref alt="Bocquet &amp; Sakov, 2014" rid="ref-bocquet2014iterative" ref-type="bibr">Bocquet
&amp; Sakov, 2014</xref>).</p>
</sec>
<sec id="outlook">
<title>Outlook</title>
<p>The intention is for DAPPER to continue benchmarking and
illustrating the latest methods in DA and beyond. For example, at the
time of writing, there are 26 open ticket in the repository’s issue
tracker. Most have been opened by the principle investigator, and are
tagged with <italic>enhancement</italic>. These are not necessarily
minor, and have mainly been filed under the <monospace>v2</monospace>
milestone.</p>
</sec>
<sec id="acknowledgements">
<title>Acknowledgements</title>
<p>We acknowledge support from Alberto Carrassi and Laurent Bertino of
NERSC in the genesis of this project, continued financial backing from
NORCE, and contributions from Maxime Tondeur, and Remy Dubois.</p>
</sec>
</body>
<back>
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<ref id="ref-asch2016data">
<element-citation publication-type="book">
<person-group person-group-type="author">
<name><surname>Asch</surname><given-names>Mark</given-names></name>
<name><surname>Bocquet</surname><given-names>Marc</given-names></name>
<name><surname>Nodet</surname><given-names>Maëlle</given-names></name>
</person-group>
<source>Data assimilation: Methods, algorithms, and applications</source>
<publisher-name>SIAM</publisher-name>
<publisher-loc>Philadelphia, PA</publisher-loc>
<year iso-8601-date="2016">2016</year>
<edition></edition>
<pub-id pub-id-type="doi">10.1137/1.9781611974546</pub-id>
<fpage>xvii+295</fpage>
<lpage></lpage>
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</ref>
<ref id="ref-bocquet2014iterative">
<element-citation publication-type="article-journal">
<person-group person-group-type="author">
<name><surname>Bocquet</surname><given-names>Marc</given-names></name>
<name><surname>Sakov</surname><given-names>Pavel</given-names></name>
</person-group>
<article-title>An iterative ensemble Kalman smoother</article-title>
<source>Quarterly Journal of the Royal Meteorological Society</source>
<publisher-name>Wiley Online Library</publisher-name>
<year iso-8601-date="2014">2014</year>
<volume>140</volume>
<issue>682</issue>
<pub-id pub-id-type="doi">10.1002/qj.2236</pub-id>
<fpage>1521</fpage>
<lpage>1535</lpage>
</element-citation>
</ref>
<ref id="ref-anderson2010non">
<element-citation publication-type="article-journal">
<person-group person-group-type="author">
<name><surname>Anderson</surname><given-names>Jeffrey L.</given-names></name>
</person-group>
<article-title>A non-Gaussian ensemble filter update for data assimilation.</article-title>
<source>Monthly Weather Review</source>
<year iso-8601-date="2010">2010</year>
<volume>138</volume>
<issue>11</issue>
<pub-id pub-id-type="doi">10.1175/2010MWR3253.1</pub-id>
<fpage>4186</fpage>
<lpage>4198</lpage>
</element-citation>
</ref>
</ref-list>
</back>
</article>
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