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10.21105.joss.05150.crossref.xml --- joss.05150/10.21105.joss.05150.crossref.xml | 142 ++++++++++++++++++++ 1 file changed, 142 insertions(+) create mode 100644 joss.05150/10.21105.joss.05150.crossref.xml diff --git a/joss.05150/10.21105.joss.05150.crossref.xml b/joss.05150/10.21105.joss.05150.crossref.xml new file mode 100644 index 0000000000..580de451aa --- /dev/null +++ b/joss.05150/10.21105.joss.05150.crossref.xml @@ -0,0 +1,142 @@ + + + + 20240229T190431-93632f684ad4ffa3ffc3fc34ee1e7bdca9b9720c + 20240229190431 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 02 + 2024 + + + 9 + + 94 + + + + DAPPER: Data Assimilation with Python: a Package for +Experimental Research + + + + Patrick N. + Raanes + https://orcid.org/0000-0001-5073-6786 + + + Yumeng + Chen + https://orcid.org/0000-0002-2319-6937 + + + Colin + Grudzien + https://orcid.org/0000-0002-3084-3178 + + + + 02 + 29 + 2024 + + + 5150 + + + 10.21105/joss.05150 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.10710355 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/5150 + + + + 10.21105/joss.05150 + https://joss.theoj.org/papers/10.21105/joss.05150 + + + https://joss.theoj.org/papers/10.21105/joss.05150.pdf + + + + + + Data assimilation: Methods, algorithms, and +applications + Asch + 10.1137/1.9781611974546 + 2016 + Asch, M., Bocquet, M., & Nodet, +M. (2016). Data assimilation: Methods, algorithms, and applications (p. +xvii+295). SIAM. +https://doi.org/10.1137/1.9781611974546 + + + An iterative ensemble Kalman +smoother + Bocquet + Quarterly Journal of the Royal Meteorological +Society + 682 + 140 + 10.1002/qj.2236 + 2014 + Bocquet, M., & 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 + + + A non-Gaussian ensemble filter update for +data assimilation. + Anderson + Monthly Weather Review + 11 + 138 + 10.1175/2010MWR3253.1 + 2010 + Anderson, J. L. (2010). A +non-Gaussian ensemble filter update for data assimilation. Monthly +Weather Review, 138(11), 4186–4198. +https://doi.org/10.1175/2010MWR3253.1 + + + + + + From b71e4766ecb0d007e3136b9a6c09049df7d9f0c9 Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Thu, 29 Feb 2024 19:05:09 +0000 Subject: [PATCH 3/4] Creating 10.21105.joss.05150.jats --- joss.05150/10.21105.joss.05150.jats | 233 ++++++++++++++++++++++++++++ 1 file changed, 233 insertions(+) create mode 100644 joss.05150/10.21105.joss.05150.jats diff --git a/joss.05150/10.21105.joss.05150.jats b/joss.05150/10.21105.joss.05150.jats new file mode 100644 index 0000000000..a9a6cfd525 --- /dev/null +++ b/joss.05150/10.21105.joss.05150.jats @@ -0,0 +1,233 @@ + + +

+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +5150 +10.21105/joss.05150 + +DAPPER: Data Assimilation with Python: a Package for +Experimental Research + + + +https://orcid.org/0000-0001-5073-6786 + +Raanes +Patrick N. + + + +* + + +https://orcid.org/0000-0002-2319-6937 + +Chen +Yumeng + + + + +https://orcid.org/0000-0002-3084-3178 + +Grudzien +Colin + + + + + + +NORCE, Bergen, Norway + + + + +NERSC, Bergen, Norway + + + + +Department of Meteorology and NCEO, University of Reading, +Reading, UK + + + + +CW3E - Scripps Institution of Oceanography, University of +California, San Diego, USA + + + + +Department of Mathematics and Statistics, University of +Nevada, Reno, USA + + + + +* E-mail: + + +10 +1 +2023 + +9 +94 +5150 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +data-assimilation +state-estimation +bayesian-filter +kalman-filtering +enkf +particle-filter +chaos +bayesian-methods + + + + + + Summary +

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.

+ +

Screenshot from “liveplotting” in DAPPER

+ +
+
+ + Statement of need +

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.

+
+ + State of the field +

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 + DART, + PDAF, + JEDI, + and + OpenDA. + 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 + (Anderson, + 2010; + Asch + et al., 2016; + Bocquet + & Sakov, 2014).

+
+ + Outlook +

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 enhancement. These are not necessarily + minor, and have mainly been filed under the v2 + milestone.

+
+ + Acknowledgements +

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.

+
+ + + + + + + AschMark + BocquetMarc + NodetMaëlle + + Data assimilation: Methods, algorithms, and applications + SIAM + Philadelphia, PA + 2016 + + 10.1137/1.9781611974546 + xvii+295 + + + + + + + BocquetMarc + SakovPavel + + An iterative ensemble Kalman smoother + Quarterly Journal of the Royal Meteorological Society + Wiley Online Library + 2014 + 140 + 682 + 10.1002/qj.2236 + 1521 + 1535 + + + + + + AndersonJeffrey L. + + A non-Gaussian ensemble filter update for data assimilation. + Monthly Weather Review + 2010 + 138 + 11 + 10.1175/2010MWR3253.1 + 4186 + 4198 + + + + +
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