Joss paper #114
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Joss paper #114
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Hi all, I have drafted a JOSS paper for MOTrainer. You have all contributed to this repository therefore I listed you all as authors. Would you like to look at this paper? It should be rendered as a GitHub Action artifact. You can download it (need to be unzipped) from here. Thanks in advance! |
rogerkuou
requested review from
sonjageorgievska,
cpranav93,
meiertgrootes,
SarahAlidoost and
xushanthu-2014
rogerkuou
commented
Jan 10, 2024
rogerkuou
commented
Jan 11, 2024
remove duplicated paragragh
rogerkuou
commented
Jan 11, 2024
Suggestions from Susan
Suggestion from Sonja and Meiert
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| Data assimilation is an essential tool in Earth and environmental sciences, enabling physical model states to be constrained using observational data. | ||
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| In the DA process, observations are integrated into the physical model through the application of a Measurement Operator (MO) – a connection model mapping physical model states to observations. Researchers have observed that employing a Machine-Learning (ML) model as a surrogate MO can bypass the limitations associated with using an overly simplified MO [@Forman:2014; @XUE:2015; @Forman:2017]. |
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What process are you referring to in the first part of this sentence In the DA process,...?
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I wanted to make a start to talk about Measurement Operators. So I stared with the function of MO in a DA process. Maybe I should say In a DA process, ....?
SarahAlidoost
approved these changes
Jan 15, 2024
Co-authored-by: SarahAlidoost <55081872+SarahAlidoost@users.noreply.github.com>
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