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\begin{document}

\title{Exploring a New Computationally Efficient Data Assimilation Algorithm
For Ocean Models}



\author[1]{Elizabeth Carlson}%
\author[2]{Luke P. Van Roekel}%
\author[2]{Humberto C Godinez}%
\author[2]{Mark R. Petersen}%
\author[1]{Adam Larios}%
\affil[1]{University of Nebraska - Lincoln}%
\affil[2]{Los Alamos National Laboratory (DOE)}%


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  \date{May 23, 2024}


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\begin{abstract}
We present a new data assimilation algorithm known as the Continuous
Data Assimilation (CDA) algorithm that has been tested extensively in
the mathematical literature and, most recently, in a downscaling
simulation in the atmospheric literature. Unlike more common data
assimilation methods, the CDA algorithm has an exponential convergence
rate and is computationally efficient. This work is the first attempt to
demonstrate the viability of the data assimilation algorithm in
large-scale ocean models. We implement the CDA algorithm in the Model
for Prediction Across Scales - Ocean in an idealized mesoscale eddy test
case, demonstrating the ability of the data assimilation algorithm to
capture the net effects of unresolved processes in low-resolution
models.%
\end{abstract}%



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