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\begin{document}

\title{Insights from very Large Ensemble Data Assimilation Experiments with a
High Resolution General circulation model of the Red Sea}



\author[1]{sivareddy sanikommu}%
\author[1]{Naila Raboudi}%
\author[2]{Mohamad Gharamti}%
\author[3]{Peng Zhan}%
\author[1]{Bilel Hadri}%
\author[4]{Ibrahim Hoteit}%
\affil[1]{King Abdullah University of Science and Technology}%
\affil[2]{Earth Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia}%
\affil[3]{southern university of science and technology}%
\affil[4]{King Abdullah Univerity of Science and Technology}%


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  \date{May 21, 2024}


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\begin{abstract}
Ensemble Kalman Filters (EnKFs), which assimilate observations based on
statistics derived from samples of ocean states called ensemble, have
become the norm for ocean data assimilation (DA) and forecasting. These
schemes are commonly implemented with inflation and localization
techniques to increase their ensemble spread and to filter out spurious
long-range correlations resulting from the limited-size ensembles
imposed by computational burden constraints. Such ad hoc methods were
found not necessary in ensemble DA experiments with simplified
ocean/atmospheric models and large ensembles. Here, we conduct a series
of 1-year-long ensemble experiments with a fully realistic EnKF-DA
system in the Red Sea using tens-to-thousands of ensemble members. The
system assimilates satellite and in-situ observations and accounts for
model uncertainties by integrating a 4km-resolution ocean model with
ECMWF atmospheric ensemble fields, perturbed internal physics and
initial conditions for forecasting.

Our results indicate that accounting for model uncertainties is more
beneficial than simply increasing the ensemble size, with the
improvements due to large ensemble leveling off at about 250 members.
Besides, and in contrast to what is commonly observed with simplified
models, the investigated ensemble DA system still required localization
even when implemented with thousands of members. These findings are
explained by (i) amplified spurious long-range correlations produced by
the low-rank nature of the ECMWF atmospheric forcing ensemble, and (ii)
non-Gaussianity generated by the perturbed internal physical
parameterization schemes. Large ensemble forcing fields and non-Gaussian
DA methods might be needed to take full benefits from large ensembles in
ocean DA.%
\end{abstract}%



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