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\begin{document}

\title{A Deep Learning Network to Retrieve Ocean Hydrographic Profiles from
Combined Satellite and In Situ Measurements}



\author[1]{Bruno Buongiorno Nardelli}%
\affil[1]{Consiglio Nazionale delle Ricerche}%


\vspace{-1em}



  
  \date{June 08, 2024}


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\begin{abstract}
An efficient combination of the data collected by multiple instruments
and platforms is needed to obtain accurate 3D ocean state estimates,
representing a fundamental step to describe ocean dynamics and its role
in the Earth climate system and marine ecosystems. Observations can
either be assimilated in ocean general circulation models or used to
feed data-driven reconstructions and diagnostic models. Here we describe
an innovative deep learning algorithm that projects sea surface
satellite data at depth after training with sparse co-located in situ
vertical profiles. The technique is based on a stacked Long Short-Term
Memory neural network, coupled to a Monte-Carlo dropout approach, and is
applied here to the measurements collected between 2010 and 2018 over
the North Atlantic Ocean. The model provides hydrographic vertical
profiles and associated uncertainties from corresponding remotely sensed
surface estimates, outperforming similar reconstructions from simpler
statistical algorithms and feed-forward networks.%
\end{abstract}%



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