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\begin{document}

\title{ResLearner: geophysically-informed machine learning for improving the
accuracy of rapid Earth orientation parameters}



\author[1]{Mostafa Kiani Shahvandi}%
\author[2]{Robert Dill}%
\author[3]{Henryk Dobslaw}%
\author[4]{Alexander Kehm}%
\author[4]{Mathis Bloßfeld}%
\author[1]{Matthias Schartner}%
\author[5]{Siddhartha Mishra}%
\author[6]{Benedikt Soja}%
\affil[1]{Institute of geodesy and photogrammetry, ETH Zurich}%
\affil[2]{Deutsches GeoForschungsZentrum GFZ}%
\affil[3]{GeoForschungsZentrum Potsdam}%
\affil[4]{Deutsches Geodätisches Forschungsinstitut (DGFI-TUM), Technical University of Munich}%
\affil[5]{ETH Zurich}%
\affil[6]{Institute of Geodesy and Photogrammetry, ETH Zurich}%


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  \date{June 14, 2024}


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\begin{abstract}
Rapid provision of Earth Orientation Parameters (EOPs, here polar motion
and dUT1) is indispensable in many geodetic applications and also for
spacecraft navigation. There are, however, discrepancies between the
rapid EOPs and the final EOPs that have a higher latency, but the
highest accuracy. To reduce these discrepancies, we focus on a
data-driven approach, present a novel method named ResLearner, and use
it in the context of deep ensemble learning. Furthermore, we introduce a
geophysically-constrained approach for ResLearner. We show that the most
important geophysical information to improve the rapid EOPs is the
effective angular momentum functions of atmosphere, ocean, land
hydrology, and sea level. In addition, semi-diurnal, diurnal, and
long-period tides coupled with prograde and retrograde tidal excitations
are important features. The influence of some climatic indices on the
prediction accuracy of dUT1 is discussed and El
Ni\textbackslash{}\textasciitilde{}\{n\}o Southern Oscillation is found
to be influential. We developed an operational framework, providing the
improved EOPs on a daily basis with a prediction window of 63 days to
fully cover the latency of final EOPs. We show that under the
operational conditions and using the rapid EOPs of the International
Earth Rotation and Reference Systems Service (IERS) we achieve
improvements as high as 60\textbackslash{}\%, thus significantly
reducing the differences between rapid and final EOPs. Furthermore, we
discuss how the new final series IERS 20 C04 is preferred over 14 C04.
Finally, we compare against EOP hindcast experiments of European Space
Agency, on which ResLearner presents comparable improvements.%
\end{abstract}%



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