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\begin{document}

\title{A New Methodology to Process the Total Solar Irradiance observations
Using Machine Learning and Data Fusion}



\author[1]{Jean-Philippe Montillet}%
\author[1]{Wolfgang Finsterle}%
\author[1]{Werner Schmutz}%
\affil[1]{PMOD WRC Physical Meteorological Observatory Davos and World Radiation Center}%


\vspace{-1em}



  
  \date{May 24, 2024}


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\begin{abstract}
Across the last decades, various space missions have measured thetotal
solar irradiance (TSI) such as the Variability of Irradiance andGravity
Oscillations (VIRGO) experiment on the Solar and HeliosphericObservatory
(SOHO) starting in 1996. Since the beginning of itsrecording time, one
challenge is to correct the measurements from thedegradation of the TSI
sensors in space. Various groupshave proposed different methodologies to
produce a continuous TSI timeseries (TSI composite) which is essential
to monitor the sun activityand its influence on the Earth's climate.
However, the benchmark to test all those solutions is source of adebate
in the community. Moreover, the input data for the TSI compositeare the
degradation-corrected measurements provided by each individualinstrument
team. Here, we propose a different approach using amachine learning and
data fusion algorithm to produce automatically thedegradation-corrected
TSI time series based on a small number of genericassumptions. The
algorithm is applied to the VIRGO/PMO6, VIRGO/DIARADand PREMOS/PMO6
data. The time series agree between each other in termsof mean value
with a difference of \textasciitilde{} 0.14 W/m2 (PREMOS),
\textasciitilde{} 0.23 W/m2(VIRGO) and \textasciitilde{} -0.18 W/m2
(DIARAD). Finally, taking a conservative valueof 0.3 W/m2 between our
different TSI products, induces a variation ofthe global mean surface
temperature of \textasciitilde{} 0.02 K based on global
climatesimulations, which is within the uncertainties of simulated
global meansurface temperatures, hence not impacting significantly any
climateforcing scenarios.%
\end{abstract}%



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