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\begin{document}

\title{Simultaneous inference of sea ice state and surface emissivity model
using machine learning and data assimilation}



\author[1]{Alan Jon Geer}%
\affil[1]{European Centre for Medium Range Weather Forecasts}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\begin{abstract}
Satellite microwave radiance observations are strongly sensitive to sea
ice, but physical descriptions of the radiative transfer of sea ice and
snow are incomplete. Further, the radiative transfer is controlled by
poorly-known microstructural properties that vary strongly in time and
space. A consequence is that surface-sensitive microwave observations
are not assimilated over sea ice areas, and sea ice retrievals use
heuristic rather than physical methods. An empirical model for sea ice
radiative transfer would be helpful but it cannot be trained using
standard machine learning techniques because the inputs are mostly
unknown. The solution is to simultaneously train the empirical model and
a set of empirical inputs: an ``empirical state'' method, which draws on
both generative machine learning and physical data assimilation
methodology. A hybrid physical-empirical network describes the known and
unknown physics of sea ice and atmospheric radiative transfer. The
network is then trained to fit a year of radiance observations from
Advanced Microwave Scanning Radiometer 2 (AMSR2), using the atmospheric
profiles, skin temperature and ocean water emissivity taken from a
weather forecasting system. This process estimates maps of the daily sea
ice concentration while also learning an empirical model for the sea ice
emissivity. The model learns to define its own empirical input space
along with daily maps of these empirical inputs. These maps represent
the otherwise unknown microstructural properties of the sea ice and snow
that affect the radiative transfer. This ``empirical state'' approach
could be used to solve many other problems of earth system data
assimilation.%
\end{abstract}%



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