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\begin{document}

\title{Deep learning to estimate model biases in an operational NWP
assimilation system}



\author[1]{Patrick Laloyaux}%
\author[2]{Thorsten Kurth}%
\author[1]{Peter Dominik Dueben}%
\author[3]{David Hall}%
\affil[1]{ECMWF}%
\affil[2]{Nvidia Corporation}%
\affil[3]{NVIDIA}%


\vspace{-1em}



  
  \date{May 14, 2024}


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\begin{abstract}
Model error is one of the main obstacles to improved accuracy and
reliability in numerical weather prediction (NWP) conducted with
state-of-the-art atmospheric models. To deal with model biases, a
modification of the standard 4D-Var algorithm, called weak-constraint
4D-Var, has been developed where a forcing term is introduced into the
model to correct for the bias that accumulates along the model
trajectory. This approach reduced the temperature bias in the
stratosphere by up to 50\% and is implemented in the ECMWF operational
forecasting system.\\
Despite different origins and applications, Data Assimilation and Deep
Learning are both able to learn about the Earth system from
observations. In this paper, a deep learning approach for model bias
correction is developed using temperature retrievals from Radio
Occultation (RO) measurements. Neural Networks require a large number of
samples to properly capture the relationship between the temperature
first-guess trajectory and the model bias. As running the IFS data
assimilation system for extended periods of time with a fixed model
version and at realistic resolutions is computationally very expensive,
we have chosen to train, the initial Neural Networks are trained using
the ERA5 reanalysis before using transfer learning on one year of the
current IFS model. Preliminary results show that convolutional neural
networks are adequate to estimate model bias from RO temperature
retrievals. The different strengths and weaknesses of both deep learning
and weak constraint 4D-Var are discussed, highlighting the potential for
each method to learn model biases effectively and adaptively.%
\end{abstract}%



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