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\begin{document}

\title{Exploring Bayesian deep learning for weather forecasting with the Lorenz
84 system}



\author[1]{Yang Liu}%
\author[2]{Jisk Jakob Attema}%
\author[3]{Wilco Hazeleger}%
\affil[1]{Netherlands eScience Center / Wageningen University}%
\affil[2]{Netherlands eScience Center}%
\affil[3]{Utrecht University}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\begin{abstract}
The need for uncertainty quantification placed by weather forecasting
makes Bayesian deep learning (BDL) a suited candidate for data-driven
weather forecasting. In this study, we use Bayesian Long-Short Term
Memory neural networks (BayesLSTMs) to forecast output from the Lorenz
84 system with seasonal forcing. The latter represents the dynamics of
large scale eddies (Rossby waves) on a westerly jet. We show that
forecasts with the BayesLSTM can stay close to the attractor of the
Lorenz model and conclude that they represent the nonlinear relations
between each component in this simplified atmospheric circulation
system. The forecasts are evaluated against persistence and a Vector
Autoregressive Model (VAR). We demonstrate that the BayesLSTMs can
produce reliable probabilistic forecasts and address uncertainties
relevant to weather forecasting. Our study indicates that BDL is an easy
and fast solution for probabilistic weather forecast and is promising to
enhance weather forecasting capabilities at short to medium-range
timescales.%
\end{abstract}%



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