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\begin{document}

\title{Postprocessing East African rainfall forecasts using a generative
machine learning model}



\author[1]{Bobby Antonio}%
\author[1]{Andrew T T McRae}%
\author[2]{David MacLeod}%
\author[1]{Fenwick C Cooper}%
\author[3]{John Marsham}%
\author[4]{Laurence Aitchison}%
\author[5]{Tim N Palmer}%
\author[4]{Peter A G Watson}%
\affil[1]{University of Oxford}%
\affil[2]{Cardiff University}%
\affil[3]{University of Leeds}%
\affil[4]{University of Bristol}%
\affil[5]{Oxford University}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\begin{abstract}
Existing weather models are known to have poor skill at forecasting
rainfall over East Africa, where there are regular threats of drought
and floods. Improved forecasts could reduce the effects of these extreme
weather events and provide significant socioeconomic benefits to the
region. We present a novel machine learning-based method to improve
precipitation forecasts in East Africa, using postprocessing based on a
conditional generative adversarial network (cGAN). This addresses the
challenge of realistically representing tropical rainfall, where
convection dominates and is poorly simulated in conventional global
forecast models. We postprocess hourly forecasts made by the European
Centre for Medium-Range Weather Forecasts Integrated Forecast System at
6-18h lead times, at \$0.1\^{}\{\textbackslash{}circ\}\$ resolution. We
combine the cGAN predictions with a novel neighbourhood version of
quantile mapping, to integrate the strengths of machine learning and
conventional postprocessing. Our results indicate that the cGAN
substantially improves the diurnal cycle of rainfall, and improves
predictions up to the \$99.9\^{}\{\textbackslash{}text\{th\}\}\$
percentile (\$\textbackslash{}sim 10
\textbackslash{}text\{mm\}/\textbackslash{}text\{hr\}\$). This
improvement extends to the 2018 March--May season, which had extremely
high rainfall, indicating that the approach has some ability to
generalise to more extreme conditions. We explore the potential for the
cGAN to produce probabilistic forecasts and find that the spread of this
ensemble broadly reflects the predictability of the observations, but is
also characterised by a mixture of under- and over-dispersion. Overall
our results demonstrate how the strengths of machine learning and
conventional postprocessing methods can be combined, and illuminate what
benefits machine learning approaches can bring to this region.%
\end{abstract}%



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