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\begin{document}

\title{Gaussian process regression-based Bayesian optimisation (G-BO) of model
parameters - a WRF model case study of southeast Australia heat extremes}



\author[1]{P. Jyoteeshkumar Reddy}%
\author[2]{Sandeep Chinta}%
\author[3]{Harish Baki}%
\author[4]{Richard J. Matear}%
\author[5]{John Taylor}%
\affil[1]{CSIRO Australia}%
\affil[2]{Massachusetts Institute of Technology}%
\affil[3]{Delft University of Technology}%
\affil[4]{CSIRO Marine and Atmospheric Research}%
\affil[5]{Australian National University}%


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  \date{May 21, 2024}


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\begin{abstract}
In Numerical Weather Prediction (NWP) models, such as the Weather
Research and Forecasting (WRF) model, parameter uncertainty in physics
parameterization schemes significantly impacts model output. Our study
adopts a Bayesian probabilistic approach, building on prior research
that identified temperature (T) and relative humidity (Rh) as sensitive
to three key WRF parameters during southeast Australia's extreme heat
events. Using Gaussian process regression-based Bayesian Optimisation
(G-BO), we accurately estimated the optimal distributions for these
parameters. Results show that the default values are outside their
corresponding optimal distribution bounds for two of the three
parameters, suggesting the need to reconsider these default values.
Additionally, the robustness of the optimal parameter distributions is
validated by their application to an independent extreme heat event, not
included in the optimisation process. In this test, the optimised
parameters substantially improved the simulation of T and Rh,
highlighting their effectiveness in enhancing simulation accuracy during
extreme heat conditions.%
\end{abstract}%



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