Gaussian Process Regression-Based Bayesian Optimisation (G-BO) of Model Parameters—A WRF Model Case Study of Southeast Australia Heat Extremes
P. Jyoteeshkumar Reddy (CSIRO: Commonwealth Scientific and Industrial Research Organisation)
Sandeep Chinta (Massachusetts Institute of Technology)
Harish Baki (TU Delft - Atmospheric Remote Sensing)
Richard Matear (CSIRO: Commonwealth Scientific and Industrial Research Organisation)
John Taylor (Australian National University, CSIRO: Commonwealth Scientific and Industrial Research Organisation)
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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 optimized parameters substantially improved the simulation of T and Rh, highlighting their effectiveness in enhancing simulation accuracy during extreme heat conditions.
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