Data for publication of "Gaussian process regression-based Bayesian optimisation (G-BO) of model parameters - a WRF model case study of southeast Australia heat extremes"

Implementation of Gaussian process regression-based Bayesian optimisation (G-BO) using the emcee package (https://emcee.readthedocs.io/en/stable/).

For more information about the implementation of G-BO in optimising the Weather Research and Forecasting (WRF) model parameters, please refer to the paper - Gaussian process regression-based Bayesian optimisation (G-BO) of model parameters - a WRF model case study of southeast Australia heat extremes.

G-BO_script.ipynb implements the GPR-based Bayesian optimisation using the Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler.

QMC_sobol_samples: This file contains the 128 parameter samples across the parameter space of three sensitive parameters utilizing the Quasi Monte-Carlo (QMC) Sobol sequence design.

nmae_all_128_ens_T_Rh: This file contains the normalised mean absolute error (NMAE) values of temperature (T) and relative humidity (Rh) of the 128 parameter sample WRF simulations. For more details, please refer to this link.