  The present study focuses on identifying the parameters from the Weather
Research and Forecasting (WRF) model that strongly influence the prediction of
tropical cyclones over the Bay of Bengal (BoB) region. Three global sensitivity
analysis (SA) methods namely the Morris One-at-A-Time (MOAT), Multivariate
Adaptive Regression Splines (MARS), and surrogate-based Sobol' are employed to
identify the most sensitive parameters out of 24 tunable parameters
corresponding to seven parameterization schemes of the WRF model. Ten tropical
cyclones across different categories, such as cyclonic storms, severe cyclonic
storms, and very severe cyclonic storms over BoB between 2011 and 2018, are
selected in this study. The sensitivity scores of 24 parameters are evaluated
for eight meteorological variables. The parameter sensitivity results are
consistent across three SA methods for all the variables, and 8 out of the 24
parameters contribute 80%-90% to the overall sensitivity scores. It is found
that the Sobol' method with Gaussian progress regression as a surrogate model
can produce reliable sensitivity results when the available samples exceed 200.
The parameters with which the model simulations have the least RMSE values when
compared with the observations are considered as the optimal parameters.
Comparing observations and model simulations with the default and optimal
parameters shows that predictions with the optimal set of parameters yield a
19.65% improvement in surface wind, 6.5% in surface temperature, and 13.2% in
precipitation predictions, compared to the default set of parameters.
