  Default values for many model parameters in Numerical Weather Prediction
models are typically adopted based on theoretical or experimental
investigations by scheme designers. Short-range forecasts are substantially
affected by the specification of parameters in the Weather Research and
Forecasting (WRF) model. The presence of a multitude of parameters and several
output variables in the WRF model renders appropriate parameter value
identification quite challenging. The objective of the current study is to
reduce the uncertainty in the model outcomes through the recognition of
parameters that strongly influence the model performance using a Global
Sensitivity Analysis (GSA) method. Morris one-step-at-a-time (MOAT), GSA
method, is used to identify the sensitivities of 23 chosen tunable parameters
corresponding to seven physical parameterization schemes of the WRF model. The
sensitivity measures (MOAT mean and standard deviation) are evaluated for
eleven output variables, out of which some are surface meteorological variables
and the remaining are atmospheric variables, which are simulated by the WRF
model with different parameters. Twelve high-intensity four day precipitation
events during the Indian summer monsoon (ISM) for the years 2015, 2016, and
2017 over the monsoon core region in India are considered for the study. Six
out of 23 parameters have high MOAT mean in the case of almost all the model
output variables indicating that these parameters have a considerable effect on
the outcome of the simulations. MOAT mean values of a few parameters are
noticeably small for all the model output variables, and thus the uncertainty
associated with them has a negligible effect on the WRF model performance. The
study also presents the physical insights into the trends of the parameter
sensitivity.
