学术文献库

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.