Assessment of WRF Model Parameter Sensitivity for High-Intensity Precipitation Events During the Indian Summer Monsoon

Default values for many 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. This study aims to identify the parameters that most strongly influence the model output variables using a Global Sensitivity Analysis (GSA) method. Morris One-At-a-Time (MOAT), a 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 11 output variables simulated by the WRF model, corresponding to different parameters. Twelve high-intensity 4-day precipitation events during the Indian summer monsoon during 2015, 2016, and 2017 over India's monsoon core region are considered for the study. Though the parameter sensitivities vary depending on the model output variable, overall results suggest a general trend. The consistency of sensitivity analysis results with different initial and lateral boundary conditions is also assessed.

Automated summary

This study aims to identify the parameters that most strongly influence the model output variables in the WRF model using Global Sensitivity Analysis (GSA) method. Morris One-At-a-Time (MOAT) is used to evaluate sensitivity of 23 chosen tunable parameters corresponding to seven physical parameterization schemes. The study considers high-intensity 4-day precipitation events during the Indian summer monsoon, and assesses the consistency of sensitivity analysis results with different initial and lateral boundary conditions.