Performance of Different Parameterization Configurations of WRF-ARW Model during Heavy Rainfall over Mi Oya River Basin

Short-term intense precipitation is one of the hallmarks climate change. Mi Oya River basin experiences severe seasonal floods annually, but damage can be lessened by developing a numerical weather forecasting (NWF) model for entire and incorporating it with effective reservoir management. Several areas in Sri Lanka have undergone NWF studies, however they are insufficient to determine best physics schemes basin. This study investigates Weather Research Forecasting (WRF-ARW) model's predictability varying three microphysics two cumulus discover optimal set parameters predicting heavy rainfall occurrences throughout Southwest Northeast monsoon seasons within nested domain configuration. The WRF results at 3 km grid resolution were compared four gauging stations events May 2016, April 2018, November 2015. Total Model Performance was derived evaluation utilizing bias, MAE, RMSE, Correlation Coefficient, slope each output data observed data. After comparing data, WSM6 Betts-Miller-Janjic other default settings determined combination forecast across region.