WRF-MODEL PARAMETERIZATION TEST FOR PREDICTING EXTREME HEAVY RAINFALL EVENT OVER KETAPANG REGENCY

Abstract

Heavy rains that cause floods and landslides in the Ketapang Regency can be predicted by utilizing the weather research and forecast (WRF) model. The WRF model used, of course, needs to be configured to represent the conditions that exist in Ketapang Regency. This study evaluates the combination of cumulus and microphysics parameterization, producing the best prediction of 24-hour accumulated rainfall. The combination of cumulus and microphysics parameterization tested as many as 24 schemes which later will be obtained which combination can produce the best prediction of rainfall accumulation with the comparison of rainfall measured at the Observation Station of the Meteorology, Climatology, and Geophysics Agency (BMKG) in Ketapang Regency. The results show that combining the KF-Scheme cumulus parameterization scheme and the Kessler-Scheme microphysics can better predict 24-hour accumulated rainfall than other tested parameterization schemes. This result is based on the root mean square error (RMSE), which shows that this combination scheme produces the smallest value and large correlation coefficient (CORR). From this research, it can also be seen that cumulus parameterization has a more dominant role than microphysics parameterization.