<![CDATA[The performance of assimilating Himawari-8 satellite radiance data on a convection-permitting model (CPM) depends on the methods of assimilation. Hence, in this study, we compared the impact of different assimilation methods of Himawari-8 satellite radiance data on a CPM's prediction skills for the case of extreme rainfall in East Kalimantan, June 2nd – 4th, 2019. The study tested four schemes on the Weather Research and Forecasting (WRF) model at 3 km resolution, comparing a scheme without assimilation (NODA) and three schemes with different assimilation methods: 3DVAR, and Hybrid 3DEnVar (HYBRID, and DUALRES). Results showed that assimilation with hybrid 3DEnVar and 3DVAR techniques significantly improved the prediction skill of extreme rain, for instance, a 25% improvement of the true positive rate. The DUALRES scheme excelled in reducing biases in rainfall distribution. It was found that assimilation with the 3DEnVar method, particularly the DUALRES scheme, improved the prediction sensitivity to complex atmospheric dynamics, produced more accurate rainfall distribution and intensity, and improved the diurnal pattern of rainfall in East Kalimantan.]]>
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