<![CDATA[There were two cases of heavy rainfall that occurred at Samarinda on 27 and 28 November 2016 with the accumulation of rainfall recorded at Temindung-Samarinda Meteorological Station were 73,7 mm/day and 72,6 mm/day. One of the ways to improve the prediction accuracy level given by performing the data assimilation to improve the initial conditions of the model. This research used Final Analysis (FNL) data, Global Forecast System (GFS) data, synoptic observation data, and Global Positioning System Radio Occultation (GPS-RO) Refractivity, where the GPS data will be assimilated into the WRF-ARW through WRF 3DVAR technique. Besides that, this research is also applying the spin-up model procedure with warm start mode which is accompanied by a rapid update cycle forecast in order to know the performance of WRF-ARW in predicting heavy rainfall phenomenon. The result of this research shows that applying the data assimilation procedure of the GPS-RO Refractivity which goes into WRF-ARW model can increase the predictions accurate level of heavy rainfall phenomenon which is occurred at that time. As for the high and low of the prediction, the result is affected by the length of time span prediction of the rain phenomenon. The length of time span prediction of the rain phenomenon, the better the rain prediction result generated by using WRF-ARW model.]]>
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