<![CDATA[Flooding is a recurrent hydrometeorological hazard that occurs when rainfall intensity exceeds river channel capacity within a watershed. In the Lombok River Basin, limited rain-gauge density hampers the detection of localized high-intensity rainfall that can trigger flood events. This study develops a satellite-based rainfall estimation model using Himawari-8 infrared Cloud Top Temperature (CTT) integrated with surface atmospheric parameters, including relative humidity (RH), zonal and meridional wind components (u and v), and surface air pressure (P). Hourly rainfall observations from 15 rain gauges were used for site-specific calibration during two major flood events (6 December 2021 and 17 June 2022). A local nonlinear exponential regression model was fitted for each station using the Non-Linear Least Squares (NLLS) method, and model performance was evaluated using R², Nash–Sutcliffe Efficiency (NSE), RMSE, and RSR. Results indicate that thermodynamic predictors, particularly CTT and RH, provide the strongest empirical relationships with rainfall variability, while wind components contribute weaker at the statistical level. Overall performance varied spatially across stations, reflecting local terrain and microclimate effects. The proposed framework supports improved rainfall characterization in tropical island basins and can be adapted to other regions with appropriate local calibration.]]>
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