Comparison of Correlation, PBIAS and RSR between Monthly, Daily, and Hourly GPM Rainfall Data - Yunita Ayu Setiyowati; Donny Harisuseno; M. Amar Sajali (Postgraduate, University of Mataram, 2025)
Accurate precipitation data is critical for hydrological modeling, flood forecasting, and water resources planning. This study evaluates the performance of satellite-based rainfall estimates from the Integrated Multi-satellite Retrievals for GPM (IMERG) Final Run Version 06 by comparing them with ground-based observations from six stations in the Jatigede Reservoir catchment, West Java, Indonesia. The analysis covers the 2014–2023 period, aligning with the reliable availability of IMERG Final Run products, and examines three temporal resolutions: monthly, daily, and hourly. Statistical evaluation employed Pearson correlation coefficient (r), the ratio of RMSE to observed standard deviation (RSR), and Percent Bias (PBIAS). Results show strong agreement at the monthly scale (r = 0.84, RSR = 0.34, PBIAS ≈ +24%), suggesting suitability for long-term water resource assessments. However, performance declines at shorter timescales. At the daily scale, IMERG underestimates rainfall (PBIAS ≈ -27%) with moderate correlation (r = 0.24). The hourly scale shows the poorest performance (r = 0.10, RSR > 3.0, PBIAS < -50%), indicating limitations in capturing short-duration, high-intensity rainfall typical in tropical regions. These findings underscore the importance of temporal aggregation and bias correction when applying IMERG data for operational hydrology and flood modeling