Performance of IMERG V07B and V06B Monthly Rainfall Data in Capturing Seasonal Magnitude and Peak Timing over Indonesia

Abstract

Accurate and consistent rainfall data are essential for climatological analysis and disaster risk management, particularly in regions with complex climatic dynamics such as Indonesia. This study evaluates the performance of Integrated Multi-satellite Retrievals for GPM (IMERG) Final Run version 07B (V07B) against its predecessor, version 06B (V06B), in estimating seasonal rainfall magnitude and peak timing across Indonesia over a complete 20-year period (2001–2020). Results show that IMERG V07B demonstrates significant improvements in accuracy, with lower relative bias (RB) (V06B = +19.04%; V07B = +9.68%) and root mean square error (RMSE) (V06B = 109.92 mm/month; V07B = 102.65 mm/month), along with slightly higher correlation coefficient (CC) (V06B = 0.77; V07B = 0.78). These improvements are consistent across most stations and are attributed to updates in the Goddard profiling algorithm to account more effectively for surface type and orographic influence. Despite substantial differences in monthly rainfall magnitudes between the versions, particularly over oceanic, mountainous, and coastal regions, the annual cycle phase remains relatively consistent. Harmonic fitting further enhances peak timing accuracy, increasing the proportion of correct estimates from 62.69% (V06B) to 71.64% (V07B), underscoring the suitability of V07B for seasonal rainfall analysis. Spatial assessments reveal that V07B generally produces higher rainfall estimates than V06B over land and ocean but lower values in coastal zones. Based on these findings, IMERG V07B is recommended for seasonal zoning and rainfall pattern studies in Indonesia. Further research using sub-daily and near-real-time data is needed to support high-impact applications such as flood early warning systems.