Performance Analysis of Satellite-Derived Precipitation Data in Aceh, Indonesia

Abstract

Accurate precipitation estimation is crucial for effective water resource management, disaster mitigation, and hydrological studies, particularly in regions with limited ground-based observations, such as Aceh Province, Indonesia. This study evaluates the performance of five satellite-based precipitation products (SPPs) by comparing their daily, monthly, and annual estimates against ground-based measurements. The selected products are Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Climate Prediction Center MORPHing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Tropical Rainfall Measuring Mission (TRMM). Performance was evaluated using statistical metrics, including correlation coefficient, root mean square error (RMSE), mean absolute error (MAE), and relative bias (RBS), along with categorical metrics such as probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), and frequency bias (Fbias). At the daily scale, CHIRPS achieved the best performance with an RMSE of 15.03 mm/day, followed by MSWEP (RMSE: 17.34 mm/day). CMORPH and PERSIANN showed moderate performance, while TRMM had the highest RMSE (23.46 mm/day). For categorical metrics, MSWEP excelled at higher precipitation thresholds, indicating its suitability for detecting heavy rainfall events. At monthly and annual scales, MSWEP consistently outperformed other SPPs, exhibiting the highest correlation and lowest error metrics. CHIRPS also demonstrated good performance but with slightly higher RMSE and bias. TRMM and PERSIANN underperformed, especially in capturing heavy rainfall, with notable biases and higher errors. Based on these findings, MSWEP is recommended for hydrological modeling and flood forecasting, while CHIRPS is more suitable for long-term climatological applications. CMORPH and PERSIANN may benefit from calibration to improve their ability to detect heavy rainfall events.