Validation of TRMM and GPM Satellite Data Using Daily Precipitation Observations

Rafika Andari (1), Nurhamidah (2), Darwizal Daoed (3), Marzuki (4)
(1) Department of Civil Engineering, Faculty of Engineering, Andalas University, Padang 25163, Indonesia
(2) Department of Civil Engineering, Faculty of Engineering, Andalas University, Padang 25163, Indonesia
(3) Department of Civil Engineering, Faculty of Engineering, Andalas University, Padang 25163, Indonesia
(4) Department of Physics, Faculty of Mathematical and Natural Sciences, Andalas University, Padang, 25163, Indonesia
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How to cite (IJASEIT) :
Andari, Rafika, et al. “Validation of TRMM and GPM Satellite Data Using Daily Precipitation Observations”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 2, Apr. 2024, pp. 555-62, doi:10.18517/ijaseit.14.2.18980.
Accurate precipitation data holds immense significance in hydrological analysis. A common challenge in this field often stems from the lack of comprehensive data availability. High-resolution satellite-based precipitation measurements covering large areas offer a potential solution. However, disparities in the resolution of observed rainfall data can impact data accuracy. The main goal of this study is to evaluate the accuracy of rainfall data obtained from the TRMM and GPM satellites in the Kuranji watershed. The evaluation was conducted on the performance of the GPM IMERG-F from the Integrated Multi-satellite Retrievals for the GPM mission and the TRMM 3B42RT on a daily scale spanning from 2015 to 2019 over the Kuranji watershed. The daily precipitation measurements were validated using three widely used statistical metrics (R, RMSE, and RB). The precipitation detection capability (POD, FAR, and CSI) was also considered in this assessment. The findings demonstrate that both satellite estimations exhibit a substantial correlation coefficient (0.68 for GPM, 0.62 for TRMM) with the measurements obtained from gauges, along with an inclination to overestimate precipitation. GPM IMERG-F and TRMM 3B42RT manifest a consistent spatial pattern in daily precipitation distribution, effectively representing the observed precipitation distribution. The greater probability of detection (POD), critical success index (CSI), and lower false alarm ratio (FAR) exhibited by GPM IMERG-F at varying rainfall intensities suggests its superior performance in accurately identifying observed precipitations. This finding supports the preference for GPM IMERG-F data over TRMM 3B42RT data across various applications, hydrology, and related disciplines in the future.

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