Statistical Comparison of IMERG Precipitation Products with Optical Rain Gauge Observations over Kototabang, Indonesia

Abstract

Satellite-based precipitation estimates play a crucial role in many hydrological and numerical weather models, especially to overcome the scarcity of rain gauge data. Globally gridded rainfall product from Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) has been used in a wide range of hydrological applications. However, the IMERG is inherently prone to errors and biases. This study evaluated the performance of the IMERG-Final run (IMERG-F) product to estimate rainfall in a mountainous area of Sumatra. Validation was carried out using optical rain gauge (ORG) data for 15 years (2002-2016), at Kototabang, West Sumatra, Indonesia. In general, IMERG-F overestimated rainfall in all time scales. The longer the time scale was, the better the performance of IMERG-F we obtained. This feature was indicated by all quantities of continuous and categorical statistical matrices used. The performance of IMERG-F was lower than in other areas of the Maritime Continent, except for the probability of detection (POD) value. IMERG-F could detect rain very well, including for daily and hourly data, but the false alarm rate (FAR) was also relatively high. Such high FAR value may indicate a significant small-scale spatial rainfall variability in mountainous area of Sumatra.

Evaluation of GPM IMERG Performance Using Gauge Data over Indonesian Maritime Continent at Different Time Scales

“…The station ID (located above sea level) as shown in Figure 2 were 96071 (328 m above sea level/ASL), 96581 (2 m ASL), 150010 (43 m ASL), and 150146 (184 m ASL). Rain gauge (AWS and MRG) and IMERG captured hourly and daily precipitation temporal trend, which is consistent with a previous study in West Sumatra [42].…”

“…Although most stations showed that IMERG overestimated rainfall, 35.4% of MRG and 41.8% of AWS stations showed that IMERG underestimated it. The dominance of the overestimated value from the observations of hourly rainfall found in this study was consistent with the pattern of overestimation often found from IMERG observations [15,42,67,68]. From all continuous statistical quantities, it can be seen that the accuracy of hourly rainfall observations from IMERG at IMC still needs to be improved.…”

“…This high FAR value can be caused by the high variability of rainfall in an IMERG grid, so that one point observation may not be adequate to represent rainfall from the grid. High FAR value can also contribute to the high overestimation of the IMERG data compared to the rain gauge [42]. However, for extreme rain events, which are especially associated with short-term and limited spatial extent events (few km 2 ), IMERG underestimates rainfall [43,47].…”

“…Additionally, the temporal resolution of the data validated in the previous study was daily. IMERG V06 validation for one observation point in Sumatra has been carried out by Helmi et al [42] and Ramadhan et al [43]. Thus, the existing validation activities are not able to assess the overall IMERG performance for the IMC area; a comprehensive validation for the entire IMC area is timely.…”

Validation of TRMM and GPM Satellite Data Using Daily Precipitation Observations

“…Multiple studies conducted in Indonesia have focused on validating satellite precipitation products for rainfall predictions [34], [40], [41], Pratiwi et al [42] conducted research where they evaluated TRMM 3B42, TRMM 3B42RT, GPM, and PERSIANN CCS satellite data. The study revealed that the GPM satellite provided good results for predicting observed rainfall data in the Dengkeng watershed, specifically for the daily period, with a correlation coefficient of 0.66.…”