Preliminary Assessment of GPM Satellite Rainfall over Myanmar

Abstract

Intensive and long-term rainfall in Myanmar causes floods and landslides that affect thousands of people every year. However, the rainfall observation network is still limited in number and extent, so satellite rainfall products have been shown to supplement observations over the ungauged areas. One example is the estimates from Global Precipitation Measurement (GPM) called Integrated Multi-satellite Retrievals for GPM (IMERG), which has high spatial (0.1 × 0.1 degree) and temporal (30 min) resolution. This has potential to be used for modeling streamflow, early warnings, and forecasting systems. This study investigates the utility of these GPM satellite estimates for representing the daily rainfall for 25 rain gauges over Myanmar. Statistical metrics were used to understand the characteristic performance of the GPM satellite estimates. Daily rainfall estimates from GPM show a range of 29.3% to 81.1% probability of detection (POD). The satellite estimates show a capability of detecting no-rain days between 61.4 and 93.5%. For different rainfall intensities, the satellite estimates have a 12.9 to 39.1% POD for light rain (1–10 mm/day), 11.1 to 49% POD for moderate rain (10–50 mm/day), a maximum of 36% for heavy rain (50–150 mm/day), and a maximum of 12.5% for extreme rain (=150 mm/day). However, the correlation coefficient (CC) only ranges from 0.064 to 0.581, which is considered low, and is not uniform for all the stations. The highest CC scores and POD scores tend to be located in the northern part and deltaic region extending to the southern coasts in Myanmar, indicating a dependency of the statistical metrics on rainfall magnitude. The high POD scores indicate the utility of the estimates without correction for early warning purposes, but the estimates have low reliability for rainfall intensity. The satellite estimates can be used for forecasting and modeling purposes in the region, but the estimates require bias-correction before application.

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

“…This was consistent with previous research in Bali showing a better correlation in the dry season in comparison to the rainy season [40]. Other observations in Myanmar [72] and Mekong River [74] also found similar results. Furthermore, the lowest correlation was shown in the DJF season (Figure 6a).…”

“…The same pattern was also found in the Maritime Continent region, such as in Singapore [25] and Malaysia [37] for IMERG-F. The patterns of overestimation of monthly rainfall were also found in areas near the Maritime Continent, such as in Myanmar [72] and Thailand [73]. However, in Malaysia the IMERG-E and IMERG-L data estimated lower rainfall than the rain gauges [37].…”

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

“…The highest correlation was found in the JJA season (Figure 6c). This result is consistent with previous research in Bali, which found a better CC during the dry season compared to the rainy season, such as in Bali (Liu et al, 2020), East Asia (Lee et al, 2019), Myanmar (Mohsan et al, 2018), and the Mekong River (Wang et al, 2017). Meanwhile, a low CC was found during the DJF season (Figure 6a).…”

The Extreme Rainfall Events of the 2020 Typhoon Season in Vietnam as Seen by Seven Different Precipitation Products

“…However, their accuracy diminished considerably when applied to decadal and daily values [10]. Various validation studies conducted in Southeast Asia have examined the compatibility between daily satellite products and rain gauge data [18,19]. These studies highlight the potential benefits of implementing bias correction techniques to enhance the quantitative alignment of rain rate values.…”