Preliminary Evaluation of GPM‐IMERG Rainfall Estimates Over Three Distinct Climate Zones With APHRODITE

Abstract

Evaluation of Global Precipitation Measurement‐Integrated Multi‐satellitE Retrieval for GPM (GPM‐IMERG) final precipitation product is performed over Japan, Nepal, and Philippines regions against further improved APHRODITE‐2 V1801R1 product. The evolution is carried out for nearly two consecutive years 2014–2015. Various qualitative and quantitative statistical indices such as mean bias, root‐mean‐square error, correlation coefficient, false alarming ratio, and probability of detection are considered to evaluate GPM‐IMERG precipitation estimates with APHRODITE‐2. Intraseasonal variability of two products is shown to explore the seasonal dependency of GPM‐IMERG performance. The performance of GPM‐IMERG research product with respect to rainfall intensity is shown by the cumulative probability distribution of target and reference data sets. Percentile‐based statistics is implemented for evaluating the advantages of GPM‐IMERG over Tropical Rainfall Measuring Mission‐3B42 while detecting the light and heavy rainfall events during wet/dry seasons. The overall performance of GPM‐IMERG seems to be good over Japan followed by Philippines and Nepal regions. This feature is clearly evidenced in terms of mean bias, root‐mean‐square error, and correlation magnitudes over three regions. GPM‐IMERG shows ability to follow the intraseasonal variability as shown by APHRODITE‐2 product with minor differences observed in precipitation maximum values during rainy season. Good agreement is seen between GPM‐IMERG and APHRODITE‐2 at different rainfall intensities except underestimation during heavy rainfall events. GPM‐IMERG seems to be improved in detecting light/heavy rainfall event magnitude than TRM‐3B42. However, the performance of both data sets encountered clear dependency on seasons.

Validation of GPM IMERG V05 and V06 Precipitation Products over Iran

“…4b, 5, and 6 and Table 7). Xu et al (2017), Anjum et al (2018), Sunilkumar et al (2019), and Islam (2018) reported similar results over southern Tibetan Plateau, northern Pakistan, Japan and Nepal, and Bangladesh, respectively. This overestimation is higher for early and late runs compared to the final runs, especially for V06.…”

How Accurately Can Satellite Products (TMPA and IMERG) Detect Precipitation Patterns, Extremities, and Drought Across the Nepalese Himalaya?

“…A study over multiple complex terrain regions found that GSMaP V07 was able to detect the orographic rainfall and the rainfall amounts and outperforms IMERG V06B in Nepal with evaluation against limited stations (Derin et al, 2019), whereas evaluation based on large‐scale station network revealed that GSMaP‐Gauge product was more consistent with reproducing spatial pattern while IMERG product was more reasonable to reproduce precipitation amount over the country (Sharma, Chen, et al, 2020). Similarly, Sunilkumar et al (2019) found that IMERG product was reliable to estimate rainfall amounts as that in APHRODITE over Asian countries, including Nepal. However, most of the abovementioned global as well as regional studies suggested for continuous evaluation of SBPP for complex topography—still needed to further advance the product algorithms (Chen & Li, 2016; Lu & Yong, 2018; Saber & Yilmaz, 2018; Wang & Yong, 2020; Wang, Zhang, & Zhang, 2019).…”

Assessment of Precipitation Estimation from the NWP Models and Satellite Products for the Spring 2019 Severe Floods in Iran

“…Then the interpolated precipitation at each station was In this study, the evaluations were conducted in three steps. In the first step, the performance of the three NWP models and SPEs in terms of capturing the spatial distribution of precipitation for the three flood events (17)(18)(19)(20)(21)(22)(24)(25)(26) March, and 31 March to 2 April 2019, respectively) were compared. The numerical forecast and SPE data have a 50 × 50 km and~10 × 10 km spatial resolution, respectively.…”

“…With respect to extreme precipitation events, Fang et al indicated that although IMERG well captured the spatial pattern of extreme precipitation over China, the topography and climate condition had a significant influence on its performance [24]. In another study, Sunikumar et al demonstrated the ability of IMERG to follow the intraseasonal variability with minor differences observed in the maximum values of precipitation during the rainy season over Japan, Philippines, and Nepal [25]. Mazzoglio et al improved an extreme precipitation detection system using IMERG data and stated that this product guarantees good results when the precipitation aggregation interval is equal to or greater than 12 h [26].…”

“…In this study, the performance of ensemble precipitation forecasts of three numerical weather prediction (NWP) models within the THORPEX interactive grand global ensemble (TIGGE) as well as the integrated multi-satellite retrievals for global precipitation measurement (GPM), namely IMERG, for precipitation estimates were evaluated in recent severe floods in Iran over the March-April 2019 period. The evaluations were conducted in three aspects: spatial distribution of precipitation, mean areal precipitation in three major basins hard hit by the floods, and the dichotomous evaluation in four precipitation thresholds (25, 50, 75, and 100 mm per day). The results showed that the United Kingdom Met Office (UKMO) model, in terms of spatial coverage and satellite estimates as well as the precipitation amount, were closer to the observations.…”