Evaluation and Intercomparison of High-Resolution Satellite Precipitation Estimates—GPM, TRMM, and CMORPH in the Tianshan Mountain Area

Abstract

With high resolution and wide coverage, satellite precipitation products like Global Precipitation Measurement (GPM) could support hydrological/ecological research in the Tianshan Mountains, where the spatial heterogeneity of precipitation is high, but where rain gauges are sparse and unevenly distributed. Based on observations from 46 stations from 2014–2015, we evaluated the accuracies of three satellite precipitation products: GPM, Tropical Rainfall Measurement Mission (TRMM) 3B42, and the Climate Prediction Center morphing technique (CMORPH), in the Tianshan Mountains. The satellite estimates significantly correlated with the observations. They showed a northwest–southeast precipitation gradient that reflected the effects of large-scale circulations and a characteristic seasonal precipitation gradient that matched the observed regional precipitation pattern. With the highest correlation (R = 0.51), the lowest error (RMSE = 0.85 mm/day), and the smallest bias (1.27%), GPM outperformed TRMM and CMORPH in estimating daily precipitation. It performed the best at both regional and sub-regional scales and in low and mid-elevations. GPM had relatively balanced performances across all seasons, while CMORPH had significant biases in summer (46.43%) and winter (−22.93%), and TRMM performed extremely poorly in spring (R = 0.31; RMSE = 1.15 mm/day; bias = −20.29%). GPM also performed the best in detecting precipitation events, especially light and moderate precipitation, possibly due to the newly added Ka-band and high-frequency microwave channels. It successfully detected 62.09% of the precipitation events that exceeded 0.5 mm/day. However, its ability to estimate severe rainfall has not been improved as expected. Like other satellite products, GPM had the highest RMSE and bias in summer, suggesting limitations in its way of representing small-scale precipitation systems and isolated deep convection. It also underestimated the precipitation in high-elevation regions by 16%, suggesting the difficulties of capturing the orographic enhancement of rainfall associated with cap clouds and feeder–seeder cloud interactions over ridges. These findings suggest that GPM may outperform its predecessors in the mid-/high-latitude dryland, but not the tropical mountainous areas. With the advantage of high resolution and improved accuracy, the GPM creates new opportunities for understanding the precipitation pattern across the complex terrains of the Tianshan Mountains, and it could improve hydrological/ecological research in the area.

Added Value of a Dynamical Downscaling Approach for Simulating Precipitation and Temperature Over Tianshan Mountains Area, Central Asia

“…Crossing national boundaries of China and Kyrgyzstan, the range located in China is known as the "Eastern Tianshan" approximately 1,700 km in length and the outside range is so called "Western Tianshan" (Figure 1). Studies from water isotope measurements (Aizen et al, 1996;Aizen et al, 2006;Kreutz et al, 2003) and modeling approaches (Keys et al, 2014;Sun & Wang, 2014;Zhang et al, 2018) suggest a number of moisture sources for the Tianshan mountains area (Baldwin & Vecchi, 2016). Moisture follows the westerlies from Aral, Caspian, Black Sea, and Mediterranean as well as the Atlantic Ocean, all the way to the mountains particularly on the west side.…”

“…And as the first mountain system barrier confronting northern air masses moving into central Asia, the Tianshan mountains area affect the climatic processes occurring in northern central Asia, similar to that of the Himalayas to the south (Aizen et al, 1997). Due to its unique long latitudinal distribution and significant hydrothermal pattern adjustment for central Asia and for the world, there are lots of climatological (Baldwin & Vecchi, 2016;Cai et al, 2019;Zhang et al, 2009), ecological (Han et al, 2016;Zhang et al, 2013), and hydrological (Hagg et al, 2007;Zhang et al, 2018) studies focusing on the Tianshan mountains area.…”

Can the GPM IMERG Hourly Products Replicate the Variation in Precipitation During the Wet Season Over the Sichuan Basin, China?

“…Many studies have shown that the GPM products have improved in terms of detection rate and detection accuracy to a certain degree compared with TRMM in different geographic locations, such as the tropics (Tan & Duan, 2017; Tan & Santo, 2018), the subtropics (Z. Wang et al, 2017), and the midlatitude regions (Gebregiorgis et al, 2018; Tang et al, 2016) and with different underlying surface condition, such as in high mountain area (C. Zhang, Chen, et al, 2018), basin area (Jiang et al, 2018), plateau (S. Zhang, Wang, et al, 2018), and coastal area (Kim et al, 2017). In particular, the IMERG products are superior to TMPA in detecting precipitation of weak to moderate intensity (Muhammad et al, 2018; C. Zhang, Chen, et al, 2018). Compared to the TRMM, the GPM significantly widens the spatial coverage of the observations (from 35° to 65°), improves the detection accuracy of weak precipitation and snowfall processes (Ku band at 13.6 GHz and Ka band at 35.5 GHz), and boosts the ability to distinguish and identify liquid‐state and solid‐state precipitation (GPM Microwave Imager at 165.5 and 183.3 GHz) (Hou et al, 2014).…”

“…IMERG products have not been widely applied in meteorological or hydrological fields, and presently, several studies tend to concentrate on evaluating data reliability. Many studies have evaluated the application of IMERG‐F at different spatiotemporal scales (Asong et al, 2017; H. Guo et al, 2016; Mahmoud et al, 2019; Reddy et al, 2019; Tang et al, 2016; Wei et al, 2018) under different topographic and climate characteristics (Kim et al, 2017; C. Zhang, Chen, et al, 2018; Lu et al, 2019, 2018; Anjum et al, 2019), and in the hydrological field (Jiang et al, 2018; Ma et al, 2019; N. Li, Tang, et al, 2017; Z. Wang et al, 2017). According to reference standards such as ground rain gauges or weather radar inversion products (e.g., quantitative precipitation estimation) (N. Li, Wang, et al, 2017), these studies evaluate IMERG‐F solely or in conjunction with other satellite‐based precipitation data analysis products (e.g., TMPA, CMORPH, and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks).…”

Comprehensive Evaluation of Satellite‐Based Precipitation at Sub‐Daily Time Scales Over a High‐Profile Watershed with Complex Terrain

“…The accuracy of precipitation data has been validated in various areas (Jiang & Bauer-Gottwein, 2019;Tan & Duan, 2017;Tang et al, 2016) prior to further comparative studies with the TRMM products. Zhang, Chen, et al (2018) indicated that GPM outperformed TRMM in estimating daily precipitations over the Tianshan mountain range, China. showed that the enhancement of IMERG over TRMM is more significant at the 3-hr scale than at the daily scale in Guangdong Province, China.…”