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.

Highlights

  • Precipitation is a key meteorological variable and a major climate change indicator, directly affecting the energy and water exchanges between the biosphere and atmosphere

  • This study aims to improve our understanding of the suitability and uncertainty of satellite precipitation products in the Tianshan Mountains, and evaluates whether the upgrades in Global Precipitation Measurement (GPM) helped to enhance its capacity in capturing light precipitation and solid precipitation in the Tianshan Mountains of the Central Asia dryland

  • Previous evaluations in China indicated that the GPM products might perform worse than the Tropical Rainfall Measurement Mission (TRMM) products in the winter season [68,69], our study showed that the Integrated Multi-Satellite Retrievals for GPM (IMERG) outperformed the other two satellite products in all seasons and had more balanced seasonal performances

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Summary

Introduction

Precipitation is a key meteorological variable and a major climate change indicator, directly affecting the energy and water exchanges between the biosphere and atmosphere. Precipitation has more complex spatiotemporal patterns, which can only be accounted for with a dense network of rain gauge stations that are usually unavailable in a study area, in the remote mountains in developing countries [3,4]. Such data issues have seriously hindered ecological and hydrological studies in the Tianshan Mountains area, which is known as “the water tower” of Central Asia [5], the largest dryland and one of the most climate sensitive ecosystems in the world [6]. Widely used spatially interpolated precipitation datasets like the Climate Research Unit (CRU) are not reliable for hydrological/ecological research in both the Tianshan Mountains and the Central Asia dryland [9,12]

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