Evaluation and Comparison of Daily GPM/TRMM Precipitation Products over the Tianshan Mountains in China

Yin Zhang,Zhiyuan Xu,Gulimire Hanati,Sulitan Danierhan,Qianqian Liu

doi:10.3390/w12113088

Abstract

Based on the complex topography and climate conditions over the Tianshan Mountains (TSM) in Xinjiang, China, the new precipitation product, the Global Precipitation Measurement (GPM) (IMERG), and its predecessor, the Tropical Rainfall Measuring Mission (TRMM) 3B42 (TMPA), were evaluated and compared. The evaluation was based on daily-scale data from April 2014 to March 2015 and analyses at annual, seasonal and daily scales were performed. The results indicated that, overall, the annual precipitation in the Tianshan area tends to be greater in the north than in the south and greater in the west than in the east. Compared with the ground reference dataset, GPM and TRMM datasets represent the spatial variation of annual and seasonal precipitation over the TSM well; however, both measurements underestimate the annual precipitation. Seasonal analysis found that the spatial variability of seasonal precipitation has been underestimated. For the daily assessment, the coefficient of variation (CV), correlation coefficient (R) and relative bias (RB) were calculated. It was found that the GPM and TRMM data underestimated the larger CV. The TRMM data performed better on the daily variability of precipitation in the TSM. The R and RB data indicate that the performance of GPM is generally better than that of TRMM. The R value of GPM is generally greater than that of TRMM, and the RB value is closer to 0, indicating that it is closer to the measured value. As for the ability to detect precipitation events, the GPM products have significantly improved the probability of detection (POD) (POD values are all above 0.8, the highest is 0.979, increased by nearly 17%), and the critical success index (CSI) (increased by nearly 9% in the TSM) is also better than TRMM, although it is only slightly weaker than TRMM in terms of the false alarm ratio (FAR) and frequency bias index (FBI). Overall, GPM underestimates the low rainfall rate by 6.4% and high rainfall rate by 22.8% and overestimates middle rain rates by 29.1%. However, GPM is better than TRMM in capturing all types of rainfall events. Based on these results, GPM-IMERG presents significant improvement over its predecessor TRMM 3B42. Considering the performance of GPM in different subregions, a lot of work still needs to be done to improve the performance of the satellite before being used for research.

Highlights

Precipitation is a fundamental component of the global water cycle
It can be seen that the Global Precipitation Measurement (GPM)-IMERG and Tropical Rainfall Measuring Mission (TRMM) datasets show obvious spatial differences in the precipitation estimation of Tianshan Mountains (TSM), with greater precipitation in the north than the south and in the west than the east (Figure 2)
Based on daily-scale data, this study evaluated the GPM/TRMM products over the TSM from 1

Summary

Introduction

Precipitation is a fundamental component of the global water cycle. It provides important information for climate research, water resource management and many other applications, and plays an important role in the interaction between the hydrosphere, atmosphere, and biosphere [1].Due to the great spatial and temporal variability of precipitation, accurate precipitation measurements remain challenging, especially in regions with complex topography [2,3]. Precipitation is a fundamental component of the global water cycle. It provides important information for climate research, water resource management and many other applications, and plays an important role in the interaction between the hydrosphere, atmosphere, and biosphere [1]. Due to the great spatial and temporal variability of precipitation, accurate precipitation measurements remain challenging, especially in regions with complex topography [2,3]. Satellite data are the most effective means of precipitation observation on a global scale [5]. Because precipitation observation satellites generally have the characteristics of real-time, average spatial distribution, wide coverage, and high temporal and spatial resolution, they have gradually been applied to various meteorological and hydrological research [6,7]

Methods

Results

Discussion

Conclusion