Abstract

The comprehensive assessment of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) V05B is important for benchmarking the product’s continued improvement and future development. The performance of IMERG V05B precipitation products was systematically evaluated using 542 precipitation gauges at multiple spatiotemporal scales from March 2014 to February 2017 over China. Moreover, IMERG V05B was compared with IMERG V04A, the Tropical Rainfall Measuring Mission (TRMM) 3B42, and the Climate Prediction Center Morphing technique (CMORPH)-CRT in this study. Categorical verification techniques and statistical methods are used to quantify their performance. Results illustrate the following. (1) Except for IMERG V04A’s severe underestimation over the Tibetan Plateau (TP) and Xinjiang (XJ) with high negative relative biases (RBs) and CMORPH-CRT’s overestimation over XJ with high positive RB, the four satellite-based precipitation products generally capture the same spatial patterns of precipitation over China. (2) At the annual scale over China, the IMERG products do not show an advantage over its predecessor (TRMM 3B42) in terms of RMSEs, RRMSEs, and Rs; meanwhile, the performance of IMERG products is worse than TRMM 3B42 in spring and summer according to the RMSE, RRMSE, and R metrics. Between the two IMERG products, IMERG V05B shows the anticipated improvement (over IMERG V04A) with a decrease in RMSE from 0.4496 to 0.4097 mm/day, a decrease of RRMSE from 16.95% to 15.44%, and an increase of R from 0.9689 to 0.9759 during the whole study period. Similar results are obtained at the seasonal scale. Among the four satellite products, CMORPH-CRT shows the worst seasonal performance with the highest RMSE (0.6247 mm/day), RRMSE (23.55%), and lowest R (0.9343) over China. (3) Over XJ and TP, IMERG V05B clearly improves the strong underestimation of precipitation in IMERG V04A with the RBs of 5.2% vs. −21.8% over XJ, and 2.78% vs. −46% over TP. Results at the annual scale are similar to those obtained at the seasonal scale, except for summer results over XJ. While, over the remaining subregions, the two IMERG products have a close performance; meanwhile, IMERG V04A slightly improves IMERG V05B’s overestimation according to RBs (except for HN) at the annual scale. However, all four products are unreliable over XJ at both an annual and seasonal scale. (4) Across all products, TRMM 3B42 best reproduces the probability density function (PDF) of daily precipitation intensity. (5) According to the categorical verification technique in this study, both IMERG products yield better results for the detection of precipitation events on the basis of probability of detection (POD) and critical success index (CSI) categorical evaluations compared to TRMM 3B42 and CMORPH-CRT over China and across most of the subregions. However, all four products have room for further improvement, especially in high-latitude and dry climate regions. These findings provide valuable feedback for both IMERG algorithm developers and data set users.

Highlights

  • Precipitation is a crucial component of the Earth’s water and energy cycles

  • (2) At the annual scale over China, the IMERG products do not show an advantage over its predecessor (TRMM 3B42) in terms of Root Mean Square Error (RMSE), Relative Root Mean Square Error (RRMSE), and Rs; the performance of IMERG products is worse than Tropical Rainfall Measuring Mission (TRMM) 3B42 in spring and summer according to the RMSE, RRMSE, and R metrics

  • As a point of reference, IMERG V04A, Center Morphing technique (CMORPH)-CRT, and TRMM 3B42 precipitation estimates are evaluated in parallel with the IMERG V05B product

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Summary

Introduction

Precipitation is a crucial component of the Earth’s water and energy cycles. Reliable and accurate precipitation information plays an important role in hydrology, climatology, and water resource management [1,2,3,4,5,6,7,8,9,10,11]. Rain gauge networks may provide relatively accurate precipitation measurements but with an uneven distribution of stations and limited spatial representativeness [12]. Likewise, groundbased weather radars provide precipitation estimates at relatively high spatiotemporal resolution, but with limited utility in mountainous and cold regions [4, 13]. Global scale precipitation estimates have become feasible through the development of satellite remote sensing techniques, and a series of satellite-based precipitation products (CMORPH [14], the TRMM Multi-satellite Precipitation Analysis (TMPA) [15], and IMERG [16]) have been developed and released. E CMORPH technique integrates direct microwavebased retrievals of precipitation rate with observations of cloud top motion dynamics derived from infrared (IR) data to estimate high-resolution (0.25°/8 km, 3 h/0.5 h) global precipitation rates (60° N-60° S). Many previous studies have evaluated the performance of CMORPH products [14, 17,18,19,20,21]

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