Component Analysis of Errors in Four GPM-Based Precipitation Estimations over Mainland China

Abstract

As the Global Precipitation Measurement (GPM) Core Observatory satellite continues its mission, the latest GPM-era satellite-based precipitation estimations, including Global Satellite Mapping of Precipitation (GSMaP) and Integrated Multi-satellitE Retrievals for the GPM (IMERG), have been released. However, few studies have systematically evaluated these products over mainland China, although this is very important for both the end users and data developers. To these ends, the final-run uncalibrated IMERG V05 (V05UC), gauge-calibrated IMERG V05 (V05C) and IMERG V04 (V04C), and latest gauge-calibrated GSMaP V7 (GSMaP) are systematically evaluated and mutually compared against a merged product obtained from the China Meteorological Data Service Center via continuous statistical indices and an error decomposition analysis technology suite over mainland China from April 2014 to December 2016 at a 3 hourly scale and 0.1° × 0.1° resolution. The results show that, irrespective of the slight overestimation in the southeast and underestimation in the northern Tibetan Plateau, all four GSPEs could generally capture the spatial patterns of precipitation over mainland China. Meanwhile, the overall quality of the GSMaP is slightly superior to the IMERG products in east and south China; however, it also suffers from an overestimation of light rain and an underestimation of heavy rain. Such overestimation and underestimation are primarily from a large false precipitation in light rain and a negative hit bias in heavy rain, respectively. The latest IMERG V05 products have not shown significant improvement over the earlier version (V04C) in east and south China, but the calibrated V05C can best reproduce the probability density function in terms of precipitation intensity. Furthermore, V04C shows remarkable underestimation over the Tibetan Plateau, while this shortcoming has been resolved significantly in V05C. Alternately, the effects of the gauge calibration algorithm (GCA) used in IMERG are examined by comparison of V05UC and V05C. The results indicate that GCA cannot reduce the missed precipitation, and even enlarges the false precipitation over some regions. This reveals that GCA cannot effectively alleviate the bias resulting from the rain areas’ delineation and raining or not-raining detection. In addition, all of the products’ performance can be improved, particularly in the dry climate and high-latitude regions. This is a systematic estimation for GSPEs, providing deep insight into the characteristics and sources of error, and it could be valuable as a reference for both algorithm developers and data users, as well as for associated global products and various applications.

The Assessment and Comparison of TMPA and IMERG Products Over the Major Basins of Mainland China

“…CRB) and the high-latitude basins (e.g., YeRB, HaiRB, and SLRB) as reported by previous studies (J Su et al, 2018;Tang, Ma, et al, 2016;Wei et al, 2018), providing some guidance for potential hydrologic applications over these basins. Our results highlight their excellent performance over the humid southeast China (e.g., YaRB, PeRB, and SeRB) and confirm their poorer performance over the arid northwest China (e.g.…”

“…In contrast, TMPA-V7 shares almost identical distributions of CC with TMPA-RT, but exhibits higher values over most parts of Mainland China. Since rain areas delineation and rain/no-rain delineation cannot be corrected by the GCA (J Su et al, 2018;Xu et al, 2016), compared to corresponding near-real-time products, there is no significant improvement of post-real-time SPEs. Even so, IMERG products still suffer from the low CC over northwest China.…”

“…Earth and Space Science partially or totally evaporate before reaching surface . Since rain areas delineation and rain/no-rain delineation cannot be corrected by the GCA (J Su et al, 2018;Xu et al, 2016), compared to corresponding near-real-time products, there is no significant improvement of post-real-time SPEs. Hence, for further improving the performance of SPEs (particularly for arid regions), more attention should be focused on developing more accurate rain areas delineation algorithm.…”

“…For instance, Chen and Li (2016) monthly evaluated IMERG and compared it with TMPA 3B43 products while Ning et al (2016) directly compared IMERG and GSMaP against gauge observations over a period from April 2014 to November 2015. Meanwhile, J Su et al (2018) attempted to reveal the 3-hr error features of multiple IMERG products and GSMaP via the error-component analysis, but the reference data used in it are an hourly gaugesatellite merged product produced by merging the ground data with the CMORPH. These researches all used a sparse gauge network (less than 840 weather stations over entire China) as the benchmark.…”

“…Due to the great interannual variation of precipitation in Mainland China, it is hard to obtain reliable statistical performance metrics of these SPEs in such a short duration. Meanwhile, J Su et al (2018) attempted to reveal the 3-hr error features of multiple IMERG products and GSMaP via the error-component analysis, but the reference data used in it are an hourly gaugesatellite merged product produced by merging the ground data with the CMORPH. Since the reference data are not truly independent reference, it may bias the assessment results.…”