Nine-Year Systematic Evaluation of the GPM and TRMM Precipitation Products in the Shuaishui River Basin in East-Central China

Abstract

Owing to their advantages of wide coverage and high spatiotemporal resolution, satellite precipitation products (SPPs) have been increasingly used as surrogates for traditional ground observations. In this study, we have evaluated the accuracy of the latest five GPM IMERG V6 and TRMM 3B42 V7 precipitation products across the monthly, daily, and hourly scale in the hilly Shuaishui River Basin in East-Central China. For evaluation, a total of four continuous and three categorical metrics have been calculated based on SPP estimates and historical rainfall records at 13 stations over a period of 9 years from 2009 to 2017. One-way analysis of variance (ANOVA) and multiple posterior comparison tests are used to assess the significance of the difference in SPP rainfall estimates. Our evaluation results have revealed a wide-ranging performance among the SPPs in estimating rainfall at different time scales. Firstly, two post-time SPPs (IMERG_F and 3B42) perform considerably better in estimating monthly rainfall. Secondly, with IMERG_F performing the best, the GPM products generally produce better daily rainfall estimates than the TRMM products. Thirdly, with their correlation coefficients all falling below 0.6, neither GPM nor TRMM products could estimate hourly rainfall satisfactorily. In addition, topography tends to impose similar impact on the performance of SPPs across different time scales, with more estimation deviations at high altitude. In general, the post-time IMERG_F product may be considered as a reliable data source of monthly or daily rainfall in the study region. Effective bias-correction algorithms incorporating ground rainfall observations, however, are needed to further improve the hourly rainfall estimates of the SPPs to ensure the validity of their usage in real-world applications.

How Accurately Can Satellite Products (TMPA and IMERG) Detect Precipitation Patterns, Extremities, and Drought Across the Nepalese Himalaya?

“…Since the terrain varies significantly over the midelevation and high‐elevation areas, the complex topographic nature and distribution of seasonal rainfall amount could be associated with the spatial distributions of seasonal bias values in IMERG and TMPA products. Moreover, as mentioned in previous study, IMERG product slightly outperforms TMPA, which might be attributed to the satellite overpasses and sensor capability (Chen & Li, 2016; Gebregiorgis et al, 2018; Yang et al, 2020).…”

“…However, both products were able to capture the spatial pattern of extreme precipitation over China. Compared with TRMM (3B42 V07), IMERG‐V3 reduces the systematical bias and is more reasonable to capture the rainfall variability in southwestern China (Yang et al, 2020). Gebregiorgis et al (2018) reported the improvement in new generation multisatellite precipitation (GPM IMERG) than TRMM‐based Multi‐satellite Precipitation Analysis (TMPA‐RT) due to miss‐rain and false‐rain bias reduction.…”

Capability of IMERG V6 Early, Late, and Final Precipitation Products for Monitoring Extreme Precipitation Events

“…Nowadays, the IMERG precipitation products have been upgraded from version 5 (V5) to version 6 (V6) by reducing biases based on the new GPCC monthly precipitation records. For example, the displacement vectors in V6 are computed using the Modern Era Retrospective Reanalysis 2 (MERRA-2) and Goddard Earth observing system (GEOS) model forward processing data instead of the previously used infrared data, which helps ensure consistency in the vectors between the IMERG-F and the IMERG-E and IMERG-L [26]. The generation times of IMERG precipitation products and gauge-based precipitation data are all universal time coordinated (UTC).…”

“…The IMERG precipitation products include three modes of runs, near real-time: early-run (IMERG-E) and late-run (IMERG-L), and post-real-time: final-run (IMERG-F) [23]. Current research has also focused on the application of the IMERG precipitation products on long-term scales [8,[24][25][26]. The research on extreme precipitation events on short-term scales is scarce.…”

Preliminary Utility of the Retrospective IMERG Precipitation Product for Large-Scale Drought Monitoring over Mainland China

“…Nevertheless, the infrared-based PERSIANN-CDR is corrected by coarse-scale GPCP [41,58], and CHIRPS utilizes relatively fewer meteorological stations for bias correction compared with IMERG [42,59]. TMPA 3B42V7 has comparatively fewer sensors than IMERG, although it is also adjusted by GPCC data [43,60]. Generally, among the numerous and continuously upgraded SPPs, the retrospective IMERG precipitation product may be an ideal dataset to surmount the challenges of data limitations to monitor drought [4,36,61].…”