Evaluation of the GPM IMERG v5 and TRMM 3B42 v7 Precipitation Products in the Yangtze River Basin, China

Wu, Yifan; Zhang, Zengxin; Huang, Yuhan; Qi, Jin; Chen, Xi; Chang, Juan

doi:10.3390/w11071459

Cited by 36 publications

(27 citation statements)

References 44 publications

(46 reference statements)

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“…Each of the three categorical metrics has been sequentially calculated annually for the days when daily rainfall exceeds 1, 5, 10, 25, 50, 75, 100, and 150 mm/d. Similar daily rainfall thresholds have been used in previous studies, such as Wu et al [29], Anjum et al [43], and Tan et al [49].…”

Section: Categorical Evaluation Metrics (1) Temporal Variationmentioning

confidence: 93%

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

Yang

Tan

et al. 2020

Remote Sensing

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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.

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Section: Categorical Evaluation Metrics (1) Temporal Variationmentioning

confidence: 93%

“…Precipitation in the basin is abundant, but also highly seasonal. Steep slopes combined with ample rainfall in summer have aggravated the risk of natural disasters such as floods and mudslides [29]. The flood in June 2016 in the SRB, for instance, has affected 58,000 people with a direct economic loss of 168 million RMB.…”

Section: Introductionmentioning

confidence: 99%

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

Yang

Tan

et al. 2020

Remote Sensing

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“…The GPM precipitation algorithm, Integrated Multi-satellite Retrievals for GPM (IMERG), is based upon the experiences from the TRMM algorithm. As the spatiotemporal resolution and coverage of GPM have been extended beyond the TRMM resolution and coverage, the performance of the GPM IMERG products needs to be evaluated and validated globally.Several studies have compared the GPM IMERG and TRMM products with ground-based measurements, i.e., rain gauge and weather radar [4,6,10,[15][16][17][18][19], considering their hydrological applications [14,[20][21][22]. Also, different GPM IMERG products regarding temporal resolutions have been evaluated considering various climatic and topographic conditions using various statistical measures across the world [5,[23][24][25][26][27][28][29][30].…”

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confidence: 99%

Ground Validation of GPM IMERG Precipitation Products over Iran

et al. 2019

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Accurate estimation of precipitation is crucial for fundamental input to various hydrometeorological applications. Ground-based precipitation data suffer limitations associated with spatial resolution and coverage; hence, satellite precipitation products can be used to complement traditional rain gauge systems. However, the satellite precipitation data need to be validated before extensive use in the applications. Hence, we conducted a thorough validation of the Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals (IMERG) product for all of Iran. The study focused on investigating the performance of daily and monthly GPM IMERG (early, late, final, and monthly) products by comparing them with ground-based precipitation data at synoptic stations throughout the country (2014)(2015)(2016)(2017). The spatial and temporal performance of the GPM IMERG was evaluated using eight statistical criteria considering the rainfall index at the country level. The rainfall detection ability index (POD) showed that the best IMERG product's performance is for the spring season while the false alarm ratio (FAR) index indicated the inferior performance of the IMERG products for the summer season. The performance of the products generally increased from IMERG-Early to -Final according to the relative bias (rBIAS) results while, based on the quantile-quantile (Q-Q) plots, the IMERG-Final could not be suggested for the applications relying on extreme rainfall estimates compared to IMERG-Early and -Late. The results in this paper improve the understanding of IMERG product's performance and open a door to future studies regarding hydrometeorological applications of these products in Iran.Remote Sens. 2020, 12, 48 2 of 23 subject to different errors and uncertainties, such as ground clutter, anomalous propagation, signal attenuation, beam blockage, and bright band contamination [6].Rain gauges are limited in describing the spatial distribution of precipitation depending on the arrangement and density of the rain gauge network [7,8]. In order to spatially characterize precipitation, gauge measurements are transformed to a gridded precipitation dataset. This is carried out through interpolation of rain gauge measurements, using spatial interpolation and geo-statistical methods [9]. These may be prone to missing values, wind effects, insufficient numbers of rain gauges, and a sparse network, especially in less accessible mountainous and oceanic areas [4].In view of the above, the spatial limitations, resolution, and coverage of ground-based measurements highlight the importance of satellite-based precipitation estimates at both the regional and global scale. Satellite-based precipitation estimates are also subject to uncertainties through cloud top reflectance, thermal radiance, infrequent satellite overpasses, and retrieval algorithm related to the nature of indirect measurement [10]. Therefore, a thorough validation of satellite precipitation data in any given area is necessary to achieve insight regarding is accuracy...

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“…The IMERG is the successor of TMPA, and the global products of IMERG have also been evaluated. Some studies have compared GPM and TRMM products [14,[33][34][35][36][37]. For example, Wang et al [38] indicated that IMERG correlates better with observations than TMPA, whereas the bias of IMERG is larger than that of TMPA at multiple temporal and spatial scales over Northeastern Tibetan Plateau.…”

Section: Introductionmentioning

confidence: 99%

Assessment on IMERG V06 Precipitation Products Using Rain Gauge Data in Jinan City, Shandong Province, China

Ren

et al. 2021

Remote Sensing

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In this study, a comprehensive assessment on precipitation estimation from the latest Version 06 release of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) algorithm is conducted by using 24 rain gauge observations at daily scale from 2001 to 2016. The IMERG V06 dataset fuses Tropical Rainfall Measuring Mission (TRMM) satellite data (2000–2015) and Global Precipitation Measurement (GPM) satellite data (2014–present), enabling the use of IMERG data for long-term study. Correlation coefficient (CC), root mean square error (RMSE), relative bias (RB), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) were used to assess the accuracy of satellite-derived precipitation estimation and measure the correspondence between satellite-derived and observed occurrence of precipitation events. The probability density distributions of precipitation intensity and influence of elevation on precipitation estimation were also examined. Results showed that, with high CC and low RMSE and RB, the IMERG Final Run product (IMERG-F) performs better than two other IMERG products at daily, monthly, and yearly scales. At daily scale, the ability of satellite products to detect general precipitation is clearly superior to the ability to detect heavy and extreme precipitation. In addition, CC and RMSE of IMERG products are high in Southeastern Jinan City, while RMSE is relatively low in Southwestern Jinan City. Considering the fact that the IMERG estimation of extreme precipitation indices showed an acceptable level of accuracy, IMERG products can be used to derive extreme precipitation indices in areas without gauged data. At all elevations, IMERG-F exhibits a better performance than the other two IMERG products. However, POD and FAR decrease and CSI increase with the increase of elevation, indicating the need for improvement. This study will provide valuable information for the application of IMERG products at the scale of a large city.

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Evaluation of the GPM IMERG v5 and TRMM 3B42 v7 Precipitation Products in the Yangtze River Basin, China

Cited by 36 publications

References 44 publications

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

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

Ground Validation of GPM IMERG Precipitation Products over Iran

Assessment on IMERG V06 Precipitation Products Using Rain Gauge Data in Jinan City, Shandong Province, China

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