Hydrologic Evaluation of TRMM and GPM IMERG Satellite-Based Precipitation in a Humid Basin of China

Zhang, Zengxin; Tian, Jiaxi; Huang, Yuhan; Chen, Xi; Chen, Sheng; Duan, Zheng

doi:10.3390/rs11040431

Cited by 48 publications

(41 citation statements)

References 41 publications

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“…This could be due to its course spatial resolution and lack of gauge-adjustment for highlands of Ethiopia (Haile et al, 2013). The IMERG6 showed better rainfall detection and estimation capability for the study area than the 3B43 product, which is consistent with findings of previous studies (Huffman et al, 2015;Zhang et al, 2018;Zhang et al, 2019). Better performance of IMERG6 is attributed to the inclusion of dual and high-frequency channels, which improve light and solid precipitation detection capability (Huffman et al, 2015).…”

Section: Discussionsupporting

confidence: 80%

Performance evaluation of multiple satellite rainfall products for Dhidhessa River Basin (DRB), Ethiopia

Wedajo¹,

Muleta²,

Gessesse³

2020

Preprint

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Abstract. Precipitation is a crucial driver of hydrological processes. Ironically, reliable characterization of its spatiotemporal variability is challenging. Ground-based rainfall measurements using rain gauges can be more accurate. However, installing a dense gauging network to capture rainfall variability can be impractical. Satellite-based rainfall estimates (SREs) can be good alternatives, especially for data-scarce basins like in Ethiopia. However, SREs rainfall is plagued with uncertainties arising from many sources. The objective of this study was to evaluate the performance of the latest versions of several SREs products (i.e., CHIRPS2, IMERG6, TAMSAT3, and 3B42/3) for the Dhidhessa River Basin (DRB). Both statistical and hydrologic modelling approaches were used for performance evaluation. The Soil and Water Analysis Tool (SWAT) was used for hydrological simulations. The results showed that whereas all four SREs products are promising to estimate and detect rainfall for the DRB, the CHIRPS2 dataset performed the best at annual, seasonal, and monthly timescales. The hydrologic simulation-based evaluation showed that SWAT's calibration results are sensitive to the rainfall dataset. The hydrologic response of the basin is found to be dominated by the subsurface processes, primarily by the groundwater flux. Overall, the study showed that both CHIRPS2 and IMERG6 products can be reliable rainfall data sources for hydrologic analysis of the Dhidhessa River Basin.

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Section: Discussionsupporting

confidence: 80%

Performance evaluation of multiple satellite rainfall products for Dhidhessa River Basin (DRB), Ethiopia

Wedajo¹,

Muleta²,

Gessesse³

2020

Preprint

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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 GPM as the successor of the TRMM provided three IMERG precipitation products (the near-real-time "early" run (IMERG-E), "late" run (IMERG-L), and the post-real-time "final" run (IMERG-F)). According to existing research, scholars mainly evaluate and compare IMERG-L with rain gauge observations and several prevailing satellite precipitation products in varying climate zones and spatial-temporal scales for capturing key features of rainfall [20][21][22][23][24][25][26][27][28][29]. Predominantly, the aforementioned studies find that IMERG products show promising potential in rainfall spatial-temporal distribution, but they pay little attention to assessing the performance of the satellite precipitation of typhoon-specific precipitation over the Yangtze River Delta (YRD).…”

Section: Introductionmentioning

confidence: 99%

Data Assimilation of High-Resolution Satellite Rainfall Product Improves Rainfall Simulation Associated with Landfalling Tropical Cyclones in the Yangtze River Delta

Wang

Yang

et al. 2020

Remote Sensing

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Floods caused by heavy rainfall events associated with landfalling tropical cyclones (TCs) represent a major risk for the Yangtze River Delta (YRD) region of China. Accurate extreme precipitation forecasting, at long lead times, is crucial for the improvement of flood prevention and warning. However, accurate prediction of timing, location, and intensity of the heavy rainfall events is a major challenge for the Numerical Weather Prediction (NWP). In this study, high-resolution satellite precipitation products like Global Precipitation Measurement (GPM) are evaluated at the hourly timescale, and the optimal Integrated Multi-satellite Retrievals for GPM (IMERG) precipitation product is selected and applied to directly assimilate into the Weather Research and Forecasting (WRF) model via the four-dimensional variational (4D-Var) method. The TC Jondari and Rumbia events of August 2018 are evaluated to analyze the performance of the WRF model with the 4D-Var method assimilated IMERG precipitation product (DA-IMERG) and the conventional observation (DA-CONV) for real-time heavy rainfall forecasting. The results indicate that (1) IMERG precipitation products were larger and wetter than the observed precipitation values over YRD. By comparison, the performance of “late” run precipitation product (IMERG-L) was the closest to the observation data with lower deviation and higher detection capability; (2) DA-IMERG experiment substantially affected the magnitude of the WRF model primary variables, which changed the precipitation pattern of the TC heavy rain. (3) DA-IMERG experiment further improved the forecast of heavy rainbands and relatively reduced erroneous detection rate than CTL and DA-CONV experiments at the grid scale. Meanwhile, the DA-IMERG experiment has a better fractions skill score (FSS) value (especially in the threshold of 10 mm/h) than DA-CONV for TC Jondari and Rumbia at the spatial scale, while it shows a lower performance than CTL and DA-CONV experiments when the threshold is lower than the 5 mm/h for the TC Rumbia.

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Hydrologic Evaluation of TRMM and GPM IMERG Satellite-Based Precipitation in a Humid Basin of China

Cited by 48 publications

References 41 publications

Performance evaluation of multiple satellite rainfall products for Dhidhessa River Basin (DRB), Ethiopia

Performance evaluation of multiple satellite rainfall products for Dhidhessa River Basin (DRB), Ethiopia

Ground Validation of GPM IMERG Precipitation Products over Iran

Data Assimilation of High-Resolution Satellite Rainfall Product Improves Rainfall Simulation Associated with Landfalling Tropical Cyclones in the Yangtze River Delta

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