Review of GPM IMERG performance: A global perspective

Pradhan, Rajani Kumar; Markonis, Yannis; Godoy, Mijael Rodrigo Vargas; Villalba-Pradas, Anahí; Andreadis, Konstantinos M.; Nikolopoulos, Efthymios I.; Papalexiou, S.; Rahim, Akif; Tapiador, Francisco J.; Hanel, Martin

doi:10.1016/j.rse.2021.112754

Cited by 169 publications

(110 citation statements)

References 146 publications

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“…In this study, given that the available Chinese Meteorological Administration (CMA) stations are sparse and unevenly distributed in the UB (Figure 1), an assessment based on such scarce precipitation gauge networks may bring much uncertainty. Some studies also recommend utilizing more than one reference precipitation dataset to evaluate gridded precipitation products [18]. In Sections 3.1 and 3.2 we used the gauge-based gridded dataset PCP_Sun as the reference data in the statistical and hydrological evaluations, as they were reconstructed with a high-density gauge networks and may best represent the real precipitation data in the UB so far [4].…”

Section: Uncertainty In Statistical Results Based On Different Benchm...mentioning

confidence: 99%

“…Among the GPM products, the Integrated Multi-satellite Retrievals for GPM (IMERG) has recently received more attention. The GPM IMERG algorithm provides three levels of products, namely the near-real-time 'Early' and 'Late' run products and the post-real-time 'Final' run product [18]. Compared to the 'Early' and 'Late' run products, the IMERG final run product is more accurate since it is adjusted with the Global Precipitation Climatology Centre (GPCC) monthly gauge observations [38].…”

Section: Four Gridded Precipitation Productsmentioning

confidence: 99%

“…The evaluation work can be generally classified into two categories: (1) direct comparison of the gridded precipitation to the corresponding rainfall gauge's precipitation data; (2) evaluation of gridded precipitation products based on their predictive ability of streamflow rate in a hydrological modeling framework. Evaluation works involved in different gridded precipitation datasets have been made over a range of scales from basin to global extent [17][18][19][20][21][22][23][24][25]. Meanwhile, recently some efforts have been put into evaluating and validating gridded precipitation over the TP and the basins within it [6,[26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation and Hydrological Application of Four Gridded Precipitation Datasets over a Large Southeastern Tibetan Plateau Basin

Zhang

et al. 2022

Remote Sensing

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Reliable precipitation is crucial for hydrological studies over Tibetan Plateau (TP) basins with sparsely distributed rainfall gauges. In this study, four widely used precipitation products, including the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of the water resources (APHRODITE), the High Asia Reanalysis (HAR), and the satellite-based precipitation estimates from Global Precipitation Measurement (GPM) and Tropical Rainfall Measurement Mission (TRMM), were comprehensively evaluated by combining statistical analysis and hydrological simulation over the Upper Brahmaputra (UB) River Basin of TP during 2001–2013. In respect to the statistical assessment, the overall performances of GPM and HAR are comparable to each other, and both are superior to the other two datasets. For hydrological assessment, both daily and monthly GPM-based streamflow simulations perform the best not only at the UB outlet with very good results, but they also illustrate satisfactory results at Yangcun and Lhasa hydrological stations within the UB. Runoff simulation using HAR only performs well at the UB outlet, whereas it shows poor results at both Yangcun and Lhasa stations. The simulated results based on APHRODITE and TRMM show poor performances at UB. Generally, the GPM shows an encouraging potential for hydro-meteorological investigation over UB, although with some bias in flood simulation.

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Section: Uncertainty In Statistical Results Based On Different Benchm...mentioning

confidence: 99%

Section: Four Gridded Precipitation Productsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation and Hydrological Application of Four Gridded Precipitation Datasets over a Large Southeastern Tibetan Plateau Basin

Zhang

et al. 2022

Remote Sensing

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“…These statistical metrics used include relative bias (RB), Pearson linear correlation coefficient (CC) and root mean square error (RMSE). These statistical indices have been used in many evaluation studies [15,16,49]. The equations are listed in Appendix A.…”

Section: Statistics Evaluation Metricsmentioning

confidence: 99%

“…The goal of MISS is to determine what percentage of drought events are detected by gauge but not by LSPEs [57]. These three categorical indices have been widely used in many studies [49,58,59]. The formulas are as follows:…”

Section: Statistics For Drought Characteristic Evaluationmentioning

confidence: 99%

Assessment of Three Long-Term Satellite-Based Precipitation Estimates against Ground Observations for Drought Characterization in Northwestern China

Guo

Nzabarinda

et al. 2022

Remote Sensing

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Long-term satellite-based precipitation estimates (LSPE) play a significant role in climatological studies like drought monitoring. In this study, three popular LSPEs (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), Rainfall Estimates from Rain Gauge and Satellite Observations (CHIRPS) and Multi-Source Weighted-Ensemble Precipitation (MSWEP)) were evaluated on a monthly scale using ground-based stations for capturing drought event characteristics over northwestern China from 1983 to 2013. To reflect dry or wet evolution, the Standardized Precipitation Index (SPI) was adopted, and the Run theory was used to identify drought events and their characteristics. The conventional statistical indices (relative bias (RB), correlation coefficient (CC), and root mean square error (RMSE)), as well as categorical indices (probability of detection (POD), false alarm ratio (FAR), and missing ratio (MISS)) are used to evaluate the capability of LSPEs in estimating precipitation and drought characteristics. We found that: (1) three LSPEs showed generally satisfactory performance in estimating precipitation and characterizing drought events. Although LSPEs have acceptable performance in identifying drought events with POD greater than 60%, they still have a high false alarm ratio (>27%) and a high missing ratio (>33%); (2) three LSPEs tended to overestimate drought severity, mainly because of an overestimation of drought duration; (3) the ability of CHIRPS to replicate the temporal evolution of precipitation and SPI values is limited; (4) in severe drought events, PERSIANN-CDR tends to overestimate precipitation, and drought severity, as well as drought area; (5) among the three LSPEs, MSWEP outperformed the other two in identifying drought events (POD > 66%) and characterizing drought features. Finally, we recommend MSWEP for drought monitoring studies due to its high accuracy in estimating drought characteristics over northwestern China. In drought monitoring applications, the overestimation of PERSIANN-CDR for drought peak value and area, as well as CHIRPS’s inferiority in capturing drought temporal evolution, must be considered.

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Assessing the influence of El Niño on the California precipitation regime during the satellite precipitation era

Chavda,

Li,

Farahmand

2024

Hydrological Processes

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A strong El Niño condition is likely to bring heavy precipitation over California. However, the 2015/2016 El‐Niño generally considered similar and comparable to another extreme event—the 1997/1998 El Niño—failed to bring above‐average precipitation over California. The muted influence of the 2015–2016 El‐Niño on California rainfall has renewed interest in the relationship between El Niño and California precipitation variability. This study assesses seasonal and regional aspects of the precipitation regimes in California for the past two decades using primarily satellite‐based precipitation products. Two satellite‐based precipitation datasets—Integrated Multi‐satellitE Retrievals for GPM (IMERG) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks‐Climate Data Record (PERSIANN‐CDR)—along with the ground‐based measurement—Global Precipitation Climatology Centre (GPCC) dataset as a reference—are analysed to understand the seasonal and regional influence of El‐Niño on precipitation regimes during the 2001–2019 period using Multivariate El Niño Southern Oscillation Index (MEI). The results show that El Niño's impact on precipitation rate varies in different seasons and regions. Specifically, we observe a statistically significant strong negative correlation (−0.72 to −0.66) between MEI and precipitation rate over southern California during the El Niño months. Both satellite precipitation products and ground‐based precipitation measurements indicate a similar El Niño–precipitation relationship over California during the study period. These results contradict the pre 2000 ‘classic’ story that El Niño was positively correlated with higher California precipitation.

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Review of GPM IMERG performance: A global perspective

Cited by 169 publications

References 146 publications

Evaluation and Hydrological Application of Four Gridded Precipitation Datasets over a Large Southeastern Tibetan Plateau Basin

Evaluation and Hydrological Application of Four Gridded Precipitation Datasets over a Large Southeastern Tibetan Plateau Basin

Assessment of Three Long-Term Satellite-Based Precipitation Estimates against Ground Observations for Drought Characterization in Northwestern China

Assessing the influence of El Niño on the California precipitation regime during the satellite precipitation era

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