Evaluation and Hydrological Utility of the Latest GPM IMERG V5 and GSMaP V7 Precipitation Products over the Tibetan Plateau

Lü, Daren; Yong, Bin

doi:10.3390/rs10122022

Cited by 121 publications

(91 citation statements)

References 77 publications

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“…Here, a commonly used threshold of 1.0 mm/day was set to define the rain/no rain event, as suggested by many previous studies [47][48][49][50]. For more detailed description of above continuous statistical metrics and contingency table metrics, readers can refer to Yong et al [25] and Lu et al [30]. We need to point out that all metrics were calculated in the 0.25 • × 0.25 • grid boxes with at least one gauge in order to ensure more convincing results (gauge distribution shows in Figure 1a).…”

Section: Methodsmentioning

confidence: 99%

“…The question of the availability exists widely in the gauge-adjusted satellite precipitation products. For example, if someone want to use PERSIANN-CDR or GSMaP_Gauge data, they must wait~3 months or~3 days after observation, respectively [21,30].In some cases, the real-time availability precipitation data is more critical for applications like rainstorm monitoring and flash flood warning, and it does not seem practical to use the traditionally delayed gauge-adjusted satellite precipitation products [31,32]. Thus, it is important to reduce the error of satellite precipitation estimates as much as possible without jeopardizing its near-real-time availability.…”

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confidence: 99%

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A Preliminary Assessment of the Gauge-Adjusted Near-Real-Time GSMaP Precipitation Estimate over Mainland China

Lü

Yong

2020

Remote Sensing

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The near-real-time satellite-derived precipitation estimates are attractive for a wide range of applications like extreme precipitation monitoring and natural hazard warning. Recently, a gauge-adjusted near-real-time GSMaP precipitation estimate (GSMaP_Gauge_NRT) was produced to improve the quality of the original GSMaP_NRT. In this study, efforts were taken to investigate and validate the performance of the GSMaP_Gauge_NRT using gauge observations over Mainland China. The analyses indicated that GSMaP_NRT generally overestimated the gauge precipitation in China. After calibration, the GSMaP_Gauge_NRT effectively reduced this bias and was more consistent with gauge observations. Results also showed that the correction scheme of GSMaP_Gauge_NRT mainly acted on hit events and could hardly make up the miss events of the satellite precipitation estimates. Finally, we extended the evaluation to the global scale for a broader view of GSMaP_Gauge_NRT. The global comparisons exhibited that the GSMaP_Gauge_NRT was in good agreement with the GSMaP_Gauge product. In conclusion, the GSMaP_Gauge_NRT had better performance than the GSMaP_NRT and was a more reliable near-real-time satellite precipitation product.

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Section: Methodsmentioning

confidence: 99%

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confidence: 99%

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A Preliminary Assessment of the Gauge-Adjusted Near-Real-Time GSMaP Precipitation Estimate over Mainland China

Lü

Yong

2020

Remote Sensing

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“…GSMaP provides three sets of mainstream types of satellite precipitation data based on different sensor input sources and algorithms, namely near-real-time product GSMaP_NRT, standard research product GSMaP_MVK, and gauge-calibrated product GSMaP_Gauge. The original instantaneous precipitation rate is estimated by a variety of passive microwave (PMW) sensors, including GPM Microwave Imager (GMI), advanced microwave scanning radiometer 2 (AMSR2), TRMM Microwave Imager (TMI), special sensor microwave imager/sounder (SSMIS), advanced microwave sounding unit-A (AMSU-A), and microwave humidity sounder (MHS) [27]. GSMaP_NRT uses less PMW input streams and a forward-only cloud advection scheme [28].…”

Section: Datamentioning

confidence: 99%

Quasi-Global Evaluation of IMERG and GSMaP Precipitation Products over Land Using Gauge Observations

Hua

Yong

2020

Water

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Understanding the error distribution of satellite precipitation products is conducive to obtaining accurate precipitation data, which is a very important input parameter in hydrological models and climate models. The error characteristics of Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) uncalibrated products on quasi-global land and six continents are evaluated, and the effects of latitude, elevation, and season on satellite precipitation product accuracy are analyzed. In order to be consistent with the Climate Prediction Center (CPC), the selected products are resampled at 0.5° and daily resolutions from 1 January 2015 to 31 August 2018. We find out that (1) GSMaP performs worse than IMERG mainly due to systematic errors and poor performance at high latitudes; (2) overestimation is obvious in high latitude areas of the northern hemisphere and also in areas with low rainfall intensity; (3) IMERG and GSMaP show good performance in summer and poor performance in winter; (4) where elevation is lower than 1500 m, the error metrics are highly correlated with the elevation; (5) the correlation coefficient is relatively high in areas with high rainfall, and the dispersion of satellite data and gauge data is also high. IMERG is a high-quality satellite precipitation product in the GPM era, but some uncertainties mentioned above are still worthy of attention by product developers and users.

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“…The study by Lu et al [13] assesses the performance of the latest GPM Integrated Multi-satellite Retrievals (IMERG V5) and Global Satellite Mapping of Precipitation version 7 (GSMaP V7) products and their hydrological application over the Tibetan Plateau. Two IMERG Final Run products Martins Costa do Amaral et al [14] apply the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) consolidated radar data processing to the X-band radar used in the CHUVA (Cloud Processes of the Main Precipitation Systems in Brazil: A Contribution to Cloud-Resolving Modeling and to the Global Precipitation Measurement) campaigns in Brazil.…”

Section: Overview Of Contributionsmentioning

confidence: 99%

Editorial for Special Issue “Remote Sensing of Precipitation”

Michaelides

2019

Remote Sensing

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This Special Issue hosts papers on all aspects of remote sensing of precipitation, including applications that embrace the use of remote-sensing techniques of precipitation in tackling issues, such as precipitation estimations and retrievals, along with their methodologies and corresponding error assessment; precipitation modelling including validation, instrument comparison, and calibration; understanding of cloud and precipitation microphysical properties; precipitation downscaling; precipitation droplet size distribution; assimilation of remotely sensed precipitation into numerical weather prediction models; and measurement of precipitable water vapor.

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Evaluation and Hydrological Utility of the Latest GPM IMERG V5 and GSMaP V7 Precipitation Products over the Tibetan Plateau

Cited by 121 publications

References 77 publications

A Preliminary Assessment of the Gauge-Adjusted Near-Real-Time GSMaP Precipitation Estimate over Mainland China

A Preliminary Assessment of the Gauge-Adjusted Near-Real-Time GSMaP Precipitation Estimate over Mainland China

Quasi-Global Evaluation of IMERG and GSMaP Precipitation Products over Land Using Gauge Observations

Editorial for Special Issue “Remote Sensing of Precipitation”

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