The Effect of Spatiotemporal Resolution Degradation on the Accuracy of IMERG Products over the Huai River Basin

Su, Jiabin; Lü, Haishen; Crow, Wade T.; Zhu, Yonghua; Cui, Yubo

doi:10.1175/jhm-d-19-0158.1

Cited by 9 publications

(6 citation statements)

References 41 publications

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“…Compared to the regional-level AWS, the national-level AWS observes more meteorological elements and has more rain gauges (3 vs. 1, type SL3-1) for precipitation measure at the same time. The CMPA is often selected as the benchmark to evaluate the IMERG precipitation estimates [13,[51][52][53]. However, there is an unexpected precipitation bias due to the wind-induced undecatch [42,54].…”

Section: Ground Reference Datamentioning

confidence: 99%

Modeling and Evaluating Systematic and Random Errors in Multiscale GPM IMERG Summer Precipitation Estimates Over the Sichuan Basin

Tang

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM IMERG) is widely used in hydrological and meteorological studies, owing to its high spatial resolution and accuracy. The quantification of systematic and random errors for the IMERG estimates at different temporal resolutions is beneficial to the calibration of observation instruments and correction of estimates, especially for regions such as the Sichuan Basin where frequent geological disasters occur in the summer. At present, there are two most commonly used models, namely the additive and multiplicative models, for modelling the systematic and random errors of precipitation. However, it's unknow which model is more suitable for the IMERG summer hourly, daily, and monthly precipitation estimates. Therefore, in this study, two models' separative capability of the systematic and random errors and predictive capability in the IMERG estimates are investigated, upon the evaluation of the models' applicability. Results show that: for the hourly and daily precipitation estimates, the multiplicative error model has better separative and predictive capabilities than the additive error model and is recommended to quantify the systematic and random errors. Conversely, as for the monthly precipitation estimates, the additive error model is a relatively better choice by comparing the overall performance of both models. However, it still has some weaknesses for heavy monthly precipitation, such as nonconstant fluctuations and reduced predictive capability. So, the additive model should be used with caution in analysing the systematic and random errors of the heavy monthly precipitation.

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Section: Ground Reference Datamentioning

confidence: 99%

Modeling and Evaluating Systematic and Random Errors in Multiscale GPM IMERG Summer Precipitation Estimates Over the Sichuan Basin

Tang

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…To assess the data quality and guide further improvements, GPM IMERG has been extensively evaluated through comparison with ground-based observational data. Many studies have reported its error characteristics and identified factors that can affect retrieval accuracy [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Impact of Atmospheric Aerosols on the Accuracy of IMERG Precipitation Estimates Over Northern China

Li,

Wang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Accurate satellite precipitation estimates are vital for understanding global and largescale regional water cycles. Among the many factors influencing satellite precipitation data quality, the detection and accuracy of precipitation products at different atmospheric aerosol concentrations are not well studied. In this study, we investigated the impact of atmospheric aerosols on the accuracy of satellite precipitation products (IMERG) over North China by comparing performance metrics such as Bias, Normalized Root Mean Squared Error, probability of detection and false alarm ratio under different atmospheric aerosol conditions. The results revealed that: (1) IMERG generally exhibits poorer detectability and quantification under pollution condition. (2) Based on the error decomposition, the estimated errors in autumn and winter were dominated by false biases, which are mainly affected by atmospheric aerosols. (3) At the sensor level, the false alarm ratios (FARs) of both infrared (IR) and passive microwave (PMW) sensors show escalating trends as pollutant concentrations increase. The probability of detection (POD) of IR sensors is affected by pollution. Pollution has a significant impact on IR detection capability. Our findings suggest that atmospheric aerosols may impact the accuracy of IMERG precipitation estimates over Northern China and need to be taken into consideration in the IMERG retrieval process and data utilization.

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“…The most typical example could be the transition from the Tropical Rainfall Measuring Mission (TRMM) to the Global Precipitation Measurement (GPM) mission [12]. The Integrated Multi-Satellite Retrievals for GPM (IMERG) provides improved precipitation data by integrating microwave and infrared data from the GPM satellite constellation [13]. Compared to TRMM products, IMERG products have higher temporal (half-hourly) and spatial resolution (0.1 • ) and improved capability for monitoring extreme precipitation [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Can GPM IMERG Capture Extreme Precipitation in North China Plain?

Zhang¹,

Yang

et al. 2022

Remote Sensing

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Extreme precipitation events (EPE) often cause catastrophic floods accompanied by serious economic losses and casualties. The latest version (V06) of the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG) provides global satellite precipitation data from 2000 at a higher spatiotemporal resolution with improved quality. It is scientifically and practically important to assess the accuracy of the IMERG V06 in capturing extreme precipitation. This study evaluates the two widely used products of IMERG during 2000–2018, i.e., IMERG late run (IMERG-L) and IMERG final run (IMERG-F), in the densely populated and flood-prone North China Plain. The accuracy of the IMERG V06 is evaluated with ground measurements from rain gauge stations at multiple scales (hourly, daily, and seasonally). A novel target tracking method is introduced to extract three-dimensional (3D) extreme precipitation events, and the near-real-time uncalibrated PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System) and GSMAP (Global Satellite Mapping of Precipitation) satellite data are added to further evaluate IMERG’s performance during extreme precipitation. Finally, for flash flood events induced by extreme rainfall in the Hebei Province from 15 to 23 July 2016, the accuracy of capturing the event with IMERG-F and IMERG-L was verified. Results reveal that IMERG-F is better than IMERG-L at all investigated scales (hourly, daily, and seasonally), but the difference between the two products is less at higher time resolutions. Both products manifest decreased performance when capturing 3D extreme precipitation events, and comparatively, IMERG-F performs better than IMERG-L. IMERG-F exhibits a distinct discontinuity in extreme precipitation thresholds between land and ocean, which is a limitation of IMERG-F not documented in previous studies. Moreover, IMERG-L and IMERG-F are comparable at an hourly scale for some metrics, which is beyond the expectation that IMERG-F is notably better than IMERG-L. This study provides a scientific basis for the performance of satellite precipitation products and contributes to guiding users when applying global precipitation products.

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The Effect of Spatiotemporal Resolution Degradation on the Accuracy of IMERG Products over the Huai River Basin

Cited by 9 publications

References 41 publications

Modeling and Evaluating Systematic and Random Errors in Multiscale GPM IMERG Summer Precipitation Estimates Over the Sichuan Basin

Modeling and Evaluating Systematic and Random Errors in Multiscale GPM IMERG Summer Precipitation Estimates Over the Sichuan Basin

Impact of Atmospheric Aerosols on the Accuracy of IMERG Precipitation Estimates Over Northern China

Can GPM IMERG Capture Extreme Precipitation in North China Plain?

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