Multi-scale evaluation of high-resolution multi-sensor blended global precipitation products over the Yangtze River

Li, Zhe; Yang, Dawen; Hong, Yang

doi:10.1016/j.jhydrol.2013.07.023

Cited by 192 publications

(134 citation statements)

References 35 publications

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“…The results differ from those of Li et al (2013), in which PERSIANN has the poorest performance. This may result from differences in study region (in the study of Li et al (2013), south China was studied). Figure 8 shows the false alarm ratio, probability of detection, and critical success index for each precipitation product.…”

Section: Daily and Monthly Scalescontrasting

confidence: 86%

“…With respect to CC, GLDAS and TRMM3B42 are equally poor, with CC values of 0.81. The results also show that PERSIANN overestimates precipitation amount, while Li et al (2013) found PERSIANN underestimates rainfall in south China. This may be attributed to the different latitudes of the study regions.…”

Section: Daily and Monthly Scalesmentioning

confidence: 62%

“…In recent years, implementation of gauge-based and remote satellite-based precipitation products has become popular, particularly for ungauged catchments (Artan et al, 2007;Jiang et al, 2012;Li et al, 2013;Müller and Thompson, 2013;Maggioni et al, 2013;Xue et al, 2013;Kneis et al, 2014;Meng et al, 2014;Ochoa et al, 2014). Numerous precipitation products have been developed to estimate rainfall, for example: Tropical Rainfall Measuring Mission (TRMM) products (Huffman et al, 2007), Global Land Data Assimilation System (GLDAS) precipitation products (Kato et al, 2007), Asian Precipitation -Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) (Xie et al, 2007;Yatagai et al, 2012), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) (Sorooshian et al, 2000(Sorooshian et al, , 2002, and Global Satellite Mapping of Precipitation product (GSMAP) (Kubota et al, 2007;Aonashi et al, 2009).…”

Section: W Qi Et Al: Evaluation Of Global Fine-resolution Precipitamentioning

confidence: 99%

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Evaluation of global fine-resolution precipitation products and their uncertainty quantification in ensemble discharge simulations

Zhang

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. The applicability of six fine-resolution precipitation products, including precipitation radar, infrared, microwave and gauge-based products, using different precipitation computation recipes, is evaluated using statistical and hydrological methods in northeastern China. In addition, a framework quantifying uncertainty contributions of precipitation products, hydrological models, and their interactions to uncertainties in ensemble discharges is proposed. The investigated precipitation products are Tropical Rainfall Measuring Mission (TRMM) products (TRMM3B42 and TRMM3B42RT), Global Land Data Assimilation System (GLDAS)/Noah, Asian Precipitation -Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and a Global Satellite Mapping of Precipitation (GSMAP-MVK+) product. Two hydrological models of different complexities, i.e. a water and energy budget-based distributed hydrological model and a physically based semi-distributed hydrological model, are employed to investigate the influence of hydrological models on simulated discharges. Results show APHRODITE has high accuracy at a monthly scale compared with other products, and GSMAP-MVK+ shows huge advantage and is better than TRMM3B42 in relative bias (RB), Nash-Sutcliffe coefficient of efficiency (NSE), root mean square error (RMSE), correlation coefficient (CC), false alarm ratio, and critical success index. These findings could be very useful for validation, refinement, and future development of satellite-based products (e.g. NASA Global Precipitation Measurement). Although large uncertainty exists in heavy precipitation, hydrological models contribute most of the uncertainty in extreme discharges. Interactions between precipitation products and hydrological models can have the similar magnitude of contribution to discharge uncertainty as the hydrological models. A better precipitation product does not guarantee a better discharge simulation because of interactions. It is also found that a good discharge simulation depends on a good coalition of a hydrological model and a precipitation product, suggesting that, although the satellite-based precipitation products are not as accurate as the gauge-based products, they could have better performance in discharge simulations when appropriately combined with hydrological models. This information is revealed for the first time and very beneficial for precipitation product applications.

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Section: Daily and Monthly Scalescontrasting

confidence: 86%

Section: Daily and Monthly Scalesmentioning

confidence: 62%

Section: W Qi Et Al: Evaluation Of Global Fine-resolution Precipitamentioning

confidence: 99%

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Evaluation of global fine-resolution precipitation products and their uncertainty quantification in ensemble discharge simulations

Zhang

et al. 2016

Hydrol. Earth Syst. Sci.

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“…The r was used to quantify the type of correlation and dependence between satellite products and gauge observations in fundamental statistics. The RB and RRMSE were used to describe the bias and error of satellite precipitation compared with gauge observations [33][34][35]. The equations of the indicators are as follows:…”

Section: Methodologiesmentioning

confidence: 99%

Multiscale Comparative Evaluation of the GPM IMERG v5 and TRMM 3B42 v7 Precipitation Products from 2015 to 2017 over a Climate Transition Area of China

Chen

Duan

et al. 2018

Remote Sensing

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Abstract:The performance of the latest released Integrated Multi-satellitE Retrievals for GPM mission (IMERG) version 5 (IMERG v5) and the TRMM Multisatellite Precipitation Analysis 3B42 version 7 (3B42 v7) are evaluated and compared at multiple temporal scales over a semi-humid to humid climate transition area (Huaihe River basin) from 2015 to 2017. The impacts of rainfall rate, latitude and elevation on precipitation detection skills are also investigated. Results indicate that both satellite estimates showed a high Pearson correlation coefficient (r, above 0.89) with gauge observations, and an overestimation of precipitation at monthly and annual scales. Mean daily precipitation of IMERG v5 and 3B42 v7 display a consistent spatial pattern, and both characterize the observed precipitation distribution well, but 3B42 v7 tends to markedly overestimate precipitation over water bodies. Both satellite precipitation products overestimate rainfalls with intensity ranging from 0.5 to 25 mm/day, but tend to underestimate light (0-0.5 mm/day) and heavy (>25 mm/day) rainfalls, especially for torrential rains (above 100 mm/day). Regarding each gauge station, the IMERG v5 has larger mean r (0.36 for GPM, 0.33 for TRMM) and lower mean relative root mean square error (RRMSE, 1.73 for GPM, 1.88 for TRMM) than those of 3B42 v7. The higher probability of detection (POD), critical success index (CSI) and lower false alarm ratio (FAR) of IMERG v5 than those of 3B42 v7 at different rainfall rates indicates that IMERG v5 in general performs better in detecting the observed precipitations. This study provides a better understanding of the spatiotemporal distribution of accuracy of IMERG v5 and 3B42 v7 precipitation and the influencing factors, which is of great significance to hydrological applications.

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“…The current operational Version-7 TMPA system produces two standard user-level (Level 3) rainfall products at relatively fine resolution (0.25˝ˆ0.25˝, 3 h), i.e., the real-time 3B42RT (hereafter referred to as "RTV7"; 6-9 h after observation time) for the latitude band 60˝N-60˝S and the gauge-adjusted, post-real-time 3B42V7 for research purposes (hereafter "V7"; two months latency) with spatial coverage of 50˝N-50˝S [5,6]. Recently, these two quasi-global satellite precipitation products have been widely utilized in various hydrological and meteorological applications in China [7][8][9][10][11][12][13]. Over the years, there have been many efforts to compare and validate available satellite precipitation estimates at global, regional, or basin scales [9,10,[14][15][16][17][18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Error-Component Analysis of TRMM-Based Multi-Satellite Precipitation Estimates over Mainland China

et al. 2016

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Abstract:The Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) products have been widely used, but their error and uncertainty characteristics over diverse climate regimes still need to be quantified. In this study, we focused on a systematic evaluation of TMPA's error characteristics over mainland China, with an improved error-component analysis procedure. We performed the analysis for both the TMPA real-time and research product suite at a daily scale and 0.25˝ˆ0.25˝resolution. Our results show that, in general, the error components in TMPA exhibit rather strong regional and seasonal differences. For humid regions, hit bias and missed precipitation are the two leading error sources in summer, whereas missed precipitation dominates the total errors in winter. For semi-humid and semi-arid regions, the error components of two real-time TMPA products show an evident topographic dependency. Furthermore, the missed and false precipitation components have the similar seasonal variation but they counter each other, which result in a smaller total error than the individual components. For arid regions, false precipitation is the main problem in retrievals, especially during winter. On the other hand, we examined the two gauge-correction schemes, i.e., climatological calibration algorithm (CCA) for real-time TMPA and gauge-based adjustment (GA) for post-real-time TMPA. Overall, our results indicate that the upward adjustments of CCA alleviate the TMPA's systematic underestimation over humid region but, meanwhile, unfavorably increased the original positive biases over the Tibetan plateau and Tianshan Mountains. In contrast, the GA technique could substantially improve the error components for local areas. Additionally, our improved error-component analysis found that both CCA and GA actually also affect the hit bias at lower rain rates (particularly for non-humid regions), as well as at higher ones. Finally, this study recommends that future efforts should focus on improving hit bias of humid regions, false error of arid regions, and missed snow events in winter.

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Multi-scale evaluation of high-resolution multi-sensor blended global precipitation products over the Yangtze River

Cited by 192 publications

References 35 publications

Evaluation of global fine-resolution precipitation products and their uncertainty quantification in ensemble discharge simulations

Evaluation of global fine-resolution precipitation products and their uncertainty quantification in ensemble discharge simulations

Multiscale Comparative Evaluation of the GPM IMERG v5 and TRMM 3B42 v7 Precipitation Products from 2015 to 2017 over a Climate Transition Area of China

Error-Component Analysis of TRMM-Based Multi-Satellite Precipitation Estimates over Mainland China

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