Evaluation of Version-7 TRMM Multi-Satellite Precipitation Analysis Product during the Beijing Extreme Heavy Rainfall Event of 21 July 2012

Huang, Yong; Chen, Sheng; Cao, Qing; Hong, Yang; Wu, Buyun; Huang, Mengyu; Qiao, Lifang; Zhang, Zengxin; Li, Zhe; Li, Weiyue; Yang, Xiuqin

doi:10.3390/w6010032

Cited by 79 publications

(49 citation statements)

References 35 publications

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“…MAE represents the averaged magnitude of the absolute error; RMSE indicates the averaged error magnitude; RB is used to measure the probability of overestimation (RB < 0) or underestimation (RB > 0) from satellite-based products; and CC reflects the synchronicity of precipitation variation between satellite precipitation products and meteorological stations. Compared with rain gauge data, the higher accuracy of satellite-based products has a higher CC, lower MAE, and RMSE [34]. Table 3.…”

Section: Categorical Statisticsmentioning

confidence: 99%

“…The Multi-Satellite Precipitation Analysis (TMPA) algorithm was proposed by Huffman et al [11] and it was combined with several other high-quality rainfall estimation algorithms, e.g., merged Active/Passive Microwave and Infrared-based rainfall estimates, as well as other multi-source data fusion of rainfall products [33]. The TMPA rainfall products are available both in 3B42RT (three-hourly) and 3B42 (three-hourly and daily), as well as the monthly rainfall dataset (3B43), covering the range of latitude 50 • S-50 • N and longitude 180 • W-180 • E. Compared to 3B42RT, 3B42V7 was corrected by the monthly rainfall data deviation from the Global Precipitation Climate Center (GPCC) calibration meteorological stations and the product has several computation improvements and a better data accuracy [34]. The most recent TRMM 3B42 Version 7 is a gauge-adjusted post-real-time rainfall product covering the 1998-present period from various satellite systems [11].…”

Section: Satellite-based Rainfall Productsmentioning

confidence: 99%

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Accuracy Evaluation of Two High-Resolution Satellite-Based Rainfall Products: TRMM 3B42V7 and CMORPH in Shanghai

Jiang

Wen

et al. 2018

Water

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Satellite-based rainfall products have extensive applications in global change studies, but they are known to contain deviations that require comprehensive verification at different scales. In this paper, we evaluated the accuracies of two high-resolution satellite-based rainfall products: the Tropical Rainfall Measurement Mission (TRMM) rainfall product 3B42V7 and the Climate Prediction Center morphing (CMORPH) technique from January 2010 to December 2011 in Shanghai, by using categorical metrics (Probability of Detection, False Alarm Ratio, and Critical Success Index) and statistical indicators (Mean Absolute Error, Root Mean Square Error, Relative Bias, and Correlation Coefficient). Our findings show that, firstly, CMORPH data has a higher accuracy than 3B42V7 at the daily scale, but it underestimates the occurrence frequency of daily rainfall for some intensity ranges. Most errors of the two products are distributed between −10 and 10 mm/day. Second, at the monthly scale, the total accuracy of 3B42V7 is higher than CMORPH in terms of the value of the Correlation Coefficient (CC) and Relative Bias (RB). Finally, CMORPH brings about daily rainfall detection results from categorical metrics computation better than 3B42V7. Generally, the two satellite-based rainfall products show a high correlation with rain gauge data in Shanghai, particularly in spring and winter. Unfortunately, in summer, both of them do not perform well in detecting the short-duration heavy rainfall events. Overall, the relatively poor data accuracy has limited their further applications in Shanghai and similar urban areas.

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Section: Categorical Statisticsmentioning

confidence: 99%

Section: Satellite-based Rainfall Productsmentioning

confidence: 99%

Accuracy Evaluation of Two High-Resolution Satellite-Based Rainfall Products: TRMM 3B42V7 and CMORPH in Shanghai

Jiang

Wen

et al. 2018

Water

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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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“…It can be seen that there is a good agreement between the measured and estimated total monthly precipitation(R 2 = 0.98, p < 0.001). While satellite derived precipitation products have limitations in terms of accuracy and resolution for extreme rainfall events [25], it has been demonstrated that they produce reliable data on longer spatio-temporal scales. …”

Section: Precipitationmentioning

confidence: 99%

A Self-Calibrating Runoff and Streamflow Remote Sensing Model for Ungauged Basins Using Open-Access Earth Observation Data

Poortinga¹,

Bastiaanssen

Simons

et al. 2017

Remote Sensing

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Due to increasing pressures on water resources, there is a need to monitor regional water resource availability in a spatially and temporally explicit manner. However, for many parts of the world, there is insufficient data to quantify stream flow or ground water infiltration rates. We present the results of a pixel-based water balance formulation to partition rainfall into evapotranspiration, surface water runoff and potential ground water infiltration. The method leverages remote sensing derived estimates of precipitation, evapotranspiration, soil moisture, Leaf Area Index, and a single F coefficient to distinguish between runoff and storage changes. The study produced significant correlations between the remote sensing method and field based measurements of river flow in two Vietnamese river basins. For the Ca basin, we found R 2 values ranging from 0.88-0.97 and Nash-Sutcliffe efficiency (NSE) values varying between 0.44-0.88. The R 2 for the Red River varied between 0.87-0.93 and NSE values between 0.61 and 0.79. Based on these findings, we conclude that the method allows for a fast and cost-effective way to map water resource availability in basins with no gauges or monitoring infrastructure, without the need for application of sophisticated hydrological models or resource-intensive data.

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Evaluation of Version-7 TRMM Multi-Satellite Precipitation Analysis Product during the Beijing Extreme Heavy Rainfall Event of 21 July 2012

Cited by 79 publications

References 35 publications

Accuracy Evaluation of Two High-Resolution Satellite-Based Rainfall Products: TRMM 3B42V7 and CMORPH in Shanghai

Accuracy Evaluation of Two High-Resolution Satellite-Based Rainfall Products: TRMM 3B42V7 and CMORPH in Shanghai

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

A Self-Calibrating Runoff and Streamflow Remote Sensing Model for Ungauged Basins Using Open-Access Earth Observation Data

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