Evaluation of latest TMPA and CMORPH satellite precipitation products over Yellow River Basin

Jiang, Shanhu; Zhou, Meng; Ren, Lei; Cheng, Xuerong; Zhang, Pengju

doi:10.1016/j.wse.2016.06.002

Cited by 62 publications

(39 citation statements)

References 21 publications

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“…Numerous previous studies have evaluated different satellite products over China [18,35,67,68] and several studies have pointed out differences between different versions of the same product [43,67]. However, none of these studies cover the different versions of GPM-based satellite precipitation products over China.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to the influences of satellite sensors, the land-surface complex, and precipitation type [18], the errors of satellite precipitation estimates show evident dependency on the precipitation-retrieval algorithm. Compared to the IMERGF-V3 product, the IMERGF-V4 product significantly underestimates precipitation over the QZ region in this study.…”

Section: Discussionmentioning

confidence: 99%

“…Quantitative and systematical precipitation estimations from satellite-based precipitation products have been used in hydrology, climatology, and water-resources management [9][10][11][12][13]. However, it remains a worthy challenge to produce accurate satellite-based precipitation estimations due to the great temporal and spatial variability of the products, acquisitions at different scales, inhomogeneous distributions of rain gauges, complex topography, and cold weather [14][15][16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comprehensive Evaluation of Two Successive V3 and V4 IMERG Final Run Precipitation Products over Mainland China

et al. 2017

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Abstract:The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Final Run (IMERGF) product has now been upgraded to Version 4 (V4), which has been available since March 2017. Therefore, it is desirable to evaluate the characteristic differences between the V4 and the previous V3 products. A comprehensive performance evaluation of the errors of the successive V3 and V4 IMERGF products is performed with a comparison of the China daily Precipitation Analysis Products (CPAP) from March 2014 to February 2015. The version 6 Global Satellite Mapping of Precipitation (GSMaP) research product (which is another Global Precipitation Measurement (GPM) based precipitation product) is also used as a comparison in this study. Overall, the IMERGF-V4 product does not exhibit the anticipated improvement for China compared to the IMERGF-V3 product. An analysis of the metrics of annual daily average precipitation over China for the IMERGF-V3 and IMERGF-V4 products indicates a decrease of the relative bias (RB) from 3.70% to −7.18%, a decrease of the correlation coefficient (CC) from 0.91 to 0.89, an increase of the fractional standard error (FSE) from 0.49 to 0.56, and an increase of the root-mean-square error (RMSE) from 0.63 mm to 0.72 mm. Compared to the IMERGF-V3 product, the IMERGF-V4 product exhibits a significant underestimation of precipitation in the Qinghai-Tibetan plateau with a much lower RB of −60.91% (−58.19%, −65.30%, and −63.74%) based on the annual (summer, autumn, and winter) daily average precipitation and an even worse performance during winter (−72.33% of RB). In comparison, the GSMaP product outperforms the IMERGF-V3 and IMERGF-V4 products and has the smallest RMSE (0.47 mm/day), highest CC (0.95), lowest FSE (0.37), and best performance of the RB (−2.39%) in terms of annual daily precipitation over China. However, the GSMaP product underestimates the precipitation more than the IMERGF-V3 product for the arid XJ region.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comprehensive Evaluation of Two Successive V3 and V4 IMERG Final Run Precipitation Products over Mainland China

et al. 2017

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“…Continuous, accurate and reliable precipitation observation is key important input variable for climatic and hydrologic research [7,8]. However, meteorological stations are very sparse and unevenly distributed in most arid and semi-arid regions for the adverse natural conditions [9,10], and the available precipitation data cannot meet the needs of the climate and hydrological research. Moreover, the existing meteorological stations are mostly located in plains or valleys, from which the observed precipitation data cannot effectively reflect the precipitation characteristics of the high lands, where the cold alpine areas are the runoff-forming regions [8].…”

Section: Introductionmentioning

confidence: 99%

Validation of TRMM 3B42V7 Rainfall Product under Complex Topographic and Climatic Conditions over Hexi Region in the Northwest Arid Region of China

Wang

Ding

Zhao

et al. 2018

Water

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Continuous and accurate spatiotemporal precipitation data plays an important role in regional climate and hydrology research, particularly in the arid inland regions where rain gauges are sparse and unevenly distributed. The main objective of this study is to evaluate and bias-correct the Tropical Rainfall Measuring Mission (TRMM) 3B42V7 rainfall product under complex topographic and climatic conditions over the Hexi region in the northwest arid region of China with the reference of rain gauge observation data during 2009-2015. A series of statistical indicators were adopted to quantitatively evaluate the error of 3B42V7 and its ability in detecting precipitation events. Overall, the 3B42V7 overestimates the precipitation with Bias of 11.16%, and its performance generally becomes better with the increasing of time scale. The agreements between the rain gauge data and 3B42V7 are very low in cold season, and moderate in warm season. The 3B42V7 shows better correlation with rain gauges located in the southern mountainous and central oasis areas than in the northern extreme arid regions, and is more likely to underestimate the precipitation in high-altitude mountainous areas and overestimate the precipitation in low-elevation regions. The distribution of the error on the daily scale is more related to the elevation and rainfall than in monthly and annual scale. The 3B42V7 significantly overestimates the precipitation events, and the overestimation mainly focuses on tiny amounts of rainfall (0-1 mm/d), which is also the range of false alarm concentration. Bias correction for 3B42V7 was carried out based on the deviation of the average monthly precipitation data during 2009-2015. The bias-corrected 3B42V7 was significantly improved compared with the original product. Results suggest that regional assessment and bias correction of 3B42V7 rainfall product are of vital importance and will provide substantive reference for regional hydrological studies.

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“…In recent years, a series of high-resolution satellite remote sensing precipitation products has provided a new source of data for global and regional precipitation observations that are widely used in the field of hydrometeorology [21][22][23][24]. Although satellite precipitation products have some deviation, they are spatially distributed to compensate for the lack of site observations [25].…”

Section: Introductionmentioning

confidence: 99%

Application of a Hybrid Interpolation Method Based on Support Vector Machine in the Precipitation Spatial Interpolation of Basins

Zhang

Liu

Wang³

et al. 2017

Water

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Abstract:In this paper, we applied the support vector machine (SVM) to the spatial interpolation of the multi-year average annual precipitation in the Three Gorges Region basin. By combining it with the inverse distance weighting and ordinary kriging method, we constructed the SVM residual inverse distance weighting, as well as the SVM residual kriging precipitation interpolation model and compared them with the inverse distance weighting, ordinary kriging, linear regression residual inverse distance weighting and linear regression residual kriging interpolation methods. The TRMM 3B43 V7 satellite precipitation information, which is processed by the latest revision algorithm, is used as the auxiliary variable for ground site precipitation interpolation along with latitude and elevation. Our results show that: (1) adding the TRMM 3B43 V7 satellite precipitation data as an auxiliary variable significantly improves the interpolation accuracy of the linear regression equation and SVM model; (2) the support vector machine hybrid interpolation method obtains superior interpolation results compared to the inverse distance weighting method, ordinary kriging method and linear regression hybrid interpolation method; (3) the interpolation accuracy of the SVM hybrid interpolation method depends on the SVM fitting degree, so we should choose a suitable fitting accuracy rather than the highest fitting accuracy; (4) the linear regression equation has a greater degree of dependency on the TRMM data than the SVM. The SVM accepts the TRMM data information while better maintaining its independence, taking into account that the TRMM data linear regression and linear regression hybrid interpolation method are not suitable for TRMM data evaluation.

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Evaluation of latest TMPA and CMORPH satellite precipitation products over Yellow River Basin

Cited by 62 publications

References 21 publications

Comprehensive Evaluation of Two Successive V3 and V4 IMERG Final Run Precipitation Products over Mainland China

Comprehensive Evaluation of Two Successive V3 and V4 IMERG Final Run Precipitation Products over Mainland China

Validation of TRMM 3B42V7 Rainfall Product under Complex Topographic and Climatic Conditions over Hexi Region in the Northwest Arid Region of China

Application of a Hybrid Interpolation Method Based on Support Vector Machine in the Precipitation Spatial Interpolation of Basins

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