Early assessment of Integrated Multi-satellite Retrievals for Global Precipitation Measurement over China

Guo, Hao; Chen, Sheng; Bao, Anming; Behrangi, Ali; Hong, Yang; Ndayisaba, Felix; Hu, Junjun; Stepanian, Phillip M.

doi:10.1016/j.atmosres.2016.02.020

Cited by 202 publications

(167 citation statements)

References 60 publications

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“…A few recent studies have proven that the GPM IMERG products are generally superior to TRMM in several regions, such as the Xinjiang region [30] and the Qinghai-Tibetan Plateau in China [30,31]; Mainland China [33,34]; Guilan, Bushehr, Kermanshah, and Tehran regions in Iran [37]; Far-East Asia [35]; and India [36]. However, in the present study, although the daily precipitation data of both the IMERG final run and TMPA 3B42V7 captured the spatiotemporal variation patterns of rainfall events in the Chindwin River basin in Myanmar, no significant improvements were found in IMERG.…”

Section: Discussionmentioning

confidence: 99%

“…Several previous studies [31][32][33][34][35][36][37] found that, although 3B42V7 and IMERG products effectively capture the spatiotemporal variations of precipitation in different regions around the world, these estimates still contain considerable errors when compared with ground observations. Given that precipitation inputs are among the most dominant uncertainty sources for hydrological models, satellite precipitation products must be bias-corrected when adopted as the input of a hydrological model for streamflow simulations.…”

Section: Bias-correction For Satellite Precipitation Productsmentioning

confidence: 99%

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Assessment of GPM and TRMM Multi-Satellite Precipitation Products in Streamflow Simulations in a Data-Sparse Mountainous Watershed in Myanmar

et al. 2017

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Satellite precipitation products from the Global Precipitation Measurement (GPM) mission and its predecessor the Tropical Rainfall Measuring Mission (TRMM) are a critical data source for hydrological applications in ungauged basins. This study conducted an initial and early evaluation of the performance of the Integrated Multi-satellite Retrievals for GPM (IMERG) final run and the TRMM Multi-satellite Precipitation Analysis 3B42V7 precipitation products, and their feasibility in streamflow simulations in the Chindwin River basin, Myanmar, from April 2014 to December 2015 was also assessed. Results show that, although IMERG and 3B42V7 can potentially capture the spatiotemporal patterns of historical precipitation, the two products contain considerable errors. Compared with 3B42V7, no significant improvements were found in IMERG. Moreover, 3B42V7 outperformed IMERG at daily and monthly scales and in heavy rain detections at four out of five gauges. The large errors in IMERG and 3B42V7 distinctly propagated to streamflow simulations via the Xinanjiang hydrological model, with a significant underestimation of total runoff and high flows. The bias correction of the satellite precipitation effectively improved the streamflow simulations. The 3B42V7-based streamflow simulations performed better than the gauge-based simulations. In general, IMERG and 3B42V7 are feasible for use in streamflow simulations in the study area, although 3B42V7 is better suited than IMERG.

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Section: Discussionmentioning

confidence: 99%

Section: Bias-correction For Satellite Precipitation Productsmentioning

confidence: 99%

Assessment of GPM and TRMM Multi-Satellite Precipitation Products in Streamflow Simulations in a Data-Sparse Mountainous Watershed in Myanmar

et al. 2017

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“…Compared to other interpolation algorithms, the OI technique results in a higher correlation with the ground-based rain gauge observations [47]. Additionally, a previous study has indicated that the relative bias (RB) of the CPAP is about 3% when compared to individual gauge measurements at a 0.5° scale, which can be used to validate the satellite precipitation accuracy at 0.25° [35,43].…”

Section: Gauge-observed Datamentioning

confidence: 99%

“…The details for calculating the five statistics have been provided by previous studies [35,43]. The metrics are calculated at the pixel scale over China to determine the spatial distribution of errors.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…The V4 IMERG data prior to March 2014 will also be processed retrospectively in late 2017. To date, researchers have identified both the uncertainty and error characteristics of the V3 IMERG datasets [34][35][36][37][38][39][40][41]. At present, a quantitative evaluation of the similarities and differences between the successive V3 and V4 products and of the global improvements of V4 over V3 are urgently required.…”

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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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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Systematical Evaluation of Satellite Precipitation Estimates Over Central Asia Using an Improved Error‐Component Procedure

et al. 2017

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Satellite precipitation estimates (SPEs) provide important alternative precipitation sources for various applications especially for regions where in situ observations are limited or unavailable, like central Asia. In this study, eight SPEs based on four different algorithms, namely, the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis 3B42, Climate Prediction Center morphing technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP), and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks are evaluated by using an improved evaluation system over central Asia with respect to their performance in capturing precipitation occurrence and magnitude. Both satellite‐only and gauge‐corrected versions are assessed against gauge‐gridded reference from June 2001 to May 2006. Main results show that all SPEs have difficulties in accurately estimating mountainous precipitation with great overestimation/underestimation in both winter and summer. In winter, CMORPH products fail to capture events over ice‐/snow‐covered region. In summer, large overestimations dominated by positive hit bias and missed precipitation are found for all products in northern central Asia. Interestingly, 3B42 and CMORPH products show great false alarm percentages (up to 90%) over lake region, which is more significant in summer than in winter. Significant elevation‐dependent errors exist in all products, especially for the high‐altitude regions (>3,000 m) with missed error and hit error being the two leading errors. Satellite‐only products have large systematic and random errors, while the gauge‐corrected products demonstrate significant improvements in reducing random errors. Generally, the gauge‐corrected GSMaP performs better than others with good skills in reducing various errors.

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Early assessment of Integrated Multi-satellite Retrievals for Global Precipitation Measurement over China

Cited by 202 publications

References 60 publications

Assessment of GPM and TRMM Multi-Satellite Precipitation Products in Streamflow Simulations in a Data-Sparse Mountainous Watershed in Myanmar

Assessment of GPM and TRMM Multi-Satellite Precipitation Products in Streamflow Simulations in a Data-Sparse Mountainous Watershed in Myanmar

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

Systematical Evaluation of Satellite Precipitation Estimates Over Central Asia Using an Improved Error‐Component Procedure

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