Assessment of the Latest GPM-Era High-Resolution Satellite Precipitation Products by Comparison with Observation Gauge Data over the Chinese Mainland

Ning, Shaowei; Wang, Jie; Jin, Juliang; Ishidaira, Hiroshi

doi:10.3390/w8110481

Cited by 64 publications

(42 citation statements)

References 39 publications

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“…Previous studies suggested that a better discharge simulation could be performed when satellite-based precipitation products with inferior accuracy are compared to gauges combined with appropriate hydrological models [21]. However, since there are no records of the hydrological simulation between IMERGF-V3 and V4, further studies about their utility in hydrology and associated uncertainty analyses should be conducted.…”

Section: Discussionmentioning

confidence: 99%

“…Figure 2 shows that the GSMaP product results in a more accurate spatial precipitation pattern than the IMERGF-V3 or IMERGF-V4 products. Two factors may be responsible for this phenomenon: first, daily CPC observed data are used by the GSMaP product to adjust the precipitation bias, which performs better than the monthly gauge analyses (GPCC) used in the IMERGF products; second, the Japan Meteorological Agency Global Analysis (GANAL) data and Merged Satellite and in-situ Global Daily Sea Surface Temperature (MGDSST) are used in the GSMaP product to calculate lookup tables, which are then used by the GSMap microwave imager and sounder algorithms [21]. Additionally, another advantage for GSMaP is its short latency time for release (one or two days) in comparison with IMERGF's two to four months.…”

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%

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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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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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“…The IMERG estimate takes advantages of three prior precipitation retrieval algorithms (PERSIANN-CCS, CMORPH, and TMPA). It combines precipitation measurements from various PMW sensors of GPM constellation and infrared estimates on geosynchronous satellites and is finally adjusted by monthly gauge data from GPCC (Global Precipitation Climatology Centre) Monitoring Product (Version 4) using the method applied in TMPA [16]. Readers can refer to Huffman et al [25,26] and Hou et al [6] for more detailed introduction about IMERG products.…”

Section: Satellite-based Precipitationmentioning

confidence: 99%

“…Currently, few researchers have assessed the accuracy of these products over China. Chen and Li [15] and Ning et al [16] analyzed IMERG product error over Mainland China for a year and 20 months, respectively, by comparing satellite precipitation products with about 800 rain-gauge stations. However, the above analyses were based on the monthly scale, and the approximate 800 discrete rain-gauge stations that were used for the analysis could not provide a very detailed precipitation observation for error analysis in spatial.…”

Section: Introductionmentioning

confidence: 99%

Error Analysis and Evaluation of the Latest GSMap and IMERG Precipitation Products over Eastern China

Ning

Song

Udmale

et al. 2017

Advances in Meteorology

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The present study comprehensively analyzes error characteristics and performance of the two latest GPM-era satellite precipitation products over eastern China from April 2014 to March 2016. Analysis results indicate that the two products have totally different spatial distributions of total bias. Many of the underestimations for the GSMap-gauged could be traced to significant hit bias, with a secondary contribution from missed precipitation. For IMERG, total bias illustrates significant overestimation over most of the eastern part of China, except upper reaches of Yangtze and Yellow River basins. GSMap-gauged tends to overestimate light precipitation (<16 mm/day) and underestimate precipitation with rain rate larger than 16 mm/day; however, IMERG underestimates precipitation at rain rate between 8 and 64 mm/day and overestimates precipitation at rain rate more than 64 mm/day. IMERG overestimates extreme precipitation indices (RR99P and R20TOT), with relative bias values of 17.9% and 11.5%, respectively. But GSMap-gauged shows significant underestimation of these indices. In addition, both products performed well in the Huaihe, Liaohe, and Yangtze River basins for extreme precipitation detection. At basin scale comparisons, the GSMap-gauged data has a relatively higher accuracy than IMERG, especially at the Haihe, Huaihe, Liaohe, and Yellow River basins.

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Spatial distribution of the level of return of extreme precipitation events in Northeast Brazil

Rodrigues

Gonçalves

Spyrides

et al. 2020

Intl Journal of Climatology

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This study aimed to estimate levels of return of extreme daily precipitation events, associating them with natural disasters in Northeast Brazil (NEB), a region characterized by different climatic conditions and low rates of social and economic development. For this, generalized Pareto distribution (GPD) models were adjusted to the daily extreme precipitation data estimated by the Tropical Rainfall Measuring Mission (TRMM) 3B42 product of the multisatellite precipitation analysis for a period of 16 years (2000-2015). In addition, the estimates of the GPD model were compared using two data sources, TRMM and pluviometer. The investigation showed that the results of the GPD model estimated by means of the extreme data from the rain gauge and the TRMM were statistically the same, with 95% confidence. Thus, using the data referring to the 2,082 grid points of the TRMM, it was possible to map the spatial distribution of the estimates of the levels of return of extreme precipitation to the return periods of 2, 5 and 10 years, per seasonal period. In general, the results indicated that the intensity of expected extreme precipitation depends on the seasonal period and the place of occurrence of precipitation. The eastern NEB stood out as the region where the highest intensities of extreme precipitation are expected. Extreme precipitation values of up to 178 mm are expected in 2 years. The areas where natural disasters occurred in the years 2016, 2017 and 2018 are similar to those in which the highest rainfall intensities are expected. The results of this study can allow the evaluation of the spatial distribution of risks related to extreme precipitation events, and therefore, support the planning of regional public policies and environmental management for the prevention of natural disasters in NEB. K E Y W O R D S diaries precipitation, generalized Pareto distribution, natural disaster, TRMM 1 | INTRODUCTION Extreme weather events are one of the main topics studied related to climate change. Future projections released by the Intergovernmental Panel on Climate Change (IPCC) show that these events will become increasingly frequent and intense in several regions of the world, including in areas of Northeast Brazil (NEB; Du et al., 2019). Extreme weather events can cause waves of heat and cold, floods, landslides, droughts and more.

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Assessment of the Latest GPM-Era High-Resolution Satellite Precipitation Products by Comparison with Observation Gauge Data over the Chinese Mainland

Cited by 64 publications

References 39 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

Error Analysis and Evaluation of the Latest GSMap and IMERG Precipitation Products over Eastern China

Spatial distribution of the level of return of extreme precipitation events in Northeast Brazil

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