Evaluation of latest GPM-Era high-resolution satellite precipitation products during the May 2017 Guangdong extreme rainfall event

Zhang, Asi; Xiao, Liusi; Min, Chao; Chen, Sheng; Kulie, Mark S.; Huang, Chaoying; Liang, Zhenqing

doi:10.1016/j.atmosres.2018.09.018

Cited by 54 publications

(25 citation statements)

References 39 publications

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“…Since the release of IMERG products, a number of studies have been conducted to evaluate and compare the performance of TMPA and IMERG products with reference to rain gauges observations in various regions of the world, e.g., China [29][30][31][32][33][34], Singapore [25], Malaysia [15][16][17], Pakistan [13,14], South America [35], and Cyprus [36]. For instance, Tan and Duan evaluated IMERG and 3B42 precipitation products on daily, monthly, seasonal, and annual scales over Singapore.…”

Section: Introductionmentioning

confidence: 99%

Systematical Evaluation of GPM IMERG and TRMM 3B42V7 Precipitation Products in the Huang-Huai-Hai Plain, China

Guo

et al. 2019

Remote Sensing

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Accurate estimation of high-resolution satellite precipitation products like Global Precipitation Measurement (GPM) and Tropical Rainfall Measuring Mission (TRMM) is critical for hydrological and meteorological research, providing a benchmark for the continued development and future improvement of these products. This study aims to comprehensively evaluate the Integrated Multi-Satellite Retrievals for GPM (IMERG) and TRMM 3B42V7 products at multiple temporal scales from 1 January 2015 to 31 December 2017 over the Huang-Huai-Hai Plain in China, using daily precipitation data from 59 meteorological stations. Three commonly used statistical metrics (CC, RB, and RMSE) are adopted to quantitatively verify the accuracy of two satellite precipitation products. The assessment also takes into account the precipitation detection capability (POD, FAR, CSI, and ACC) and frequency of different precipitation intensities. The results show that the IMERG and 3B42V7 present strong correlation with meteorological stations observations at annual and monthly scales (CC > 0.90), whereas moderate at the daily scale (CC = 0.76 and 0.69 for IMERG and 3B42V7, respectively). The spatial variability of the annual and seasonal precipitation is well captured by these two satellite products. And spatial patterns of precipitation gradually decrease from south to north over the Huang-Huai-Hai Plain. Both IMERG and 3B42V7 products overestimate precipitation compared with the station observations, of which 3B42V7 has a lower degree of overestimation. Relative to the IMERG, annual precipitation estimates from 3B42V7 show lower RMSE (118.96 mm and 142.67 mm, respectively), but opposite at the daily, monthly, and seasonal scales. IMERG has a better precipitation detection capability than 3B42V7 (POD = 0.83 and 0.67, respectively), especially when detecting trace and solid precipitation. The two precipitation products tend to overestimate moderate (2–10 mm/d) and heavy (10–50 mm/d) precipitation events, but underestimate violent (>50 mm/d) precipitation events. The IMERG is not found capable to detecting precipitation events of different frequencies more precisely. In general, the accuracy of IMERG is better than 3B42V7 product in the Huang-Huai-Hai Plain. The IMERG satellite precipitation product with higher temporal and spatial resolutions can be regarded a reliable data sources in studying hydrological and climatic research.

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Section: Introductionmentioning

confidence: 99%

Systematical Evaluation of GPM IMERG and TRMM 3B42V7 Precipitation Products in the Huang-Huai-Hai Plain, China

Guo

et al. 2019

Remote Sensing

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“…AghaKouchak et al [239] have demonstrated that all products tend to miss a significant volume of rainfall and to worsen their performance as the choice of extreme precipitation threshold increased. Resolution and accuracy limitations still persist in estimating extreme precipitation [240]. The definition of the accuracy in representing extreme rainfall events for varying time aggregation intervals is one more problem.…”

Section: Observing Extremesmentioning

confidence: 99%

Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate

Levizzani

Cattani

2019

Remote Sensing

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The water cycle is the most essential supporting physical mechanism ensuring the existence of life on Earth. Its components encompass the atmosphere, land, and oceans. The cycle is composed of evaporation, evapotranspiration, sublimation, water vapor transport, condensation, precipitation, runoff, infiltration and percolation, groundwater flow, and plant uptake. For a correct closure of the global water cycle, observations are needed of all these processes with a global perspective. In particular, precipitation requires continuous monitoring, as it is the most important component of the cycle, especially under changing climatic conditions. Passive and active sensors on board meteorological and environmental satellites now make reasonably complete data available that allow better measurements of precipitation to be made from space, in order to improve our understanding of the cycle’s acceleration/deceleration under current and projected climate conditions. The article aims to draw an up-to-date picture of the current status of observations of precipitation from space, with an outlook to the near future of the satellite constellation, modeling applications, and water resource management.

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“…The correlation coefficient (CC), the relative bias (RB) and the root-mean-square error (RMSE) are explored to quantify the comparison. These statistic metrics are calculated as follows (Chen et al, 2013;Zhang et al, 2019):…”

Section: Validation Of Cmst Datamentioning

confidence: 99%

Sea ice export through the Fram Strait derived from a combined model and satellite data set

Min

Yang

et al. 2019

The Cryosphere

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Abstract. Sea ice volume export through the Fram Strait plays an important role in the Arctic freshwater and energy redistribution. The combined model and satellite sea ice thickness (CMST) data set assimilates CryoSat-2 and soil moisture and ocean salinity (SMOS) thickness products together with satellite sea ice concentration. The CMST data set closes the gap of stand-alone satellite-derived sea ice thickness in summer and therefore allows us to estimate sea ice volume export during the melt season. In this study, we first validate the CMST data set using field observations, and then we estimate the continuous seasonal and interannual variations in Arctic sea ice volume flux through the Fram Strait from September 2010 to December 2016. The results show that seasonal and interannual sea ice volume export vary from about -240(±40) to -970(±60) km3 and -1970(±290) to -2490(±280) km3, respectively. The sea ice volume export reaches its maximum in spring and about one-third of the yearly total volume export occurs in the melt season. The minimum monthly sea ice export is −11 km3 in August 2015, and the maximum (−442 km3) appears in March 2011. The seasonal relative frequencies of sea ice thickness and drift suggest that the Fram Strait outlet in summer is dominated by sea ice that is thicker than 2 m with relatively slow seasonal mean drift of about 3 km d−1.

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Evaluation of latest GPM-Era high-resolution satellite precipitation products during the May 2017 Guangdong extreme rainfall event

Cited by 54 publications

References 39 publications

Systematical Evaluation of GPM IMERG and TRMM 3B42V7 Precipitation Products in the Huang-Huai-Hai Plain, China

Systematical Evaluation of GPM IMERG and TRMM 3B42V7 Precipitation Products in the Huang-Huai-Hai Plain, China

Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate

Sea ice export through the Fram Strait derived from a combined model and satellite data set

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