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Remote Sens. 2018, 10(2), 168;

Inter-Comparison and Evaluation of Remote Sensing Precipitation Products over China from 2005 to 2013

State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China
College of Environmental and Resource Science, Chengdu University of Information Technology, Chengdu 610225, China
Environmental Meteorological and 3S Application Technology Laboratory, Chongqing Institute of Meteorological Sciences, Chongqing 401147, China
University of Chinese Academy of Sciences, Beijing 100049, China
Beijing Huayun Shinetek Science and Technology Co., Ltd., Beijing 100081, China
Author to whom correspondence should be addressed.
Received: 7 December 2017 / Revised: 11 January 2018 / Accepted: 19 January 2018 / Published: 25 January 2018
PDF [6996 KB, uploaded 25 January 2018]


Precipitation is a key aspect of the climate system. In this paper, the dependability of five satellite precipitation products (TRMM [Tropical Rainfall Measuring Mission] 3BV42, PERSIANN [Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks] CDR, GSMaP [Global Satellite Mapping of Precipitation] RENALYSIS, CMORPH [Climate Prediction Center’s morphing technique] BLD and CMORPH_RAW) were compared with in situ measurements over China for the period of 2005 to 2013. To completely evaluate these precipitation products, the annual, seasonal and monthly precipitation averages were calculated. Overall, the Huaihe River and Qinlin mountains are shown to have heavy precipitation to the southeast and lighter precipitation to the northwest. The comparison results indicate that Gauge correction (CMORPH_BLD) improves the quality of the original satellite products (CMORPH_RAW), resulting in the higher correlation coefficient (CC), the low relative bias (BIAS) and root mean square error (RMSE). Over China, the GSMaP_RENALYSIS outperforms other products and shows the highest CC (0.91) and lowest RMSE (0.85 mm/day) and all products except for PERSIANN_CDR exhibit underestimation. GSMaP_RENALYSIS gives the highest of probability of detection (81%), critical success index (63%) and lowest false alarm ratio (36%) while TRMM3BV42 gives the highest of frequency bias index (1.00). Over Tibetan Plateau, CMORPH_RAW demonstrates the poorest performance with the biggest BIAS (4.2 mm/month) and lowest CC (0.22) in December 2013. GSMaP_RENALYSIS displays quite consistent with in situ measurements in summer. However, GSMaP_RENALYSIS and CMORPH_RAW underestimate precipitation over South China. CMORPH_BLD and TRMM3BV42 show consistent with high CC (>0.8) but relatively large RMSE in summer. View Full-Text
Keywords: precipitation; statistics methods; China; Tibetan Plateau; South China’s precipitation; statistics methods; China; Tibetan Plateau; South China’s

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Zeng, Q.; Wang, Y.; Chen, L.; Wang, Z.; Zhu, H.; Li, B. Inter-Comparison and Evaluation of Remote Sensing Precipitation Products over China from 2005 to 2013. Remote Sens. 2018, 10, 168.

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