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Atmosphere 2016, 7(1), 6; doi:10.3390/atmos7010006

Comprehensive Evaluation of High-Resolution Satellite-Based Precipitation Products over China

1
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Key Laboratory of Beibu Gulf Environmental Evolution and Resources Utilization (Guangxi Teachers Education University), Ministry of Education, Nanning 530001, China
4
School of Computer Science, University of Oklahoma, Norman, OK 73072, USA
5
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
6
School of Meteorology, University of Oklahoma, Norman, OK 73072, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Robert W. Talbot
Received: 28 October 2015 / Revised: 4 December 2015 / Accepted: 8 December 2015 / Published: 31 December 2015
View Full-Text   |   Download PDF [8539 KB, uploaded 31 December 2015]   |  

Abstract

Characterizing the errors in satellite-based precipitation estimation products is crucial for understanding their effects in hydrological applications. Six precipitation products derived from three algorithms are comprehensively evaluated against gauge data over mainland China from December 2006 to November 2010. These products include three satellite-only estimates: the Global Satellite Mapping of Precipitation Microwave-IR Combined Product (GSMaP_MVK), the Climate Prediction Center (CPC) MORPHing (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), as well as their gauge-corrected counterparts: the GSMaP Gauge-calibrated Product (GSMaP_Gauge), bias-corrected CMORPH (CMORPH_CRT), and PERSIANN Climate Data Record (PERSIANN-CDR). Overall, the bias-correction procedures largely reduce various errors for the three groups of satellite-based precipitation products. GSMaP_Gauge produces better fractional coverage with the highest correlation (0.95) and the lowest RMSE (0.53 mm/day) but also high RB (15.77%). In general, CMORPH_CRT amounts are closer to the gauge reference. CMORPH shows better performance than GSMaP_MVK and PERSIANN with the highest CC (0.82) and the lowest RMSE (0.93 mm/day), but also presents a relatively high RB (−19.60%). In winter, all six satellite precipitation estimates have comparatively poor capability, with the IR-based PERSIANN_CDR exhibiting the closest performance to the gauge reference. Both satellite-only and gauge-corrected satellite products show poor capability in detecting occurrence of precipitation with a low POD (<50%) and CSI (<35%) and a high FAR (>40%). View Full-Text
Keywords: satellite precipitation estimates; error characteristic; bias correction satellite precipitation estimates; error characteristic; bias correction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Guo, H.; Chen, S.; Bao, A.; Hu, J.; Yang, B.; Stepanian, P.M. Comprehensive Evaluation of High-Resolution Satellite-Based Precipitation Products over China. Atmosphere 2016, 7, 6.

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