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

Atmosphere

Hao Guo⁵  Sheng Chen⁴  Anming Bao⁵  Junjun Hu²  Banghui Yang¹  Phillip M. Stepanian³ 

[1] Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China;School of Computer Science, University of Oklahoma, Norman, OK 73072, USA;School of Meteorology, University of Oklahoma, Norman, OK 73072, USA;Key Laboratory of Beibu Gulf Environmental Evolution and Resources Utilization (Guangxi Teachers Education University), Ministry of Education, Nanning 530001, China;State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China;

关键词: satellite precipitation estimates;    error characteristic;    bias correction;   

DOI  :  10.3390/atmos7010006

来源: mdpi

【 摘 要 】

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%).

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.