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Remote Sens. 2015, 7(6), 7181-7211; doi:10.3390/rs70607181

Inter-Comparison of High-Resolution Satellite Precipitation Products over Central Asia

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 100039, 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
Hydrometeorology and Remote Sensing Laboratory and School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK 73072, USA
6
Advanced Radar Research Center, National Weather Center, Norman, OK 73072, USA
7
State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, China
*
Author to whom correspondence should be addressed.
Academic Editors: Richard Gloaguen and Prasad S. Thenkabail
Received: 20 February 2015 / Revised: 17 April 2015 / Accepted: 18 May 2015 / Published: 1 June 2015
View Full-Text   |   Download PDF [28042 KB, uploaded 2 June 2015]   |  

Abstract

This paper examines the spatial error structures of eight precipitation estimates derived from four different satellite retrieval algorithms including TRMM Multi-satellite Precipitation Analysis (TMPA), Climate Prediction Center morphing technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). All the original satellite and bias-corrected products of each algorithm (3B42RTV7 and 3B42V7, CMORPH_RAW and CMORPH_CRT, GSMaP_MVK and GSMaP_Gauge, PERSIANN_RAW and PERSIANN_CDR) are evaluated against ground-based Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) over Central Asia for the period of 2004 to 2006. The analyses show that all products except PERSIANN exhibit overestimation over Aral Sea and its surrounding areas. The bias-correction improves the quality of the original satellite TMPA products and GSMaP significantly but slightly in CMORPH and PERSIANN over Central Asia. 3B42RTV7 overestimates precipitation significantly with large Relative Bias (RB) (128.17%) while GSMaP_Gauge shows consistent high correlation coefficient (CC) (>0.8) but RB fluctuates between −57.95% and 112.63%. The PERSIANN_CDR outperforms other products in winter with the highest CC (0.67). Both the satellite-only and gauge adjusted products have particularly poor performance in detecting rainfall events in terms of lower POD (less than 65%), CSI (less than 45%) and relatively high FAR (more than 35%). View Full-Text
Keywords: satellite-based precipitation estimates; bias correction; quantitative precipitation estimation; error characteristic; Central Asia satellite-based precipitation estimates; bias correction; quantitative precipitation estimation; error characteristic; Central Asia
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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.; Gebregiorgis, A.S.; Xue, X.; Zhang, X. Inter-Comparison of High-Resolution Satellite Precipitation Products over Central Asia. Remote Sens. 2015, 7, 7181-7211.

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