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Open AccessArticle

Impact of Missing Passive Microwave Sensors on Multi-Satellite Precipitation Retrieval Algorithm

State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, SOA, Hangzhou 310012, China
School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, OK 73019, USA
NOAA/National Severe Storms Laboratory, Norman, OK 73072, USA
Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
Author to whom correspondence should be addressed.
Academic Editors: Xin Li, Yuei-An Liou, Qinhuo Liu and Prasad S. Thenkabail
Remote Sens. 2015, 7(1), 668-683;
Received: 10 October 2014 / Accepted: 6 January 2015 / Published: 9 January 2015
The impact of one or two missing passive microwave (PMW) input sensors on the end product of multi-satellite precipitation products is an interesting but obscure issue for both algorithm developers and data users. On 28 January 2013, the Version-7 TRMM Multi-satellite Precipitation Analysis (TMPA) products were reproduced and re-released by National Aeronautics and Space Administration (NASA) Goddard Space Flight Center because the Advanced Microwave Sounding Unit-B (AMSU-B) and the Special Sensor Microwave Imager-Sounder-F16 (SSMIS-F16) input data were unintentionally disregarded in the prior retrieval. Thus, this study investigates the sensitivity of TMPA algorithm results to missing PMW sensors by intercomparing the “early” and “late” Version-7 TMPA real-time (TMPA-RT) precipitation estimates (i.e., without and with AMSU-B, SSMIS-F16 sensors) with an independent high-density gauge network of 200 tipping-bucket rain gauges over the Chinese Jinghe river basin (45,421 km2). The retrieval counts and retrieval frequency of various PMW and Infrared (IR) sensors incorporated into the TMPA system were also analyzed to identify and diagnose the impacts of sensor availability on the TMPA-RT retrieval accuracy. Results show that the incorporation of AMSU-B and SSMIS-F16 has substantially reduced systematic errors. The improvement exhibits rather strong seasonal and topographic dependencies. Our analyses suggest that one or two single PMW sensors might play a key role in affecting the end product of current combined microwave-infrared precipitation estimates. This finding supports algorithm developers’ current endeavor in spatiotemporally incorporating as many PMW sensors as possible in the multi-satellite precipitation retrieval system called Integrated Multi-satellitE Retrievals for Global Precipitation Measurement mission (IMERG). This study also recommends users of satellite precipitation products to switch to the newest Version-7 TMPA datasets and the forthcoming IMERG products whenever they become available. View Full-Text
Keywords: satellite precipitation; TRMM; GPM; IMERG; TMPA; hydrological application satellite precipitation; TRMM; GPM; IMERG; TMPA; hydrological application
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Yong, B.; Chen, B.; Hong, Y.; Gourley, J.J.; Li, Z. Impact of Missing Passive Microwave Sensors on Multi-Satellite Precipitation Retrieval Algorithm. Remote Sens. 2015, 7, 668-683.

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