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Remote Sens. 2016, 8(5), 440;

Error-Component Analysis of TRMM-Based Multi-Satellite Precipitation Estimates over Mainland China

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
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20742, USA
NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, OK 73019, USA
Author to whom correspondence should be addressed.
Academic Editors: Ken Harrison, Alfredo R. Huete and Prasad S. Thenkabail
Received: 16 February 2016 / Revised: 25 April 2016 / Accepted: 17 May 2016 / Published: 23 May 2016
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
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The Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) products have been widely used, but their error and uncertainty characteristics over diverse climate regimes still need to be quantified. In this study, we focused on a systematic evaluation of TMPA’s error characteristics over mainland China, with an improved error-component analysis procedure. We performed the analysis for both the TMPA real-time and research product suite at a daily scale and 0.25° × 0.25° resolution. Our results show that, in general, the error components in TMPA exhibit rather strong regional and seasonal differences. For humid regions, hit bias and missed precipitation are the two leading error sources in summer, whereas missed precipitation dominates the total errors in winter. For semi-humid and semi-arid regions, the error components of two real-time TMPA products show an evident topographic dependency. Furthermore, the missed and false precipitation components have the similar seasonal variation but they counter each other, which result in a smaller total error than the individual components. For arid regions, false precipitation is the main problem in retrievals, especially during winter. On the other hand, we examined the two gauge-correction schemes, i.e., climatological calibration algorithm (CCA) for real-time TMPA and gauge-based adjustment (GA) for post-real-time TMPA. Overall, our results indicate that the upward adjustments of CCA alleviate the TMPA’s systematic underestimation over humid region but, meanwhile, unfavorably increased the original positive biases over the Tibetan plateau and Tianshan Mountains. In contrast, the GA technique could substantially improve the error components for local areas. Additionally, our improved error-component analysis found that both CCA and GA actually also affect the hit bias at lower rain rates (particularly for non-humid regions), as well as at higher ones. Finally, this study recommends that future efforts should focus on improving hit bias of humid regions, false error of arid regions, and missed snow events in winter. View Full-Text
Keywords: remote sensing; satellite precipitation; TMPA; uncertainty; error component remote sensing; satellite precipitation; TMPA; uncertainty; error component

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Yong, B.; Chen, B.; Tian, Y.; Yu, Z.; Hong, Y. Error-Component Analysis of TRMM-Based Multi-Satellite Precipitation Estimates over Mainland China. Remote Sens. 2016, 8, 440.

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