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Atmosphere 2016, 7(12), 161; doi:10.3390/atmos7120161

Tracing the Source of the Errors in Hourly IMERG Using a Decomposition Evaluation Scheme

1
The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2
National Meteorological Information Center (NMIC), China Meteorological Administration (CMA), Beijing 100081, China
3
College of Land and Resources, Hebei Agricultural University, Baoding 071001, China
*
Author to whom correspondence should be addressed.
Academic Editor: Nicole Mölders
Received: 27 October 2016 / Revised: 3 December 2016 / Accepted: 8 December 2016 / Published: 13 December 2016
(This article belongs to the Special Issue Global Precipitation with Climate Change)
View Full-Text   |   Download PDF [5994 KB, uploaded 13 December 2016]   |  

Abstract

Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) is an important satellite precipitation product of Global Precipitation Measurement (GPM) mission. Quantitative information about the errors of IMERG has great significance for the data developers and end users. In order to investigate the characteristics and the source of the errors contained in IMERG, a bias-decomposition scheme was employed to evaluate the hourly IMERG over the eastern part of Mainland China during the warm season. First, the total bias of IMERG before and after calibration (termed as precipitationUncal and precipitationCal) was calculated using rain gauge measurements as reference. Then the bias was decomposed into three independent components including false bias, missed bias, and hit bias. Finally, the hit bias was further decomposed according to the rainfall intensity measured by rain gauges. The results indicate that (1) the bias of precipitationUncal over the north part is dominated by hit bias and false bias, leading to the serious overestimation for the precipitation over this area, but it underestimates the precipitation over the south part with the false bias and missed bias acting as major contributors; (2) the precipitationCal overestimates the precipitation over more than 80% of the study areas mainly as a result of a large amplitude of false bias; (3) the calibration algorithm used by IMERG could not reduce the missed bias and enlarges the false bias over some regions, revealing a shortcoming of this algorithm in that it could not effectively alleviate the bias resulting from the rain areas delineation; (4) the hit bias of IMERG is strongly related with the rainfall intensity of rain gauge measurements, which should be beneficial for reducing the errors of IMERG. This study provides a deep insight into the characteristics and sources of the biases inherent in IMERG, which is significant for its utilization and possible correction in future. View Full-Text
Keywords: IMERG; uncertainty analysis; bias decomposition; mainland China IMERG; uncertainty analysis; bias decomposition; mainland China
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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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Xu, S.; Shen, Y.; Du, Z. Tracing the Source of the Errors in Hourly IMERG Using a Decomposition Evaluation Scheme. Atmosphere 2016, 7, 161.

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