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

作者:Xu, SG (Xu, Shiguang)[ 1 ] ; Shen, Y (Shen, Yan)[ 2 ] ; Du, Z (Du, Zhe)[ 3 ]

ATMOSPHERE

卷: 7  期: 12

文献号: 161

DOI: 10.3390/atmos7120161

出版年: DEC 2016

摘要

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.

通讯作者地址: Xu, SG (通讯作者)

Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.

地址:

[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

[ 2 ] China Meteorol Adm, Natl Meteorol Informat Ctr, Beijing 100081, Peoples R China

[ 3 ] Hebei Agr Univ, Coll Land & Resources, Baoding 071001, Peoples R China