气候变化领域集成服务门户(CCPortal): A novel bias correction framework of TMPA 3B42 daily precipitation data using similarity matrix/homogeneous conditions

CCPortal

DOI	10.1016/j.scitotenv.2019.133680
	A novel bias correction framework of TMPA 3B42 daily precipitation data using similarity matrix/homogeneous conditions
	Choubin, Bahram; Khalighi-Sigaroodi, Shahram; Mishra, Ashok; Goodarzi, Massoud; Shamshirband, Shahaboddin; Ghaljaee, Esmatullah; Zhang, Fan
通讯作者	Khalighi-Sigaroodi, S (通讯作者)
发表日期	2019
ISSN	0048-9697
EISSN	1879-1026
卷号	694
英文摘要	Reduction of bias in remotely sensed precipitation products is a major challenge in environment modeling, hydrology, and managing the water resources. Various bias correction techniques are applied to reduce the bias from pixel to gauge data. However, a successful methodology to improve bias correction on the daily scale is often challenging and limited. We present a methodology that can be used to correct the daily bias in remote sensing rainfall data, and to demonstrate the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 data was used. The proposed bias correction method is based on the concept of similarity (homogeneous) conditions developed based on the periodicity and different percentile-based precipitation amount, and by identifying the best donor pixel to transfer bias correction factor to a specific ungauged pixel (the receptor pixel) based on the similarity (elevation, latitude, and longitude). Bias correction factors were obtained using the mean bias-removal (MBR) and multiplicative ratio (MR) techniques in the cells of the similarity matrix. The proposed methodology demonstrates a significant removal of bias associated with TMPA 3B42 data sets and it is capable of removing the bias in daily precipitation data on an average by 57% (51%) in the gauged pixels, and 25% (22%) in the ungauged pixels for MBR (MR) method. (C) 2019 Elsevier B.V. All rights reserved.
关键词	SOIL-MOISTURE SATELLITE PRECIPITATION DOWNSCALING ALGORITHM RIVER-BASIN MODEL RAINFALL IMPROVEMENT ADJUSTMENT FORECASTS ACCURACY
英文关键词	Daily bias correction; TMPA 3B42; Similarity matrix; Mean bias-removal; Multiplicative ratio
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Environmental Sciences
WOS记录号	WOS:000496780900019
来源期刊	SCIENCE OF THE TOTAL ENVIRONMENT
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/259563
推荐引用方式 GB/T 7714	Choubin, Bahram,Khalighi-Sigaroodi, Shahram,Mishra, Ashok,et al. A novel bias correction framework of TMPA 3B42 daily precipitation data using similarity matrix/homogeneous conditions[J]. 中国科学院青藏高原研究所,2019,694.
APA	Choubin, Bahram.,Khalighi-Sigaroodi, Shahram.,Mishra, Ashok.,Goodarzi, Massoud.,Shamshirband, Shahaboddin.,...&Zhang, Fan.(2019).A novel bias correction framework of TMPA 3B42 daily precipitation data using similarity matrix/homogeneous conditions.SCIENCE OF THE TOTAL ENVIRONMENT,694.
MLA	Choubin, Bahram,et al."A novel bias correction framework of TMPA 3B42 daily precipitation data using similarity matrix/homogeneous conditions".SCIENCE OF THE TOTAL ENVIRONMENT 694(2019).