气候变化领域集成服务门户(CCPortal): A two-stage blending approach for merging multiple satellite precipitation estimates and rain gauge observations: an experiment in the northeastern Tibetan Plateau

CCPortal

DOI	10.5194/hess-25-359-2021
	A two-stage blending approach for merging multiple satellite precipitation estimates and rain gauge observations: an experiment in the northeastern Tibetan Plateau
	Ma, Yingzhao; Sun, Xun; Chen, Haonan; Hong, Yang; Zhang, Yinsheng
通讯作者	Sun, X (通讯作者)
发表日期	2021
ISSN	1027-5606
EISSN	1607-7938
起始页码	359
结束页码	374
卷号	25 期号:1
英文摘要	Substantial biases exist in satellite precipitation estimates (SPEs) over complex terrain regions, and it has always been a challenge to quantify and correct such biases. The combination of multiple SPEs and rain gauge observations would be beneficial to improve the gridded precipitation estimates. In this study, a two-stage blending (TSB) approach is proposed, which firstly reduces the systematic errors of the original SPEs based on a Bayesian correction model and then merges the bias-corrected SPEs with a Bayesian weighting model. In the first stage, the gauge-based observations are assumed to be a generalized regression function of the SPEs and terrain feature. In the second stage, the relative weights of the bias-corrected SPEs are calculated based on the associated performances with ground references. The proposed TSB method has the ability to extract benefits from the bias-corrected SPEs in terms of higher performance and mitigate negative impacts from the ones with lower quality. In addition, Bayesian analysis is applied in the two phases by specifying the prior distributions on model parameters, which enables the posterior ensembles associated with their predictive uncertainties to be produced. The performance of the proposed TSB method is evaluated with independent validation data in the warm sea- son of 2010-2014 in the northeastern Tibetan Plateau. Results show that the blended SPE is greatly improved compared to the original SPEs, even in heavy rainfall events. This study can be expanded as a data fusion framework in the development of high-quality precipitation products in any region of interest.
关键词	GLOBAL PRECIPITATION RADAR-RAINFALL FORECASTS PRODUCTS CMORPH
语种	英语
WOS研究方向	Geology ; Water Resources
WOS类目	Geosciences, Multidisciplinary ; Water Resources
WOS记录号	WOS:000612667300002
来源期刊	HYDROLOGY AND EARTH SYSTEM SCIENCES
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/260502
推荐引用方式 GB/T 7714	Ma, Yingzhao,Sun, Xun,Chen, Haonan,et al. A two-stage blending approach for merging multiple satellite precipitation estimates and rain gauge observations: an experiment in the northeastern Tibetan Plateau[J]. 中国科学院青藏高原研究所,2021,25(1).
APA	Ma, Yingzhao,Sun, Xun,Chen, Haonan,Hong, Yang,&Zhang, Yinsheng.(2021).A two-stage blending approach for merging multiple satellite precipitation estimates and rain gauge observations: an experiment in the northeastern Tibetan Plateau.HYDROLOGY AND EARTH SYSTEM SCIENCES,25(1).
MLA	Ma, Yingzhao,et al."A two-stage blending approach for merging multiple satellite precipitation estimates and rain gauge observations: an experiment in the northeastern Tibetan Plateau".HYDROLOGY AND EARTH SYSTEM SCIENCES 25.1(2021).