干旱区生态环境知识资源中心(Arid): Quantitative estimation of hourly precipitation in the Tianshan Mountains based on area-to-point kriging downscaling and satellite-gauge data merging

Arid

DOI	10.1007/s11629-021-6901-5
	Quantitative estimation of hourly precipitation in the Tianshan Mountains based on area-to-point kriging downscaling and satellite-gauge data merging
	Lu Xin-yu; Chen Yuan-yuan; Tang Guo-qiang; Wang Xiu-qin; Liu Yan; Wei Ming
通讯作者	Chen, YY (corresponding author), Zhejiang Univ Technol, Coll Environm, Hangzhou 310014, Peoples R China.
来源期刊	JOURNAL OF MOUNTAIN SCIENCE
ISSN	1672-6316
EISSN	1993-0321
出版年	2022
英文摘要	Precipitation, a basic component of the water cycle, is significantly important for meteorological, climatological and hydrological research. However, accurate estimation on the precipitation remains considerably challenging because of the sparsity of gauge networks and the large spatial variability of precipitation over mountainous regions. Moreover, meteorological stations in mountainous areas are often dispersed and have difficulty in accurately reflecting the intensity and evolution of precipitation events. In this study, we proposed a novel method to produce high-quality, high-resolution precipitation estimates in the Tianshan Mountains, China, based on area-to-point kriging (ATPK) downscaling and a two-step correction, i.e., probability density function matching-optimum interpolation (PDF-OI). We obtained 1-km hourly precipitation data in the Tianshan Mountains by merging estimates from the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) product with observations from 1065 meteorological stations in the warm season (May to September) during 2016-2018. The spatial resolution and accuracy of the merged precipitation data greatly increased compared to IMERG. According to a cross-validation with gauged observations, the correlation coefficient (CC), probability of detection (POD) and critical success index (CSI) increased from 0.30, 0.50 and 0.24 for IMERG to 0.63, 0.65 and 0.38, respectively, for the merged estimates, and the root mean squared error (RMSE), mean error (ME) and false alarm ratio (FAR) decreased from 0.46 to 0.38 mm/h, 0.06 to 0.05 mm/h and 0.69 to 0.52, respectively. The proposed method will be useful for developing high-resolution precipitation estimates in mountainous areas such as central Asia and the Belt and Road Initiative regions.
英文关键词	Hourly precipitation Downscaling merging Tianshan Mountains IMERG Area-to-point kriging (ATPK)
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000733876400006
WOS关键词	DAY-1 IMERG ; ARID REGION ; PRODUCTS ; TMPA ; CHINA ; TRMM ; HEADWATERS ; REGRESSION ; ALGORITHM ; RUNOFF
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/374739
作者单位	[Lu Xin-yu; Wang Xiu-qin; Liu Yan] China Meteorol Adm, Inst Desert Meteorol, Urumqi 830002, Peoples R China; [Lu Xin-yu] Cent Asia Res Ctr Atmosphere Sci, Urumqi 830002, Peoples R China; [Chen Yuan-yuan] Zhejiang Univ Technol, Coll Environm, Hangzhou 310014, Peoples R China; [Tang Guo-qiang] Univ Saskatchewan, Coldwater Lab, Canmore, AB T1W 3G1, Canada; [Tang Guo-qiang] Univ Saskatchewan, Ctr Hydrol, Saskatoon, SK S7N 5A2, Canada; [Wei Ming] Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, Nanjing 210044, Peoples R China
推荐引用方式 GB/T 7714	Lu Xin-yu,Chen Yuan-yuan,Tang Guo-qiang,et al. Quantitative estimation of hourly precipitation in the Tianshan Mountains based on area-to-point kriging downscaling and satellite-gauge data merging[J],2022.
APA	Lu Xin-yu,Chen Yuan-yuan,Tang Guo-qiang,Wang Xiu-qin,Liu Yan,&Wei Ming.(2022).Quantitative estimation of hourly precipitation in the Tianshan Mountains based on area-to-point kriging downscaling and satellite-gauge data merging.JOURNAL OF MOUNTAIN SCIENCE.
MLA	Lu Xin-yu,et al."Quantitative estimation of hourly precipitation in the Tianshan Mountains based on area-to-point kriging downscaling and satellite-gauge data merging".JOURNAL OF MOUNTAIN SCIENCE (2022).