干旱区生态环境知识资源中心(Arid): Which Precipitation Product Works Best in the Qinghai-Tibet Plateau, Multi-Source Blended Data, Global/Regional Reanalysis Data, or Satellite Retrieved Precipitation Data?

Arid

DOI	10.3390/rs12040683
	Which Precipitation Product Works Best in the Qinghai-Tibet Plateau, Multi-Source Blended Data, Global/Regional Reanalysis Data, or Satellite Retrieved Precipitation Data?
	Bai, Lei 1,2,3; Wen, Yuanqiao 1,2,4; Shi, Chunxiang 3; Yang, Yanfen 5; Zhang, Fan 2,4; Wu, Jing 6; Gu, Junxia 3; Pan, Yang 3; Sun, Shuai 3; Meng, Junyao 7
通讯作者	Shi, Chunxiang
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2020
卷号	12 期号:4
英文摘要	Precipitation serves as a crucial factor in the study of hydrometeorology, ecology, and the atmosphere. Gridded precipitation data are available from a multitude of sources including precipitation retrieved by satellites, radar, the output of numerical weather prediction models, and extrapolation by ground rain gauge data. Evaluating different types of products in ungauged regions with complex terrain will not only help researchers in applying scientific data, but also provide useful information that can be used to improve gridded precipitation products. The present study aims to evaluate comprehensively 12 precipitation datasets made by raw retrieved products, blended with rain gauge data, and blended multiple source datasets in multi-temporal scales in order to develop a suitable method for creating gridded precipitation data in regions with snow-dominated regions with complex terrain. The results show that the Multi-Source Weighted-Ensemble Precipitation (MSWEP), Global Satellite Mapping of Precipitation with Gauge Adjusted (GSMaP_GAUGE), Tropical Rainfall Measuring Mission (TRMM_3B42), Climate Prediction Center Morphing Technique blended with Chinese observations (CMORPH_SUN), and Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) can represent the spatial pattern of precipitation in arid/semi-arid and humid/semi-humid areas of the Qinghai-Tibet Plateau on a climatological spatial pattern. On interannual, seasonal, and monthly scales, the TRMM_3B42, GSMaP_GAUGE, CMORPH_SUN, and MSWEP outperformed the other products. In general, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN_CCS) has poor performance in basins of the Qinghai-Tibet Plateau. Most products overestimated the extreme indices of the 99(th) percentile of precipitation (R-99), the maximal of daily precipitation in a year (Rmax), and the maximal of pentad accumulation of precipitation in a year (R(5d)max). They were underestimated by the extreme index of the total number of days with daily precipitation less than 1 mm (dry day, DD). Compared to products blended with rain gauge data only, MSWEP blended with more data sources, and outperformed the other products. Therefore, multi-sources of blended precipitation should be the hotspot of regional and global precipitation research in the future.
英文关键词	arid semi-arid area complex terrain precipitation weather research and forecasting climate forecast system reanalysis data assimilation
类型	Article
语种	英语
国家	Peoples R China
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000519564600094
WOS关键词	GLOBAL PRECIPITATION ; GAUGE OBSERVATIONS ; RIVER-BASIN ; PASSIVE MICROWAVE ; HYDROLOGICAL APPLICATION ; PERSIANN-CDR ; MULTISATELLITE ; CMORPH ; RAINFALL ; CLIMATE
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
来源机构	西北农林科技大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/315428
作者单位	1.Wuhan Univ Technol, Intelligent Transportat Syst Res Ctr, Wuhan 430063, Peoples R China; 2.Wuhan Univ Technol, Sch Nav, Wuhan 430063, Peoples R China; 3.Natl Meteorol Informat Ctr, Beijing 100081, Peoples R China; 4.Hubei Key Lab Inland Shipping Technol, Wuhan 430063, Peoples R China; 5.Northwest A&F Univ, Inst Soil & Water Conservat, State Key Lab Soil Eros & Dryland Farming Loess P, Yangling 712100, Shaanxi, Peoples R China; 6.Lanzhou Cent Meteorol Observ, Lanzhou 730020, Peoples R China; 7.Chiyuan Sci Technol, Beijing 101108, Peoples R China
推荐引用方式 GB/T 7714	Bai, Lei,Wen, Yuanqiao,Shi, Chunxiang,et al. Which Precipitation Product Works Best in the Qinghai-Tibet Plateau, Multi-Source Blended Data, Global/Regional Reanalysis Data, or Satellite Retrieved Precipitation Data?[J]. 西北农林科技大学,2020,12(4).
APA	Bai, Lei.,Wen, Yuanqiao.,Shi, Chunxiang.,Yang, Yanfen.,Zhang, Fan.,...&Meng, Junyao.(2020).Which Precipitation Product Works Best in the Qinghai-Tibet Plateau, Multi-Source Blended Data, Global/Regional Reanalysis Data, or Satellite Retrieved Precipitation Data?.REMOTE SENSING,12(4).
MLA	Bai, Lei,et al."Which Precipitation Product Works Best in the Qinghai-Tibet Plateau, Multi-Source Blended Data, Global/Regional Reanalysis Data, or Satellite Retrieved Precipitation Data?".REMOTE SENSING 12.4(2020).