Assimilation of Multi-Source Precipitation Data over Southeast China Using a Nonparametric Framework

doi:10.3390/rs13061057

	Assimilation of Multi-Source Precipitation Data over Southeast China Using a Nonparametric Framework
	Zhou, Yuanyuan 1,2,5; Qin, Nianxiu 4; Tang, Qiuhong 3,6; Shi, Huabin 1,2; Gao, Liang 1,2,5
刊名	REMOTE SENSING
	2021-03-01
卷号	13 期号:6 页码:21
关键词	precipitation assimilation nonparametric modeling multi-source
DOI	10.3390/rs13061057
通讯作者	Gao, Liang(gaoliang@um.edu.mo)
英文摘要	The accuracy of the rain distribution could be enhanced by assimilating the remotely sensed and gauge-based precipitation data. In this study, a new nonparametric general regression (NGR) framework was proposed to assimilate satellite- and gauge-based rainfall data over southeast China (SEC). The assimilated rainfall data in Meiyu and Typhoon seasons, in different months, as well as during rainfall events with various rainfall intensities were evaluated to assess the performance of this proposed framework. In rainy season (Meiyu and Typhoon seasons), the proposed method obtained the estimates with smaller total absolute deviations than those of the other satellite products (i.e., 3B42RT and 3B42V7). In general, the NGR framework outperformed the original satellites generally on root-mean-square error (RMSE) and mean absolute error (MAE), especially on Nash-Sutcliffe coefficient of efficiency (NSE). At monthly scale, the performance of assimilated data by NGR was better than those of satellite-based products in most months, by exhibiting larger correlation coefficients (CC) in 6 months, smaller RMSE and MAE in at least 9 months and larger NSE in 9 months, respectively. Moreover, the estimates from NGR have been proven to perform better than the two satellite-based products with respect to the simulation of the gauge observations under different rainfall scenarios (i.e., light rain, moderate rain and heavy rain).
资助项目	Science and Technology Development Fund, Macau SAR[SKL-IOTSC-2021-2023] ; Science and Technology Development Fund, Macau SAR[0030/2020/A1] ; Science and Technology Development Fund, Macau SAR[0021/2020/ASC] ; UM Research Grant[SRG2019-00193-IOTSC] ; UM Research Grant[SRG2020-00020-IOTSC] ; UM Research Grant[MYRG2020-00072-IOTSC] ; Guangdong-Hong Kong-Macau Joint Laboratory Program[2020B1212030009] ; National Natural Science Foundation of China[41730645] ; CORE[EF017/IOTSC-GL/2020/HKUST]
WOS关键词	GAUGE OBSERVATIONS ; ANALYSIS TMPA ; SATELLITE ; PERFORMANCE ; RETRIEVALS
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	MDPI
WOS记录号	WOS:000651998400001
资助机构	Science and Technology Development Fund, Macau SAR ; UM Research Grant ; Guangdong-Hong Kong-Macau Joint Laboratory Program ; National Natural Science Foundation of China ; CORE
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/162936]
专题	中国科学院地理科学与资源研究所
通讯作者	Gao, Liang
作者单位	1.Univ Macau, State Key Lab Internet Things Smart City, Macau 999078, Peoples R China 2.Univ Macau, Dept Civil & Environm Engn, Macau 999078, Peoples R China 3.Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 4.Nanning Normal Univ, Key Lab Beibu Gulf Environm Change & Resources Us, Minist Educ, Nanning 530001, Peoples R China 5.Ctr Ocean Res Hong Kong & Macau CORE, Hong Kong 999077, Peoples R China 6.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Zhou, Yuanyuan,Qin, Nianxiu,Tang, Qiuhong,et al. Assimilation of Multi-Source Precipitation Data over Southeast China Using a Nonparametric Framework[J]. REMOTE SENSING,2021,13(6):21.
APA	Zhou, Yuanyuan,Qin, Nianxiu,Tang, Qiuhong,Shi, Huabin,&Gao, Liang.(2021).Assimilation of Multi-Source Precipitation Data over Southeast China Using a Nonparametric Framework.REMOTE SENSING,13(6),21.
MLA	Zhou, Yuanyuan,et al."Assimilation of Multi-Source Precipitation Data over Southeast China Using a Nonparametric Framework".REMOTE SENSING 13.6(2021):21.