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Assimilation of multi-source precipitation data over southeast china using a nonparametric framework
Zhou, Yuanyuan1,2; Qin, Nianxiu3; Tang, Qiuhong4,5; Shi, Huabin1; Gao, Liang1,2
2021-03-02
Source PublicationRemote Sensing
ISSN2072-4292
Volume13Issue:6Pages:1057
Abstract

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).

KeywordAssimilation Multi-source Nonparametric Modeling Precipitation
DOI10.3390/rs13061057
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEnvironmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEnvironmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000651998400001
PublisherMDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
Scopus ID2-s2.0-85102995219
Fulltext Access
FWCI0.35902768
Citation statistics
Document TypeJournal article
CollectionTHE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU)
Corresponding AuthorGao, Liang
Affiliation1.State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, 999078, Macao
2.Center for Ocean Research in Hong Kong and Macau (CORE), 999077, Hong Kong
3.Key Laboratory of Beibu Gulf Environment Change and Resources Use, Ministry of Education, Nanning Normal University, Nanning, 530001, China
4.Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
5.Institute of Geographic Sciences and Natural Resources Research, University of Chinese Academy of Sciences, Beijing, 100049, China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
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):1057.
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),1057.
MLA Zhou, Yuanyuan,et al."Assimilation of multi-source precipitation data over southeast china using a nonparametric framework".Remote Sensing 13.6(2021):1057.
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