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Article

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

1
State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, Macao 999078, China
2
Center for Ocean Research in Hong Kong and Macau (CORE), Hong Kong 999077, China
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
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(6), 1057; https://doi.org/10.3390/rs13061057
Submission received: 10 January 2021 / Revised: 24 February 2021 / Accepted: 2 March 2021 / Published: 11 March 2021
(This article belongs to the Special Issue Remote Sensing in Hydrology and Water Resources Management)

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).
Keywords: precipitation; assimilation; nonparametric modeling; multi-source precipitation; assimilation; nonparametric modeling; multi-source

Share and Cite

MDPI and ACS Style

Zhou, Y.; Qin, N.; Tang, Q.; Shi, H.; Gao, L. Assimilation of Multi-Source Precipitation Data over Southeast China Using a Nonparametric Framework. Remote Sens. 2021, 13, 1057. https://doi.org/10.3390/rs13061057

AMA Style

Zhou Y, Qin N, Tang Q, Shi H, Gao L. Assimilation of Multi-Source Precipitation Data over Southeast China Using a Nonparametric Framework. Remote Sensing. 2021; 13(6):1057. https://doi.org/10.3390/rs13061057

Chicago/Turabian Style

Zhou, Yuanyuan, Nianxiu Qin, Qiuhong Tang, Huabin Shi, and Liang Gao. 2021. "Assimilation of Multi-Source Precipitation Data over Southeast China Using a Nonparametric Framework" Remote Sensing 13, no. 6: 1057. https://doi.org/10.3390/rs13061057

APA Style

Zhou, Y., Qin, N., Tang, Q., Shi, H., & Gao, L. (2021). Assimilation of Multi-Source Precipitation Data over Southeast China Using a Nonparametric Framework. Remote Sensing, 13(6), 1057. https://doi.org/10.3390/rs13061057

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