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

• Main Authors: Yuanyuan Zhou, Nianxiu Qin, Qiuhong Tang, Huabin Shi, Liang Gao
• Format: Article
• Language: English
• Published: MDPI AG 2021-03-01
• Series: Remote Sensing
• Subjects: precipitation; assimilation; nonparametric modeling; multi-source
• Online Access: https://www.mdpi.com/2072-4292/13/6/1057

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).