考虑降水时间相关性的地面观测-雷达-卫星遥感逐时 降水融合方法研究.

In this study, an hourly precipitation data fusion model was proposed based on the framework of XGBoost-based machine learning algorithm and geostatistical theory. The model integrated the multisource hourly precipitation data, including gauge, radar, Integrated Multi-satellite Retrievals for GPM (IMERG), Global Satellite Mapping of Precipitation (GSMaP), and gridded precipitation data. The gridded precipitation data recorded in the previous two hours were analyzed to summarize the temporal correlation characteristics of precipitation in the proposed hourly precipitation data fusion model. The 1 km IMERG and GSMaP precipitation data were downscaled by using the area-to-point Kriging method. The proposed hourly precipitation data fusion model was built using hourly precipitation data from 200 rain gauges and tested with three regional rainstorm events occurred during April 23 to 28, May 7 to 11, and September 16 to 17, 2018 in northern Guangdong. The results were compared with the results estimated from the XGBoost and Random Forest (RF) algorithms without considering the temporal correlation characteristics of precipitation. The results showed that: (1) For the three regional rainstorm events, the three models reported similar spatial distribution of hourly precipitation. (2) Compared with the XGBoost and RF models, the proposed model that integrated the temporal correlation of hourly precipitation data could significantly improve the accuracy of estimated precipitation data. (3) The XGBoost model showed better performance than the RF model in capturing the nonlinear relationship between gauge precipitation data and independent variables. [ABSTRACT FROM AUTHOR]