A comprehensive comparison of data fusion approaches to multi-source precipitation observations: a case study in Sichuan province, China

With the complex landform and climate in the Sichuan region, the need for practical and scientific research production by only utilising the rainfall data derived from ground stations or satellites has not been satisfied. To overcome this difficulty, rainfall data from 161 meteorological stations in 2016 are used in this study. According to the distribution of stations, 146 rainfall data from 161 meteorological stations in 2016 are used for inverse distance weighted interpolation, and then, linear regression, weighted regression, and Kalman filter fusion and optimal interpolation method data fusion are performed with TRMM 3B42 satellite rainfall data, respectively. Then, 15 meteorological stations evenly distributed in the study area are selected for the accuracy test. The results show that compared with the measurement at ground stations, linear regression shows the best merging effect on rainfall data derived from ground stations and satellite rainfall estimates across the daily scale: the correlation coefficient is the most significantly improved (0.2–0.7) and the reduction in root-mean-square error (RMSE) is the largest. The method is applicable for use in Sichuan Province when merging rainfall data. At the monthly scale, the rainfall data processed by using the Kalman filter present the highest accuracy (0.72–0.84). At this scale, the Kalman filter is more suitable.