## 3 Results
### Performance of the Merged Satellite Precipitation  
In this paper, based on three kinds of calibration data and quality evaluation indicators, different methods, including the principal component analysis (PCA), EW, AHP, and fusion of AHP and EW, were used to determine the weight of the target point and promote the whole region to merge multi-source satellite calibration data. Figures 8 and 9 and Table 7 show the accuracy changes in different calibration data and multiple fusion data from 2010 to 2014. As one of the most widely used weight determination method, the EW method results are better than those of AHP and PCA; however, the performance of the EW method is worse than the subjective and objective weighting method that combines AHP and EW, which is consistent with the results in the literature [106; 107]. It can be concluded that the merged satellite precipitation data, obtained when fusing AHP with EW, provide the most accurate estimation on a diurnal scale compared to calibration data and other fused data due to their lower RMSE and BIAS values (RMSE = 2.189 mm, BIAS = 0.142 mm); furthermore, the CC value (CC = 0.790) is higher than in the calibration data and any other fused precipitation product. The results show that the data fusion technique can significantly reduce the systematic error [108]. They also prove the applicability of the subjective and objective weighting method combined with the AHP and EW method, as introduced in this study in the fusion of multi-source satellite calibration data.  
Figure 8: Scatter plots showing relationship between rain gauge daily observations and **(a)** the calibrated CMORPH, **(b)** the calibrated ERA5, **(c)** the calibrated IMERG, **(d)** the MSP data at one selected station location for the period of 2010-2014.  
PCA, EW, and AHP, as the most widely used methods for determining the weighting of data structures, have shown superior performance in different scientific fields [100; 109]; however, in this research, their performances seem to be weaker than that of the subjective and objective weighting method combining AHP and EW, which may be because individually they only consider the amount of data information or the prior knowledge of experts [110]. Although the subjective and objective weighting method has achieved the best fusion results, it needs to combine the AHP and EW methods to determine the weight. The associated time cost and calculation cost are also higher than for other weighting methods [106; 111].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **CC** & **RMSE (mm)** & **BIAS (mm)** \\ \hline IMERG\_C & 0.760 & 2.453 & 0.147 \\ CMORPH\_C & 0.733 & 2.544 & 0.211 \\ ERA5\_C & 0.672 & 2.656 & 0.103 \\ EW & 0.786 & 2.203 & 0.155 \\ AHP & 0.780 & 2.197 & 0.148 \\ PCA & 0.785 & 2.202 & 0.150 \\ MSP & 0.790 & 2.189 & 0.142 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluation statistics of three calibration precipitation data and combined precipitation data with different methods from 2010 to 2014.  
Figure 9: Scatter plots showing relationship between rain gauge daily observations and **(a)** PCA fused data, (b) EW fused data, (c) AHP fused data, and (d) MSP data at one selected station location for the period of 2010–2014.