## 4 Results and Discussion
### Performance of Retrieving Real Rainfall Field  
The joint network of ESLs and HMLs is able to accurately retrieve the distribution of rain rates in rain cells over the area. However, the spatial distribution of real rainfall is more complex and irregular than the structure of simulated rain cells. Therefore, it is necessary to validate the performance of retrieving real rainfall fields using the joint network of ESLs and HMLs by real rainfall fields that match the characteristics of actual rainfall. In this paper, we constructed the real rainfall fields in the experimental area by processing the rainfall products from the Climate Prediction Center Morphing Technique of NOAA (CMORPH) through spatial interpolation, so as to further investigate the performance of retrieving real rainfall fields by using the joint network of ESLs and HMLs. Rainfall fields with a time interval of 0.5 h can be obtained through the CMORPH rainfall product, which means the time resolution of the real rainfall fields in this research is 0.5 h [51]. We chose a total of 1000 rainfall fields for the period from 2017 to 2019, each of which consisted of 1225 rainfall rates. To verify the performance of retrieving the real rainfall field using the joint network of ESLs and HMLs, the real rainfall field from CMORPH is firstly used as the initial field, and then, the sparse distributed rain rates at the location of the links in the area can be measured by the joint network of ESLs and HMLs. Eventually, the OK algorithm and the RBF neural network are used to reconstruct the sparse rain rates, respectively, so as to obtain the rainfall field of the whole area. For the RBF neural network, we used 70% of the real rainfall fields as the training set, thus determining the parameters of the computational model for the RBF neural network to retrieve the real rainfall field. The remaining rainfall fields would be used as the initial fields to test the retrieval performance of the joint network of ESLs and HMLs. The results of the joint network of ESLs and HMLs retrieving all rain rates from 300 real rainfall fields are shown in Figure 9. From the validation in Figure 9a, it can be seen that the RMSE and CC between the results retrieved from the network of ESLs and HMLs based on the OK algorithm and the rain rates of real rainfall fields are approximately 0.56 mm/h and 0.996, respectively, and the average rain rate retrieved is very close to that of real rainfall fields. In addition, from the validation in Figure 9b it, can be shown that the RMSE and CC between the results retrieved from the network of ESLs and HMLs based on RBF neural networks and the rain rates of the real rainfall fields are about 0.51 mm/h and 0.997, respectively, and the average rain rate retrieved is significantly lower than that of the real rainfall fields, which is more obvious during heavy rainfall. The retrieval results of the OK algorithm and the RBF neural network are generally unbiased, as evidenced by the MB, \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Rain Cells**} & \multicolumn{2}{c}{**RMSE (mm/h)**} & \multicolumn{2}{c}{**MB (mm/h)**} & \multicolumn{2}{c}{**CC**} \\  & **OK** & **RBF** & **OK** & **RBF** & **OK** & **RBF** \\ \hline Rain cell 1 & 0.52 & 0.69 & \(-\)0.14 & \(-\)0.12 & 0.995 & 0.999 \\ Rain cell 2 & 0.27 & 0.28 & 0.03 & \(-\)0.20 & 0.986 & 0.994 \\ Rain cell 3 & 0.75 & 0.54 & 0.02 & 0.29 & 0.985 & 0.998 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of the joint network of ESLs and HMLs retrieving the different rain cells. but the RMSE and CC of the RBF neural network for retrieving the rain rates of rainfall fields are better than the retrieval results of the OK algorithm. However, the performance of the RBF neural network in retrieving the average rain rate of the real rainfall fields is not as good as that of the OK algorithm. In general, the results of the OK algorithm retrieval have a few results with large errors across the range of rain rates.