## 4 Results and Discussion
### Performance of Retrieving Real Rainfall Field
In addition, it is revealed from the MB of the retrieval results that both the OK algorithm and the RBF neural network are essentially unbiased for the retrieval of stratiform rainfall. However, for the retrieval of convective rainfall, the OK algorithm significantly overestimates rainfall field 7, and the RBF neural network obviously underestimates rainfall field 6 and rainfall field 7, which is mainly due to the factor that RBF will underestimate extreme rainfall. Overall, the errors and unbiasedness of retrieving stratiform rainfall for the OK algorithm and RBF neural network are better than those of retrieving convective cloud rainfall, but the consistency of retrieving convective rainfall is better than the results of retrieving stratiform rainfall. Moreover, the retrieval results of the RBF neural network for both types of rainfall are generally superior to those of the OK algorithm. Figure 10: Statistics on the distribution of rain rates from all rainfall fields in the experimental area. The results of the retrieval of real rainfall fields show that the joint network of ESLs and HMLs can accurately retrieve the rain rates of real rainfall fields and can effectively monitor stratiform rainfall and convective rainfall. Thereby, the feasibility of the joint network of ESLs and HMLs to retrieve rainfall fields in the area is validated. Additionally, it also shows the significant potential of combined ESLs and HMLs for monitoring rainfall over large areas. Figure 11: The results of retrieving stratiform rainfall event that occurred on 3 July 2019 by combining ESLs and HMLs. Rainfall field 1 to rainfall field 4 in (**a–d**) show the development of the stratiform rainfall at 0.5 h intervals, characterized by small rain rate, long duration and slow movement. (**e–h**) are the results of retrieving the stratiform rainfall from rainfall field 1 to rainfall field 4 by the OK algorithm, and (**i–l**) are the results of retrieving the stratiform rainfall from rainfall field 1 to rainfall field 4 by the RBF neural network. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Rainfall Type**} & \multirow{2}{*}{**Rainfall Fields**} & \multicolumn{2}{c}{**RMSE (mm/h)**} & \multicolumn{2}{c}{**MB (mm/h)**} & \multicolumn{2}{c}{**CC**} \\  & & **OK** & **RBF** & **OK** & **RBF** & **OK** & **RBF** \\ \hline \multirow{4}{*}{Stratiform rainfall} & Rainfall field 1 & 0.52 & 0.32 & \(-\)0.08 & \(-\)0.01 & 0.988 & 0.999 \\  & Rainfall field 2 & 0.54 & 0.26 & \(-\)0.03 & \(-\)0.02 & 0.980 & 0.998 \\  & Rainfall field 3 & 0.42 & 0.23 & \(-\)0.01 & \(-\)0.04 & 0.964 & 0.990 \\  & Rainfall field 4 & 0.31 & 0.30 & \(-\)0.05 & \(-\)0.06 & 0.949 & 0.954 \\ \hline \multirow{4}{*}{Convective rainfall} & Rainfall field 5 & 1.05 & 0.58 & \(-\)0.20 & 0.09 & 0.979 & 0.997 \\  & Rainfall field 6 & 2.56 & 1.60 & 0.22 & \(-\)1.00 & 0.971 & 0.997 \\ \cline{1-1}  & Rainfall field 7 & 4.04 & 3.44 & 1.27 & \(-\)1.94 & 0.988 & 0.998 \\ \cline{1-1}  & Rainfall field 8 & 1.75 & 1.73 & 0.47 & 0.62 & 0.992 & 0.998 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The results of retrieving two different types of rainfall events by the joint network of ESLs and HMLs. Figure 12: The results of retrieving convective rainfall event that occurred on 22 July 2019 by combining ESLs and HMLs. Rainfall field 5 to rainfall field 8 in (**a–d**) show the development of the convective rainfall with a high peak of rain rate, rapid development and fast movement of the rain cluster. (**e–h**) are the results of retrieving the convective rainfall from rainfall field 5 to rainfall field 8 by the OK algorithm, and (**i–l**) are the results of retrieving the convective rainfall from rainfall field 5 to rainfall field 8 by the RBF neural network.