## 4 Results and Discussion
### Performance of Retrieving Real Rainfall Field
This is directly reflected in the fact that some of the data points deviate from the fitting straight line for all ranges of rain rates retrieved by the OK algorithm in Figure 9a. In addition, the results retrieved by the RBF neural network are in overall better agreement with the rain rates of the real rainfall fields than those retrieved by the OK algorithm, as can be seen in Figure 9. However, the rain rates over 50 mm/h retrieved by the RBF neural network are lower than those of the real rainfall field in Figure 9b, which shows that the RBF neural network underestimates the rain rates for retrieving extreme rainfall (over 50 mm/h). Figure 10 shows the probability density function (PDF) and cumulative distribution function (CDF) of the rain rates from all rainfall fields in the experimental area. It can be shown that extreme rainfall accounts for a small percentage of the total rainfall in the area after the statistics of all rain rates. The PDF shows that the proportion of all rain rates above 30 mm/h is below 1.0%. In addition, 99.4% of the rain rates in the sample are below 30 mm/h, as can be seen from the CDF. Therefore, the main reason for the RBF neural network to underestimate the rain rates of extreme rainfall is that the number of extreme rainfall samples in the training set is too small. However, the RBF neural network still has high accuracy for the retrieval of common rainfall in the area. To further examine the performance in monitoring actual rainfall event, we retrieved two different types of rainfall in July 2019 using the joint network of ESLs and HMLs, respectively. Figures 11 and 12 show the results of the joint network of ESLs and HMLs retrieving the two different types of rainfall events, respectively. Among them, the rainfall field 1 to rainfall field 4 is a partial process of stratiform rainfall event with a duration of two hours. Additionally, the rainfall field 5 to rainfall field 8 is a convective rainfall event with a duration of two hours. The stratiform rainfall is characterized by small rain rate, long duration and slow movement. In contrast, the convective cloud rainfall has a high peak of rain rate, develops rapidly, and the movement of the rain cluster is fast. From monitoring the results of the stratiform rainfall and convective rainfall, it can be seen that the rainfall fields retrieved by the joint network of ESLs and HMLs based on the OK algorithm and  
Figure 9: The results of the joint network of ESLs and HMLs retrieving all rain rates from real rainfall fields: (**a**) Comparison of rain rates retrieved by the joint network of ESLs and HMLs based on the OK algorithm with the real rain rates; (**b**) Comparison of rain rates retrieved by the joint net of ESLs and HMLs based on the RBF neural network with the real rain rates. RBF neural network are generally consistent with the distribution of the actual rainfall. Moreover, the joint network of ESLs and HMLs can accurately retrieve the characteristics of the stratiform rainfall and convective rainfall. However, there are still differences between the retrieved rainfall fields and the structure of the actual rainfall in a small, localized area. The RMSE, MB and CC for the two types of rainfall retrieved by the joint network of ESLs and HMLs are shown in Table 4. It can be concluded that the RMSE of retrieving stratiform rainfall and convective rainfall by the joint network of ESLs and HMLs based on the OK algorithm is lower than 0.54 mm/h and 4.04 mm/h, respectively, while the CC of retrieving stratiform rainfall and convective rainfall is higher than 0.949 and 0.971, respectively. In addition, the RMSE for retrieving stratiform rainfall and convective rainfall by the joint network of ESLs and HMLs based on the RBF neural network is lower than 0.32 mm/h and 3.44 mm/h, respectively, and the CC for retrieving stratiform rainfall and convective rainfall is higher than 0.954 and 0.997, respectively.