# Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest
## 3 Results
### Precipitation Estimation Model  
The model is also realized through three steps. This section analyses the optimal sample proportion and parameters for model tuning as well as summarizes the precipitation estimation results in the validation dataset and testing dataset, respectively. Figure 11 shows the sample's distribution for different ratios during the day and at night after random upsampling. Tables 9 and 10 show the validation results for different ratios of day and night, respectively. The results show that when the sample size for moderate rain, heavy rain and heavy rain is increased, and the sample proportion of light rain, moderate rain, heavy rain and torrential rain is set to 1:1:1:1, the BIAS and RMSE values of moderate rain, heavy rain and heavy rain are reduced, and the model effect is better. Therefore, 1:1:1:1 is selected as the optimal sample proportion. Figure 12 shows the change in the MSE score under different numbers of parameters in the model during the day and at night. Before the number of trees is 200, the MSE value has a significant decreasing trend, and after 200, the MSE value no longer changes significantly. Therefore, 500 trees are selected as the optimal number of trees in the precipitation estimation model. When the number of features is 11 during both the day and at night, the MSE value is the smallest. Therefore, 11 features are selected as the number of optimal features in the precipitation estimation model. The importance of each feature of the model can also be output, as shown in Figure 13. The infrared bright temperature and infrared bright temperature difference, which represent cloud microphysical parameters such as CTH, CWP, CTT and CP, still make a large contribution. Meanwhile, in the precipitation estimation model, the contribution of WV to model training is also large, especially in the night model. DEM and SAZ are also important for precipitation estimation. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample Ratio**} & \multicolumn{2}{c}{**Light Rain**} & \multicolumn{2}{c}{**Moderate Rain**} & \multicolumn{2}{c}{**Heavy Rain**} & \multicolumn{2}{c}{**Torrential Rain**} \\ \cline{2-9}  & **BIAS** & **RMSE** & **BIAS** & **RMSE** & **BIAS** & **RMSE** & **BIAS** & **RMSE** \\ \hline
75:20:3:1 & 0.722 & 1.179 & \(-0.541\) & 2.175 & \(-4.985\) & 6.212 & \(-15.003\) & 17.871 \\
1:1:1:1 & 0.910 & 1.374 & \(-0.237\) & 2.131 & \(-4.767\) & 5.986 & \(-14.481\) & 17.334 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Validation results for different ratios at night in the precipitation estimation model. Figure 11: Sample numbers for different ratios during (**a**) day and (**b**) night. Figure 12: MSE for different numbers of (**a**) trees and (**b**) features. The trained model is applied to the validation dataset, as shown in Table 11. The precipitation retrieval by the day model is consistent with the GPM IMERG product, and the precipitation intensity error is small. At night, the R is 0.604, BIAS is 0.332 mm/h and RMSE is 2.558 mm/h. A scatterplot of the precipitation intensity is shown in Figure 14, where red indicates a high density of data. In the light rain region, the model shows a significant overestimation, while in the heavy rain and torrential rain regions, the model shows an underestimation. The independent testing dataset is used to estimate the precipitation intensity in precipitation areas and is compared with the FY-4B/AGRI operational precipitation product for the same period, as shown in Table 12. The BIAS between the model and the product is positive, indicating that the amount of precipitation is overestimated by the model and the operational product as a whole. The RMSE of the model is lower than that of the operational product both during the day and at night, indicating that the retrieval accuracy of the model is higher than that of the operational product. The error of the day model is