# Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest
## 3 Results  
### Precipitation Identification Model  
The model is realized through the following three steps: model tuning; model training and validation; and model testing. This section analyses the optimal sample proportion and parameters of the model tuning and summarizes the precipitation identification results in the validation dataset and testing dataset, respectively. Figure 3 shows the sample distribution of different ratios during the day and at night after random subsampling. Tables 5 and 6 show the validation results for different sample ratios during the day and at night, respectively. The results show that as the proportion of non-precipitation and precipitation samples in the training set decreases, the FAR and POD values increase continuously, while the CSI and ETS values increase at first and then significantly decrease when the ratio is one to one. Although the validation results with a one-to-one sample ratio have the highest POD value, the FAR value is the highest and the ETS value is the lowest, which means that the false alarm ratio is very large and the model has a high misjudgment rate in predicting precipitation. Therefore, after a comprehensive comparison of all the validation results, a two-to-one ratio of non-precipitation to precipitation is selected as the optimal sample proportion. Figure 3: Sample number of different ratios (**a**) during the day and (**b**) at night. Figure 4 shows the change in ROC-AUC score for different numbers of parameters in the model during the day and at night. Before the number of trees is 200, the score follows a significant increasing trend. After the number of trees is 200, the score no longer significantly changes, and the more trees there are, the more computer memory and time will be required. Therefore, 500 trees are selected as the optimal number of trees in the precipitation identification model. At the same time, when the number of features is 11 during the day and 12 at night, the score is the largest. Therefore, 11 and 12 features are selected as the number of optimal features in the precipitation identification model during the day and at night, respectively. The RF classifier will give each input feature a specific weight so that the importance score of each feature can be calculated from the sum of the reduced Gini coefficients of all nodes split on that feature in all of the decision trees. Thus, we can use the 'feature_importances_' to output the importance of each feature. As shown in Figure 5, the infrared bright temperature and infrared bright temperature difference, which represent cloud microphysical parameters such as CTH, CP, CWP and CTT, have made great contributions. Among them, CTH is the most important. At the same time, DEM, the topography variable, and SAZ, the satellite observation angle variable, are also of high importance. The trained model is applied to the validation dataset. As shown in Table 7, the precipitation prediction effect during the day is better. The FAR score is 0.326, that is, for 32.6% of the cases in which precipitation is predicted, the IMERG does not observe precipitation. The POD score is 0.744, that is, the probability of the model correctly identifying precipitation pixels is 74.4% when precipitation is observed. The CSI score is 0.547 and the ETS score is \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Non-Precipitation: Precipitation** & **FAR** & **POD** & **CSI** & **ETS** \\ \hline
4:1 & 0.248 & 0.593 & 0.496 & 0.413 \\
3:1 & 0.277 & 0.639 & 0.513 & 0.425 \\
2:1 & 0.346 & 0.740 & 0.530 & 0.430 \\
1:1 & 0.453 & 0.859 & 0.502 & 0.379 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Validation results for different ratios at night in the precipitation identification model. Figure 4: ROC-AUC score for different numbers of (**a**) trees and (**b**) features.