# Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest
## 3 Results
### Precipitation Identification Model
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Non-Precipitation: Precipitation** & **FAR** & **POD** & **CSI** & **ETS** \\ \hline
4:1 & 0.226 & 0.610 & 0.518 & 0.439 \\
3:1 & 0.300 & 0.658 & 0.535 & 0.452 \\
2:1 & 0.326 & 0.744 & 0.547 & 0.454 \\
1:1 & 0.436 & 0.856 & 0.515 & 0.400 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Validation results for different ratios during day in the precipitation identification model. 0.454. At night, the FAR score is 0.346, the POD score is 0.740, the CSI score is 0.530 and the ETS score is 0.430. The independent testing dataset is used to determine the precipitation region and is compared with the FY-4B/AGRI operational precipitation product of the same period, as shown in Table 8. The results show that the FAR of the model is higher than that of the operational product. The POD, CSI and ETS scores are all higher than those of the operational product. Overall, the model is better at identifying precipitation. From the comparison of the day and night models, the FAR score of the night model is higher, and the POD, CSI and ETS scores are not as high as those of the day model, which may be due to the relatively more input feature variables of the day model and the fact that it learns more precipitation features. A boxplot of the evaluation indicators (FAR, POD, CSI and ETS) is used to represent the hour-by-hour results of the precipitation identification model on the testing dataset, as shown in Figure 6. The red line represents the mean value, and the red dot represents the outlier. The lower end and upper end of the blue box correspond to 25% and 75% after all the values are arranged from small to large. The short horizontal line on the upper boundary of the box represents the maximum value except the outlier, and the horizontal line on the lower boundary represents the minimum value except the outlier. During the daytime period (2200 UTC\(\sim\)1000 UTC, corresponding to 0600-18:00 Beijing time), the model shows relatively high POD, CSI and ETS scores and a relatively low FAR score. The change in each index is relatively stable. During the night period (1100 UTC\(\sim\)2100 UTC, corresponding to 19:00-05:00 Beijing time), the performance of the model is relatively poor, and the change in indicators is large. Meanwhile, the transition of the indicators between day and night is relatively continuous. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **FAR** & **POD** & **CSI** & **ETS** \\ \hline Day & 0.326 & 0.744 & 0.547 & 0.454 \\ Night & 0.346 & 0.740 & 0.530 & 0.430 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Evaluation indicators of the identification model. Figure 5: Importance ranking of feature variables in precipitation identification model (**a**) during the day and (**b**) at night. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation** & \multicolumn{3}{c}{**Retrieval Model**} & \multicolumn{3}{c}{**Operational Product**} \\ \cline{2-5}
**Indicators** & **Day** & **Night** & **Day** & **Night** \\ \hline FAR & 0.385 & 0.448 & 0.319 & 0.393 \\ POD & 0.680 & 0.639 & 0.328 & 0.337 \\ CSI & 0.477 & 0.421 & 0.284 & 0.277 \\ ETS & 0.432 & 0.369 & 0.252 & 0.239 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Evaluation indicators of precipitation identification model and FY-4B/AGRI operational precipitation product. We take 0200 UTC\(-\)0230 UTC on 18 August 2022 and 1500 UTC\(-\)1530 UTC on 9 August 2022 as an example to evaluate the effect of the day and night models, respectively, as shown in Figures 7 and 8, where the blue region represents precipitation and the colourless region represents non-precipitation. Compared with the GPM IMERG product, the model is basically accurate in retrieving the location of the precipitation region, but the region is relatively large. Particularly in the western region, the night model does not identify precipitation well.