# Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest
## 3 Results
### Precipitation Estimation Model
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **R** & **BIAS** & **RMSE** \\ \hline Day & 0.631 & 0.308 & 2.495 \\ Night & 0.604 & 0.332 & 2.558 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Evaluation indicators of the precipitation estimation model. Figure 14: Density scatterplot of precipitation intensity with GPM IMERG and RF predictions (**a**) during day and (**b**) at night. Figure 13: Importance ranking of feature variables in precipitation estimation model (**a**) during day and (**b**) at night. smaller than that of the night model, indicating that the day model has a better retrieval effect on the precipitation intensity. In general, it is noted that the accuracy of the day model is improved by 38.98% compared with that of the operational product. Similarly, the night model's accuracy is improved by 40.85%. The formula for the improvement in accuracy is as follows:  
\[\Big{(}RMSE_{product}-RMSE_{model}\Big{)}/RMSE_{product} \tag{10}\]  
The BIAS and RMSE values of different precipitation levels are also evaluated, as shown in Figure 15. The results show that the BIAS value of both the model and operational product is positive for light rain, indicating that both the model and operational product overestimate the amount of precipitation. At the moderate rain level, the BIAS of the day model is negative, indicating that the day model underestimates the amount of rainfall, while the BIAS of the day product as well as the night model and product are positive, indicating that the amount of precipitation is overestimated. And it is worth mentioning that the BIAS of moderate rain is quite small. For heavy rain and torrential rain, both the model and operational product underestimate the amount of precipitation on the whole, and the degree of underestimation increases with the increase in precipitation level. At the same time, the RMSE increases with the increase in precipitation level. The RMSE of the day model is smaller than that of the night model, and the RMSE of the model is lower than that of the operational product. For torrential rain, the RMSE of the day model is smaller than that of the day operational product, but the RMSE of the night model is larger, indicating that the accuracy of the model is relatively low when retrieving precipitation at night. Figure 16 shows the hour-by-hour results (R, BIAS snd RMSE) of the precipitation estimation model for the testing dataset. During the daytime (2200 UTC-1000 UTC), the change in each index is relatively stable. During the night period (1100 UTC-2100 UTC), the performance of the model is relatively poor, and the variation in indicators is large. In \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Evaluation**} & \multicolumn{2}{c}{**Retrieval Model**} & \multicolumn{2}{c}{**Operational Product**} \\ \cline{2-5}  & **Day** & **Night** & **Day** & **Night** \\ \hline R & 0.441 & 0.421 & 0.254 & 0.311 \\ BIAS & 0.744 & 1.029 & 0.598 & 1.517 \\ RMSE & 2.832 & 3.127 & 4.641 & 5.291 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Evaluation indicators of precipitation estimation model and FY-4B/AGRI operational precipitation product. Figure 15: (**a**) BIAS and (**b**) RMSE between retrieval model and operational product at different precipitation levels. particular, in the period of 1100 UTC-1500 UTC, the R value is relatively decreased, and the BIAS and RMSE values are relatively increased. Additionally, the transition of indicators between day and night is relatively continuous, and there are no large fluctuations. 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 17 and 18, where the colour code represents the precipitation intensity. Compared with the GPM IMERG product, the model is able to better capture the precipitation regions  
Figure 16: Hour-by-hour results of the precipitation estimation model for the testing dataset (**a**) R, (**b**) BIAS and (**c**) RMSE.