# Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest
## 5 Conclusions  
In the present study, a new precipitation retrieval model is proposed during the day and at night using FY-4B/AGRI Level1 satellite data, based on a random forest classification and regression model. The use of the proposed method is evaluated for China for the time period from July to August 2022. The GPM IMERG product is used as a reference to evaluate the retrieval effect of the model. Our key study findings are summarized as follows:  
1. Compared with the FY-4B/AGRI operational precipitation product, the retrieval model is better able to identify precipitation and better able to capture precipitation areas of light rain, moderate rain, heavy rain and torrential rain. During the day, the POD score increased from 0.328 to 0.680, the CSI score increased from 0.284 to 0.477 and the ETS score increased from 0.252 to 0.432. At night, the POD score increased from 0.337 to 0.639, the CSI score increased from 0.277 to 0.421 and the ETS score increased from 0.239 to 0.369. 2. The precipitation estimation accuracy of the retrieval model is higher than that of the FY-4B/AGRI operational precipitation product, in which the accuracy of the day model increased by 38.98% and that of the night model by 40.85%. Moreover, the retrieval error of the model increases with the increase in precipitation level. For light rain, both the model and operational product overestimate the amount of precipitation. For moderate rain, the day model underestimates the amount of precipitation, while the day product, the night model and the night product overestimate the amount of precipitation. And it is worth mentioning that the BIAS score of moderate rain is quite small. For heavy rain and torrential rain, both the model and product underestimate the amount of precipitation on the whole, and the degree of underestimation increases with the increase in precipitation level. 3. In our comparative analysis of different underlying surfaces, due to the surface uniformity of the ocean, the model can identify precipitation better on the ocean than on the land. For different underlying surfaces of the land, there is no significant difference in each evaluation index of the model, indicating the universal applicability of the model. Particularly for more vegetated areas and areas covered by water, the model is able to accurately estimate precipitation. In conclusion, the precipitation retrieval model established in this study can better determine precipitation regions and estimate precipitation intensity compared with the FY-4B/AGRI operational precipitation product. It can provide some reference value for users carrying out future precipitation retrieval research on FY-4B/AGRI and those who need information on dynamic changes in precipitation with a high spatial and temporal resolution. It should be pointed out this study only trained and verified the precipitation data in the summer, and the model may not perform well in other seasons of the year. In the future, we will consider constructing a precipitation retrieval model with a longer applicable period. Our results also showed that different underlying surfaces do have an impact on the retrieval of precipitation data. We can consider to include these factors as variables in the model. In addition, the dataset is split in two subsets, day and night, according to the solar zenith angle. Thus, the day model and the night model were established separately. However, it should be noted that since China spans over four to five timezones, it is very likely that the night samples are affected by sunlight, and vice versa. We will divide the time period into day, dusk and night or consider a unified model for the entire day in later works. In this study, a resampling method is adopted to change the distribution of samples so as to improve the ability of the precipitation retrieval model to identify a few types of samples. This method sacrifices the accuracy of the majority of class samples.