# Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest
## 4 Discussion  
This study focuses on establishing an FY-4B/AGRI precipitation identification and estimation model during the day and at night based on an RF. To further improve the accuracy of satellite precipitation retrieval, especially for heavy rain and torrential rain, we made the following improvements during the training of the model. Firstly, we selected 18 feature variables in the day model and 16 feature variables in the night model related to precipitation as the input of the model. These variables can better reflect the characteristics of clouds and precipitation. Among them, in the precipitation identification model, CTH is the most important, indicating that the cloud region with a large cloud top height and a low cloud top temperature have a large amount of cloud water and a higher probability and intensity of rainfall. In the precipitation estimation model, the contribution of WV to model training is important, especially in the night model, indicating that abundant water vapor conditions have an important effect on precipitation. Meanwhile, in the summer, due to the strong influence of the East Asian summer monsoon, the prevailing southeast wind brings abundant water vapor. Under a high temperature, the water vapor condenses with the uplift of terrain, which is the main mechanism for generating precipitation. Therefore, the impact of DEM on precipitation is also significant. At the same time, SAZ, the satellite observation angle variable, is also of high importance. For some features that contribute relatively little, their relatively small contribution may come from overlapping with other information. In the decision tree algorithm, a feature's contribution to an overlapping part will be assigned to the most distinguishable feature variable first, while the contribution of other features will not be recorded, although they may have partially overlapping information. Thus, this results in the underestimation of some features' contributions. Secondly, we tested the effect of different proportions of sample size on the accuracy of the model training and selected the optimal sample proportion. In the precipitation identification model, the method of random subsampling is used to reduce the samples of non-precipitation. The proportion of non-precipitation to precipitation samples is set as 4:1, 3:1, 2:1 and 1:1 for training. In the precipitation estimation model, the upsampling method is used to greatly increase the number of samples of moderate rain, heavy rain and torrential rain. The proportion of light rain, moderate rain, heavy rain and torrential rain is set as 75:20:3:1 and 1:1:1:1, respectively, for training. The results show differences in sample size do have an impact on the accuracy of the model. After a comprehensive comparison of all the validation results, a 2:1 ratio of non-precipitation to precipitation and a 1:1:1:1 ratio of light rain, moderate rain, heavy rain and torrential rain are selected as the optimal sample proportion, respectively. Such settings enhance the ability of the model to learn minority samples and reduce the retrieval error. Figure 21: Evaluation indicators of precipitation estimation model and FY-4B/AGRI operational precipitation product based on the ground rain gauge data and GPM IMERG product (**a**) during the day and (**b**) at night. Thirdly, we evaluated the retrieval effect of different underlying surfaces. The ocean has a better ability to identify precipitation than the land. Its better performance may be due to the fact that compared with the land, the ocean's surface is more uniform. And there are more heavy rainfall events over the ocean, so these unpredictable heavy rainfall events also inevitably increase the frequency of large errors over the ocean [20]. In different underlying surfaces of the land, water bodies perform better in the day model. The evaporation of river water increases the amount of water vapor in the atmosphere and increases cloud cover and precipitation, so the model is better able to identify precipitation.