# Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest
## 2 Materials and Methods
### Methods
#### 2.3.3 Selection of Feature Variables  
For the identification and estimation of precipitation, it is necessary to select feature variables related to precipitation as the input of the model. Considering the tropical deep convective precipitation process and mid-latitude stratospheric cloud precipitation process, physical variables related to precipitation process are selected, mainly including cloud top height (CTH), cloud top temperature (CTT), cloud water path (CWP), cloud phase (CP) and water vapor (WV). CTH reflects the level of cloud development [35], and clouds with significant vertical development contain a large amount of precipitation. CTT is widely used to estimate precipitation in convective systems [36], and a colder cloud top temperature corresponds to a higher precipitation rate. CWP reflects the size of water or ice particles and the optical thickness of the cloud [37]. A cloud region with higher cloud water path, that is, the cloud region with high optical thickness and large effective particle radius, has a large amount of cloud water and a higher probability and intensity of precipitation. CP reflects whether the upper part of the cloud is ice cloud or water cloud [37]. The rainfall process is related to the ice particles in the upper part of the cloud, and the cloud phase information can be used to distinguish the clear sky. WV can be used to represent different sensitivity characteristics to cloud water vapor. The optical and microphysical properties of clouds can be determined by satellite observations. Visible channels can be used to observe clouds during day, with large differences in albedo values observed between clouds and clear sky. Near-infrared channels can be used to distinguish between snow and clouds; the albedo of snow is significantly higher than the albedo of lower clouds composed of water droplets. The radiation characteristics of the infrared channels are more sensitive to the size and distribution of water vapor condensation. An increase in particle radius leads to an increase in transmittance and cloud emissivity and a decrease in reflectivity. Therefore, the optical and microphysical properties of clouds can be deduced by appropriate satellite spectral channels and their combination, so as to reflect the radiation signal characteristics of the precipitation clouds. The 10.8 \(\upmu\)m infrared brightness temperature can be used to obtain information about CTT and CTH, especially for convective clouds [35; 36]. Since the 10.8 \(\upmu\)m band is located in the atmospheric window region and is relatively transparent, the influence of water vapor above the cloud top on the radiation in the window region is negligible, so the brightness temperature of the 10.8 \(\upmu\)m channel in the window region is usually regarded as the CTT [38]. The generation of precipitation is the result of the continuous development of clouds. In general, the development of clouds at a single station shows that the brightness temperature of the cloud top is constantly decreasing. The attenuation of clouds at a single station shows that the brightness temperature of the cloud top is rising. Therefore, the change in the brightness temperature of the cloud top can be used as an indicator of the growth factor of the clouds and can also reflect the change in its precipitation intensity [22]. Research shows that the mean and variance of adjacent samples are usually used to represent the environment around pixel points [39]; thus, the brightness temperature variance is calculated as follows: a satellite image is divided into 5 \(\times\) 5 panes, and the average brightness temperature of all pixels at 10.8 \(\upmu\)m is calculated in each pane. Then, the variance between each pixel and the average brightness temperature are calculated. Then, the cloud top brightness temperature gradient of each pixel is calculated to represent the cloud top brightness temperature change.