# A Random Forest-Based Precipitation Detection Algorithm for FY-3C/3D MWTS2 over Oceanic Regions
## 3 Data Preprocessing and Model Training
### Model Training
#### 3.2.2 Sensitivity Analysis of the Cloud Parameters  
This paper employs the Gini index, which is inherently provided by the scikit-learn version 1.2 software package, to assess the importance of various input variables to the \(RF\_SI\) model. The relationship between the Gini index and the variable importance levels  
Figure 7: Scatter plot of the \(SI\) and \(RF\_SI\) values in the testing dataset.  
is as follows. In the context of a random forest, the importance of each input variable is determined by the decrease in the Gini index that results from splitting on that variable. The more a variable reduces the Gini index when used to split a node, the more important it is considered to be for the model.  
The sensitivity analysis results obtained for the VISSR inversion products are shown in Figure 8. The figure shows that _RF_SI_ is sensitive to all the input parameters, with the sensitivity values varying from 0.17 to 0.005. Notably, cumulonimbus clouds with strong vertical velocities induce hydrometer sizes that are large enough to trigger microwave scattering at 50-60 GHz. Therefore, _RF_SI_ is most sensitive to cumulonimbus cloud cover data, with a value of 0.17. The cloud region humidity values at 500 hPa and 1000 hPa reflect the condensable water vapor content in the vertical atmospheric column. In particular, the humidity at the 500 hPa level represents the water vapor content that can sustain the phase change exhibited by water under a satisfactory vertical velocity and temperature within the clouds. However, the humidity at 1000 hPa represents the total water vapor content that can be elevated and condensed to multiphase hydrometeors in the upper part of the clouds. The sensitivities of _RF_SI_ to these indicators are 0.15 and 0.11, respectively. Similar mechanisms apply to the humidity values observed for the 400 hPa and 700 hPa cloud regions. The humidity values of the cloud region at 850 hPa and 925 hPa exhibit mechanisms that are analogous to those presented at 1000 hPa. At approximately 300 hPa, the altitude in the divergence zone of clouds and water vapor already undergoes phase changes, and the cloud region humidity indicator is minor, with a value of only approximately 0.03.  
The CTT correlates well with the height of the cloud top; when the height is high and the humidity is sufficient, the chance of scattering increases, and clouds can be classified as singular convective clouds or thick cumulus clouds, resulting in a sensitivity of 0.12 for the CTT. Cirrus clouds (Ci) also have a high degree of sensitivity to _RF_SI_ (0.07) because they are good indicators of strong convective cloud anvils and strong convections. However, the \"other clouds\" class refers to all occurrences of clouds as long as the necessary condition stating that \"precipitation occurs in cloud-covered areas\" is satisfied. The sensitivity of _RF_SI_ for this scenario is 0.017. Additionally, precipitation is predominantly influenced by convection, and most convection occurs in the lower troposphere. Thus, the sensitivity  
Figure 8: Gini importance of the VISSR retrieval products within the training dataset. Cb represents cumulonimbus clouds, HPF300-HPF1000 represents the relative humidity at 300–1000 hPa, CTT represents the cloud-top temperature, Ci dens represents dense cirrus clouds, UTH represents the relative humidity in the middle and upper levels of troposphere, “Other Clouds” represents other types of clouds, and “Clear Ocean” represents a clear-sky surface.  
of the UTH is relatively low (0.04). The \"clear ocean\" feature has a minimal impact on precipitation, with the lowest importance value of 0.005.  
In conclusion, when evaluating the sensitivity of the input features to the microwave spectrum scattering exhibited by hydrometeor particles, the sensitivity rankings obtained from the data-driven machine learning algorithm are highly consistent with those derived from conventional meteorological processes.