## 4 Statistical Prediction Model
### Relative Importance Predictors  
Random forests classification algorithms can assess the importance of each predictor [39]. In theory, the importance scores (IS) represent the weighting coefficients of every predictor for fitting a RF prediction model. It can be used to evaluate a quantitative contribution of every predictor for the fitting model, which is used to improve RF model training and selection of predictors. Table 6 shows the ranking results of the IS of 83 predictors for training the optimal RF prediction model using the independent dataset of Scenario-1 with n_estimators = 100, max_depth = 5, and max_features = 10 (Scenario-1). \"max\", and \"min\" represent the 10% of maximum and the minimum pixels, respectively, in the tracked convective storm cloud cluster. Also \"mean\" represents the averaged value of all the pixels in the tracked convective storm cloud cluster. From this table, we find that most of the top ranking factors are satellite observation variables, such as T\({}_{6,2}\), T\({}_{6.9\text{-}10.4}\) and T\({}_{9.6}\). It is worth noting that the water vapor bands (6.2 \(\upmu\)m, 6.9 \(\upmu\)m and 7.3 \(\upmu\)m) with a relatively \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Measured Value**} \\ \cline{2-4}  & 1 & 0 \\ \hline \multirow{2}{*}{Expected value} & 1 & A & C \\  & 0 & B & D \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Contingency table. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **POD** & **FAR** & **CSI** & **HR** \\ \hline \multirow{2}{*}{Scenario-1} & Severe & 0.66 & 0.71 & 0.25 & 0.79 \\ \cline{2-6}  & Medium & 0.70 & 0.91 & 0.39 & \\ \hline \multirow{2}{*}{Scenario-2} & Severe & 0.34 & 0.20 & 0.31 & \\ \cline{2-6}  & Medium & 0.90 & 0.88 & 0.43 & 0.82 \\ \hline \multirow{2}{*}{Scenario-3} & Severe & 0.32 & 0.17 & 0.30 & \\ \cline{2-6}  & Medium & 0.79 & 0.83 & 0.40 & 0.90 \\ \hline \multirow{2}{*}{Scenario-0} & Severe & 0.30 & 0.18 & 0.28 & 0.97 \\ \cline{2-6}  & Medium & 0.11 & 0.47 & 0.10 & \\ \hline \multirow{2}{*}{Scenario-5} & Severe & 0.69 & 0.69 & 0.27 & 0.79 \\ \cline{2-6}  & Medium & 0.62 & 0.92 & 0.08 & \\ \hline \end{tabular} Note: Scenario-1 (n_estimators = 100, max_depth = 5, and max_features = 10); Scenario-2 (n_estimators = 50, max_depth = 15, and max_features = 10); Scenario-3 (n_estimators = 50, max_depth = 10, and max_features = 8); Scenario-0 (n_estimators = 200, max_depth = 10, and max_features = 8); and Scenario-S (n_estimators = 100, max_depth = 5, and max_features = 10)  
\end{table} 
Table 5: On the best performance metrics of convection classification using four independent RF classification models based on the four different scenarios of Scenario-1/2/3/0 and Scenario-S.  
high rank are closely associated with convectively dominated precipitation areas [24; 28], which always exhibit a large cloud depth and a higher cloud top at the troposphere. This high correlation is also due to the convective storm samples tracked in this study, which are finally marked and determined using GPM IMERG rain rate product introduced in Section 3. However, we also find some important variables with high ranks from real-time NWP data in Table 6, such as CIN, \(\theta\), MR\({}_{925}\) and TPW, indicating a strong connection between atmospheric stability, air moisture content and the occurrence of sudden convective storm [28]. When compared with high-ranking variables, we still find some low ranking variables (means low weight) from real-time NWP data in Table 6, such as EBS and CAPE index. This implies a weak connection between the sudden convective storm and the spatiotemporally matched characteristics of EBS and CAPE index. From the results of Scenario-S at the last line in Table 5, it is found that the POD and FAR of severe convective storms of Scenario-S are slightly improved but the POD and CSI (weaken from 0.39 to 0.08) of medium convective storms are significantly decreased.