## 4 Statistical Prediction Model  
### RF Classification Model Training  
Random Forests as an important ensemble and advanced ML algorithm is widely used in data classification and nonparametric regression [39; 40]. Here, it is used to build a connection between convective storm system and satellite observations, which can predict the occurrence and intensity of convection. For a detailed introduction to the RF algorithm, please refer to the Appendix A at the end of this paper. For validating the performance of the RF algorithm based SWIPE model, the data on the 2 nd and 15 th days of each month are used as independent samples (mentioned in Section 3.3 above) for testing and evaluating the SWIPE model. The test data sets include 47 severe convective systems, 150 medium convective systems and 5498 weak convective systems. These independent data are not included in the training, and the remaining data from April to October of 2016 are used as a training dataset to generating an effective RF classification model -- SWIPE. Based on the tracked \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Month** & **Severe** & **Medium** & **Slight (or None)** \\ \hline April & 72 & 82 & 5426 \\ May & 133 & 266 & 11,412 \\ June & 76 & 289 & 10,492 \\ July & 78 & 511 & 14,019 \\ August & 123 & 497 & 15,835 \\ September & 121 & 493 & 16,380 \\ October & 106 & 396 & 11,538 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Monthly total numbers of three typical convective storm systems in the area of interest from April to October 2016.  
convective storm system dataset mentioned in Section 3.3, a total of 83 predictive factors (see Table 2) from H08/AHI observations and spatiotemporally matched NWP data, are used to train the SWIPE model for identifying three different types of convective storm systems, which is one of the key steps for the SWIPE model. According to the previous studies for identifying and tracking convective storm systems [41], the predictors from H08/AHI mainly include the BTs observed by water vapor absorption and IR split window bands. Related studies by [PERSON] et al [42] also indicate that the ECMWF (European Centre for Medium-range Weather Forecasts) analysis data is likely to be able to capture the synoptic-scale and mesoscale features of convective environments. These weather forecast indices can provide a good description of the thermal (K Index), dynamic (CAPE, CIN, Lifted Index, EBS) and moisture (TPW) characteristics of the atmospheric environment. Details of predictors from GFS NWP data used here are listed in Table 2[43; 44]. Note that the total numbers of three different convective systems will affect the final model training and prediction. Previous studies have already pointed out that the sample ratio of different types in the dataset can significantly impact the final accuracy of the prediction model [22; 24]. For the original dataset, the natural ratio between severe, medium, and weak convections is about 1:3.6:120, which is also referred to as the original dataset or Scenario-0. When the weak convective systems in the model training are too much, the final prediction will be biased towards this excessive type. In order to further improve the prediction accuracy, the numbers of medium convective systems and weak convective systems are reduced. A variety of scale models were tried to ensure an optimal model. The ratios are adjusted to 1:1:1, 1:3:6:3.6 and 1:3:6:7.2, for three scenarios that are marked as Scenario-1, Scenario-2, and Scenario-3, respectively. This method for adjusting proportions of different types in the dataset is known as the sample-balance technique [45]. By including the original sample scenario, Table 3 shows the numbers of weak, medium and severe convections of four typical sample datasets under three different scenarios as described above. Previous studies have shown that using the best performing samples can increase the accuracy of prediction by more than 20% [24].