## 6 Summary  
This investigation aims to develop an efficient and robust predictive model called SWIPE for tracking and identifying sudden local convective storm systems over East Asia using combined AHI spectral, temporal, spatial information and the NWP based atmospheric environmental information. Based on an advanced RF learning algorithm, seven months of continuous GPM gridded rain rate data are used to define the three types of convective storms. H08/AHI and NWP data from April to October in 2016 are used to make a RF model training dataset. The RF algorithm is chosen because of its merits on better capturing non-linear patterns between predictors and sudden local convective storm systems. Before making a training dataset, a classical area-overlapped method is employed to track the potential convective cloud clusters using three continuous BT images at the 10.4 um band from AHI. Built on the conclusions of previous studies, a sample-balance technique is used to randomly reduce the sample numbers of majority class in the training dataset. This technique can effectively equate the numbers of minority and majority class samples, and improve the poor performance on predicting both the minority and majority classes. Finally, 83 variables in total, including IR window bands and water vapor absorption bands observations from H08/AHI, and the thermal (K-Index), dynamic (CAPE, CIN, LI, and EBS) and moisture (TPW) parameters of atmospheric environment from NWP, are chosen as predictors to train and establish the RF classification model. It is found that some variables from H08 (i.e. water vapor bands at 6.2 um, 6.9 um and 7.3 um) and NWP data (i.e. TPW and CIN index) show relatively high  
Figure 6: Same as Figure 5, for a sudden convective storm case at 03:40 UTC on 27 July 2018 in Shandong province of China. ranks in RF model training. Because of their high dependency on convectively dominated precipitation areas, it implies the importance of predictors from both H08 and NWP data for training a convective storm system classification model. Through parameter tuning iteratively in the RF model, an optimal classification predictive model is chosen here as final SWIPE for research and applications; which takes into account the needs for high POD on medium and severe convective storm systems recognition. The final accuracy of the optimal RF model under Scenario-1 is 0.79 for all the convective storm systems classification. The POD of the optimal RF model for severe and medium convections can also reach 0.66 and 0.70, respectively. The use of NWP variables can noticeably improve the prediction of convective storms, especially for medium cases. Therefore, combined satellite and NWP data are important for the effective applications of this RF algorithm based SWIPE model. Two typical sudden local convective storm cases in Hainan and Shandong provinces of China in 2018 are studied for demonstration of SWIPE applications. These two cases are successfully tracked and captured by the SWIPE algorithm 2 hours and 40 minutes and 56 minutes earlier than the heavy rainfall event starting, respectively. In the future, NWP data with a higher spatial resolution will be used to further improve the SWIPE prediction model. Also, some predictors for training SWIPE model need to be adjusted. While usually ground based radar observations provide critical information on storm development after it is initiated, in this study, the ground based radar observations are not used because the focus here is on the local convective storm identification in the pre-convection environment. The option to use ground-based radar observations will also be included in the model in the future. For example, the radar observations can be either used to define the convective categories instead of using GPM, or used as additional predictors in the RF model. Conceptualization, M.M., Y.A. and J.L. ; methodology, J.L., M.M. and D.Q. ; software, M.M., F.S., Z.L. ([PERSON]), Y.A. and Z.L. ([PERSON]); validation, Z.L. ([PERSON]), D.D., G.L., Y.L. and X.Z. ; formal analysis, Z.L.