## 1 Introduction
In recent years, China Meteorological Administration (CMA), Japan Meteorological Agency (JMA), and U.S. National Oceanic and Atmospheric Administration (NOAA) have already successfully launched their own new-generation geostationary weather satellites in succession since 2014. The new generation GEO weather satellites, such as Chinese FengYun-4 (FY-4) series [8], Japanese Himawari-8/9 [18], and U.S. Geostationary Operational Environmental Satellites-R (GOES-R) series [19], carrying advanced sensors, provide new opportunities for detecting and tracking severe convective storm systems. New measurements can help to further understand the occurrence and development of convection from a satellite perspective [20]. It is worthy to note that the Himawari-8 was successfully launched on 7 October 2014. It carries a 16-band Visible (VIS) and IR Advanced Himawari Imager (AHI) with spatial resolutions from 0.5 km (VIS) to 2.0 km (IR) and a full-disk observation within a 10-minute time interval ([[http://www.jma-net.go.jp/msc/en/](http://www.jma-net.go.jp/msc/en/)]([http://www.jma-net.go.jp/msc/en/](http://www.jma-net.go.jp/msc/en/))). With new measurements from the advanced GEO space-based sensors, some advanced machine learning (ML) techniques, such as random forests (RF), support vector machines (SVM), artificial neural network (ANN), deep learning (DL), etc. that were successfully used to solve non-linear weather-related issues [21; 22] and can be used to better understand convective storms. [PERSON] [21] examined the specific problem of combining NWP model, radar, and satellite for forecasting thunderstorm initiation in a one-hour timeframe [21]. These innovative applications are benefited from the rapid development of ML frameworks, such as scikit-learn ([[http://scikit-learn.org/](http://scikit-learn.org/)]([http://scikit-learn.org/](http://scikit-learn.org/))), Theano ([[http://deeplearning.net/software/theano/](http://deeplearning.net/software/theano/)]([http://deeplearning.net/software/theano/](http://deeplearning.net/software/theano/))), TensorFlow ([[http://www.tensorflow.cn/](http://www.tensorflow.cn/)]([http://www.tensorflow.cn/](http://www.tensorflow.cn/))), and PyTorch ([[https://pytorch.org](https://pytorch.org)]([https://pytorch.org](https://pytorch.org))), which are easy to be implemented for statistical model training and predicting. As one of many accurate and high-efficiency ML algorithms, the Random Forest (RF) has been successfully and extensively utilized in weather and remote sensing applications [23]; it is capable of capturing non-linear relationship between predictors and predictands. In this study, based on H08/AHI data, the RF learning algorithm is used to develop a near real-time (NRT) tracking and warning predictive model for convective storm systems. The predictive model can capture the sudden local convective systems from a newly-formed cell using high spatiotemporal resolution H08/AHI IR observations, for example, to predict the occurrence and the intensity of convective storm systems using the variables from the spatially-temporally matched H08/AHI observations and GFS (Global Forecast System) NWP data [24]. Unlike the traditional method, some important parameters, such as total precipitable water (TPW), from NWP data are introduced here to provide atmospheric environmental field information for better identifying convective storm systems. By using AHI and the real-time GFS NWP data in the warning algorithm, the convective storm system tracking and identify model called Storm Warning In Pre-convective Environment (SWIPE) has been developed for nowcasting applications [25]. Section 2 introduces the new GEO and GFS NWP data. Section 3 presents the convective-tracking algorithm and collected dataset. Section 4 elaborates the RF classification algorithm, the SWIPE prediction model and its evaluation. Two typical convective storm cases tracked by the SWIPE model are introduced and discussed in Section 5. Finally, Section 6 provides a summary and future work.