## 2 Data  
Seven months of continuous H08 and GFS NWP data (from April to October 2016) are used here to build a robust and efficient convective storm prediction model (SWIPE) with RF algorithm. This period covers the typical summer precipitation season over China. Himawari-8, the next-generation geostationary satellite belonging to the Japan Meteorological Agency (JMA, [[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/))), was successfully launched into geosynchronous orbit and centered around 140.7\({}^{\circ}\)E on 7 October 2014. The AHI onboard H08 has 16 bands including 4 VIS, 2 near-IR (NIR), and 10 IR bands with central wavelengths ranging from 0.47 to 13.3 \(\upmu\)m. It routinely operates a full disk and five sub-region scanning modes within a 10 min (or 2.5 min for a regionally rapid scanning mode) interval with spatial resolutions 0.5 km and 1 km for VIS bands, and 2 km for NIR and IR bands. As a primary H08 data user in China, the China Meteorological Administration (CMA) can obtain the H08/AHI Level-1B data with geolocation and radiometric calibration from JMA in NRT for now-casting applications [25; 26; 27].  
In addition to the radiances at the top of atmosphere observed by H08/AHI, some other important atmospheric environment parameters are also used. The NWP model data, containing global three-dimension (3D) atmospheric environmental parameters such as temperature, humidity, pressure, wind speed, etc., with a horizontal spatial resolution of 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) and 26 vertical layers from 1000 hPa to 10 hPa, are routinely generated by the National Centers for Environmental Prediction (NCEP) GFS, a global NWP system containing a global computer model and variational analysis run by NOAA National Weather Service (NWS). A linear interpolation technique is used here to match H08/AHI observations and GFS NWP data, and NWP data are mapped to observations. Based on the NWP data, some environmental parameters (such as TPW, K-Index and Lifted Index) are chosen to train the convective storm prediction model, which are likely to be closely associated with the severe convective weather events [28].  
Besides the two datasets mentioned above, we also use the Global Precipitation Measurement (GPM) level three gridded Integrated Multi-satellite Retrievals for GPM (IMERG) V04A version data [29] for reliable training and validation data in this study. The GPM is a joint mission between NASA and JAXA to make frequent observations of global precipitation. It is an important part of NASA's Precipitation Measurement Missions (PMM) program and works with a satellite constellation to provide full global coverage. As the successor to Tropical Rainfall Measuring Mission (TRMM) mission, the GPM can provide more frequent and accurate observations of global precipitation. As a key sensor, the microwave imager captures the precipitation intensity and horizontal morphology, while the dual-band precipitation radar provides three-dimensional structure of precipitation aggregates. IMRG is a merged precipitation product based on GPM observations and other satellite microwave precipitation estimates [30], with a half an hour interval and a spatial resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\). It can cover the global area between the latitudes of 60\({}^{\circ}\)N and 60\({}^{\circ}\)S [31]. This product has been well validated using ground-based gauges or surface based radars [30]. Therefore, the GPM IMRG product is used as the truth for training the SWIPE prediction model based on its high quality.