Accurate and prior identification of local severe storm systems in pre-convection environments using geostationary satellite imagery measurements is a challenging task. Methodologies for “convective initiation” identification have already been developed and explored for operational nowcasting applications; however, warning of such convective systems using the new generation of geostationary satellite imagery measurements in pre-convection environments is still not well studied. In this investigation, the Random Forest (RF) machine learning algorithm is used to develop a predictive statistical model for tracking and identifying three different types of convective storm systems (weak, medium, and severe) over East Asia by combining spatially-temporally collocated Himawari-8 (H08) measurements and Numerical Weather Prediction (NWP) forecast data. The Global Precipitation Measurement (GPM) gridded product is used as a benchmark to train the predictive models based on a sample-balance technique which can adjust or balance the samples of three different convection types to avoid over-fitting any type of dataset. Variables such as brightness temperatures (BTs) from H08 water vapor absorption bands (6.2 μm, 6.9 μm and 7.3 μm) and Total Precipitable Water (TPW) from NWP show relatively high ranks in the predictive model training. These sensitive variables are closely associated with convectively dominated precipitation areas, indicating the importance of predictors from both H08 and NWP data. The final optimal RF model is achieved with an accuracy of 0.79 for classification of all convective storm systems, while the Probability of Detection (POD) of this model for severe and medium convections can reach 0.66 and 0.70, respectively. Two typical sudden convective storm cases in the warm season of 2018 tracked by this algorithm are described, and results indicate that the H08 and NWP based statistical model using the RF algorithm is capable of capturing local burst convective storm systems about 1–2 h earlier than the outbreak of heavy rainfall.

Highly convection-related short-duration heavy rainfall (SDHR), defined as rainfall greater than 20 mm h⁻¹ of a whole hour, causes severe damage every year in China. An objective forecasting method is developed to provide guidance products for the short-term probability of SDHR. Representative parameters of environmental moisture content, instability, and dynamical forcing are selected as predictors based on the ingredients-based methodology. The predictors are selected by comparing their ability to discriminate between SDHR and both no rainfall and ordinary rainfall with hourly rainfall records and the NCEP reanalysis dataset during the warm seasons of 2002 and 2009. A fuzzy logic approach is obtained for the calculation of SDHR probability. Intervals of intensities are obtained based on specific percentiles and various weight settings examined. The probabilistic SDHR forecasts during the 2015 warm seasons with the NCEP GFS dataset are obtained, and forecasts are evaluated by using an operational used spatial verification method. Results show that the reference operational SDHR forecasts are surpassed by the 00–12 h period objective SDHR forecasts measured with the maximum critical success index (CSI), and even the average CSI (CSI_ave) for the top groups is better than the reference. The guidance SDHR products are skillful within 60 h. Although the weights vary significantly, the short-term patterns of the SDHR probability are mainly determined by the environmental conditions. The objective forecasting method is ingredients-based but is combined with fuzzy logic algorithms. The new approach provides a feasible exploration of the convective weather phenomenon.