A Binary Logistic Regression Model for Severe Convective Weather with Numerical Model Data

Abstract

Based on meteorological observations and products of a GRAPES and an ECMWF model from March to April 2014, some indexes and parameters with good relevancy were selected as predictors. Through analyzing the spatial distributions and the binary logistic regressions of the indexes, estimated values of the predictors and severe convective weather diagnostic prediction equations were established to get a severe weather predictor P for forecasting severe convective weather for the next 12 hours in Guangdong province. The equations were tested and analyzed, respectively, with the two models as well as the radiosonde data. The results indicated that the severe weather forecasts’ CSI by the predictor P was obviously higher than by any single index. The TT error between the models and the soundings was small, while the K index of the models was more discrete than the soundings. The index MDPIs were 1 greater than the soundings, but their trends of change were consistent with the soundings.