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.
Highlights
Severe convective weather is the main severe weather in the Guangdong Province, China, during its first flood season.e severe convective weather affecting Guangdong Province mainly includes severe thunderstorm wind gusts, hail, tornadoes, and short-time heavy rains
The K indexes of the two numerical weather prediction model (NWP) were relatively discrete compared to the soundings’, and the errors of the 4 stations were all greater than TT indexes’ (Figure 2)
(1) e correlation coefficients between 16 indexes and severe convection weather were analyzed. e correlation coefficients between K index, TT index, MDPI index, IQ index, and severe convection weather were better than the other indexes. en, the 4 indexes were selected for binary logistic regression analysis
Summary
Severe convective weather is the main severe weather in the Guangdong Province, China, during its first flood season. Pablo et al [9] introduced 31 stability indexes in a binary logistic regression model, which selected the most accurate ones for detecting hail days in the region, namely, the Showalter index, dew point temperature at 850 hPa, and TQ index. Pang et al [10,11,12,13] used indexes calculated with the radiosonde data as a potential forecasting factor and made related studies on the severe convective weather potential forecasting in the Guangdong Province. Most of the researches were based on the real-time data, which are of poor temporal and spatial resolutions To improve these resolutions, products of GRAPES, a new numerical weather prediction model (NWP) developed in China with a resolution of 12 km, are adopted in this study. ECMWF (EC) products with a resolution of 25 km were used to compare and analyze the prediction effects
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