Objective Prediction of Warm Season Afternoon Thunderstorms in Northern Taiwan Using a Fuzzy Logic Approach

Abstract

n this study, a fuzzy logic algorithm is developed to provide objective guidance for the prediction of afternoon thunderstorms in northern Taiwan using preconvective predictors during the warm season (May?October) from 2005 to 2008. The predictors are derived from surface stations and sounding measurements. The study is limited to 277 days when synoptic forcing was weak and thermal instability produced by the solar heating is primarily responsible for thunderstorm initiation. The fuzzy algorithm contains 29 predictors and associated weights. The weights are based on the maximum of the critical success index (CSI) to forecast afternoon thunderstorms. The most important predictors illustrate that under relatively warm and moist synoptic conditions, sea-breeze transport of moisture into the Taipei Basin along with weak winds inland provide favorable conditions for the occurrence of afternoon convective storms. In addition, persistence of yesterday?s convective storm activity contributed to improving today?s forecast. Skill score comparison between the fuzzy algorithm and forecasters from the Taiwan Central Weather Bureau showed that for forecasting afternoon thunderstorms, the fuzzy logic algorithm outperformed the operational forecasters. This was the case for both the calibration and independent datasets. There was a tendency for the forecasters to overforecast the number of afternoon thunderstorm days. The fuzzy logic algorithm is able to integrate the preconvective predictors and provide probability guidance for the prediction of afternoon thunderstorms under weak synoptic-scale conditions, and could be implemented in real-time operations as a forecaster aid.