Forecasting of thunderstorms in pre-monsoon season over northwest India

Abstract

Early detection and forecasting of thunderstorms is important in safeguarding and prevention of damages resulting from violent thunderstorms. An Expert System for Thunderstorm Forecasting (ESTF) during pre-monsoon season over Delhi the representative location over northwest India has been developed for the first time in India by using technique of approaching the problem “bottom up” by using inductive machine learning techniques to automatically acquire the knowledge about thunderstorm forecasting from the weather development data set consisting of  TEMP data of Delhi for the months of May and June for the years from 1995-1999. Only input required is the entry of 0000 UTC TEMP data of Delhi at surface, 850, 700, 500 and 300 hPa levels. The rules are based on stability indices and other thermodynamic parameters evaluated from the said sounding. The system also provides climatological information about thunderstorms over Delhi. To compare the ESTF with the objective techniques, Dynamical-statistical methods for yes or no type thunderstorm occurrence forecast over Delhi during pre-monsoon months of May and June have been developed by using graphical discrimination method and multiple regression method and by using the same development data set i.e.,  TEMP data of Delhi for the months of May and June for the years from 1995-1999 and by using the same potential predictors as used in development of ESTF. In multiple regression method the parameters were found to be significant by stepwise screening procedure. The three methods developed were tested with independent data sets of May and June for the years from 2000-2001. Comparison of verification parameters of the forecast issued by Graphical Discrimination method, Multiple Regression Technique and by ESTF indicates that results of multiple regression method are better than those of graphical discrimination method. The results obtained by using ESTF were better than those obtained by using dynamical statistical models.