Binary Logistic Regression Models for short term prediction of premonsoon convective developments over Kolkata (India)

Scientists have identified certain dynamic and thermodynamic parameters, which are significant for the occurrence of convective developments in the premonsoon period (March-May) in Kolkata (India). In this paper Binary Logistic Regression Models have been considered for prediction of these convective developments from a prior knowledge of the values of the significant parameters. Analyses have been carried out for lead times of 12 h. Initially the evening radiosonde information (1200 UTC) has been considered as these convective developments occur in Kolkata mostly toward the evening. The performance of the model for similar developments in the morning has also been taken up at a later point. As some of the identified parameters are correlated, the dimension of the problem has been reduced and uncorrelated explanatory variables generated for the Logistic Regression Model. Two conventional variable selection methods, for example, forward and backward selections have been used for this purpose. A comparative study of the two variable selection procedures in the light of the predictive power of the resulting models has been presented. The probable causes underlying the poor performance of the models in case of correct prediction of morning convection over the city has been explored and possible remedies suggested at the end. Copyright (c) 2006 Royal Meteorological Society