The inclusion of surface synoptic data in very short range predictions of convective events

Summary Predictor vectors, including upper air as well as surface data, were used for categorical forecasting convective events over a subregion of the Czech territory, and the effect of including surface variables in the predictor vector was examined. While upper air data were considered as Perfect Prognosis, the surface data were successively included according to the time of their origin. The forecasting technique was based on linear multiple regression with learning, and the accuracy of the forecast was measured by the Critical Success Index. The input data from the three May-September periods in 1989–91 were used, and the first year served as the learning set. The aerological data from TEMP 12 UTC, simulating Perfect Prognosis, were the source of the upper air predictors. The performance of all, upper air, surface and combined, predictors were evaluated and compared. It turned out that the improvement of prediction accuracy due to the inclusion of surface variables was not negligible. Significant improvements were made in the forecasts of thunderstorm occurrence between 18 and 24 UTC.