Analysis and nowcasting of deep convective systems over Germany in multi-source data

By using a multi-source data set consisting of high resolution satellite, radar, lightning, and model data this study presents the analysis of characteristics of deep convective systems over Germany and first results of a new model to predict the remaining lifetime of existing thunderstorms. Contrary to previous studies, the analysis was performed for the full mixture of observed convective systems regardless of their organization type, since our focus is an operational forecasting environment where no simple method is available to differentiate organization types. Basis for the study are all deep convective cell detections in satellite data (using Cb-TRAM, Thunderstorm Tracking and Monitoring) in a five month period (June 2016, May, June, and July 2017, and June 2018). The lifetimes of all cells are normalized, averaged and separated into life cycle phases to investigate the behavior of the parameters from the different data sources during the detected lifetime. Furthermore, the thunderstorm cells are sorted by their lifetime to determine differences between the characteristics of long- and short-lived convective systems. Parameters with predictive skill are then combined with fuzzy logic to determine the actual stage of a thunderstorm, and to nowcast its remaining lifetime. It will be shown that the new lifetime prediction model contributes to an improvement of the thunderstorm nowcasting.