Deep moist convection constitutes one of the most important atmospheric transport mechanisms and is associated with several extreme-weather events (i.e. storms, flooding). As a consequence of climate change, the severity of these events is expected to increase in the future. Spatiotemporally highly resolved short-term predictions of deep moist convection are an essential tool to limit significant social and economic damage. Applications range from flood forecasting to the optimal routing of air traffic.

An important part of the forecasting uncertainties stems from the insufficient representation of the governing convective processes. For this reason, this PhD thesis aims at developing a lifecycle model of deep moist convection and integration that model into short-term forecasting. As an observational basis, we rely on the new Meteosat Third Generation (MTG) satellite mission. The development of the lifecycle model shall combine physical principles and machine-learning methods. In addition to thorough validation of the model, a case study over the Alps is planned. To this end, a comparison with the digital twin currently developed by the Italian and German weather services should be conducted.

The integration of this PhD thesis into the Italian-German IDEA-S4S network, coordinated by the German weather service (DWD), offers significant potential for cooperation. In particular, an integral part of the PhD will be a twelve-months research stay at the Italian weather service (Arpae) in Bologna during the second year.