Identifying Cloud Regimes and Their Transitions Related to Extreme Precipitation and Hail Events over the Alps Through Self-Supervised Learning

Extreme precipitation and hail events over complex terrain are strongly dependent on the multiscale evolution of convective systems, yet their development remains challenging to observe and model. In this study, we apply a self-supervised learning (SSL) framework to Meteosat infrared 10.8 μm imagery over the Alpine region to derive physically meaningful representations of convective cloud organization across the full daily cycle. The learned representation organizes convective cloud scenes into three primary regimes: early convection (EC), deep convection (DC), and overcast anvil systems (OA), each exhibiting distinct cloud-top properties, spatial structures, and day–night distributions consistent with established convective development stages. Within the developed SSL framework, reported extreme precipitation and hail events are preferentially associated with these regimes, occupying the upper tail of cloud-top height, optical thickness, and cloud coverage distributions within the latent space. This indicates that extremes correspond to rare, intense realizations of continuous convective variability. Two dominant pathways of cloud structure evolution are identified: a rapid EC→DC transition and a persistent DC mode. The former is characterized by fast cloud growth and intensification, while the latter corresponds to mature convective systems with sustained cloud properties. Overall, the results demonstrate that infrared-based SSL representations capture physically consistent convective stages and their transitions, providing a novel observational framework for characterizing extreme convective events and supporting the evaluation of convection-resolving numerical weather prediction models.