The Microphysics of Severe Storms as Seen by a Lagrangian Super-Particle Model: ICON/McSnow

Cloud microphysical processes contribute significantly to thecomplexity of severe storms such as squall lines, bow echoes, andsupercells. Bulk microphysical schemes, which are most commonly usedin cloud-resolving models, have substantial limitations and greatlysimplify the complexity of microphysics in real clouds. Even spectralbin microphysics, though computationally more expensive than bulkschemes, still involves simplifications regarding ice particle habits,snowflake geometry variability, and the properties of mixed-phasehydrometeors in the wet-growth regions of convectivestorms. Lagrangian particle models can overcome many of theselimitations and offer valuable opportunities for detailed studies ofthe microphysics in severe storms.We present simulations of a squall line using ICON/McSnow toinvestigate hydrometeor types, particle habits, and microphysicalprocesses such as aggregation, riming, and fragmentation ofsnowflakes, and how these processes influence storm dynamics andprecipitation efficiency. McSnow is a Lagrangian super-particlemicrophysical model that predicts ice mass, rime mass, rime volume,frozen mass, liquid mass, aspect ratio, ice volume, monomer count, andparticle habit of individual hydrometeors. This enables unprecedenteddetail in representing microphysical properties in severe storms,including hail formation.In this study, we focus on the phenomenology of an idealized squallline. What types of hydrometeors does McSnow simulate throughout thelifecycle of a squall line? How does the transition from theconvective core to the stratiform region evolve, and what are thecharacteristics of particles that end up in the stratiform region?Do prognostic ice crystal habits or variations in snowflake geometryinfluence the structure and evolution of squall lines? Finally, canICON/McSnow realistically represent the wet-growth region and theformation of hail?