Impact of aerosol and microphysical uncertainty on the evolution of a severe hailstorm

Forecasting high impact weather events is a major challenge for numerical weather prediction. Initial condition uncertainty plays an important role but so do uncertainties arising from the representation of subgrid-scale processes, e.g. cloud microphysics. Here, we investigate the impact of cloud microphysical parameter uncertainties on the forecast of a selected severe convective storm over South-Eastern Germany in 2019, which is generally referred to as the Munich hailstorm (Wilhelm et al., 2020).
The storm is simulated using the ICON model (2-moment cloud microphysics, 1 km grid-spacing) with perturbed microphysical parameters related to graupel and hail formation. Combinations of parameter perturbations are chosen according to a Latin hyper cube design and one-at-a-time parameter perturbations for the smallest and largest parameter values. Important impacts on surface (hail) precipitation are found for parameters pertaining to (i) CCN and INP activation, (ii) diffusional growth of ice, and (iii) the mass-diameter and mass-fall velocity relations for graupel. The behavior of graupel particles are thereby controlled by their density.
The one-at-a-time parameter perturbation simulations are used to track microphysical process rates. By closing the hydrometeor mass budgets we explore changes in precipitation formation pathways (based on the approach by Barrett and Hoose, 2023) arising from perturbations of the most impactful parameters. Preliminary results show a strong influence of graupel density on the hail particle size distribution as well as total precipitation, but less so on surface hail amount.
The analysis allows us to draw conclusions about the most impactful cloud microphysical parameters for hail forecast uncertainty as well as the underlying mechanisms.