Comparing the impact of environmental conditions and microphysics on the forecast uncertainty of deep convective clouds and hail

Abstract. Severe hailstorms have the potential to damage buildings and crops. However, important processes for the prediction of hailstorms are insufficiently represented in operational weather forecast models. Therefore, our goal is to identify model input parameters describing environmental conditions and cloud microphysics, such as vertical wind shear and strength of ice multiplication, which lead to large uncertainties in the prediction of deep convective clouds and precipitation. We conduct a comprehensive sensitivity analysis simulating deep convective clouds in an idealized setup of a cloud-resolving model. We use statistical emulation and variance-based sensitivity analysis to enable a Monte Carlo sampling of the model outputs across the multi-dimensional parameter space. The results show that the model dynamical and microphysical properties are sensitive to both the environmental and microphysical uncertainties in the model. The microphysical parameters, especially the fall velocity of hail, lead to larger uncertainties in the output of integrated hydrometeor masses and precipitation variables. In contrast, variations in the environmental conditions mainly affect the vertical profiles of the diabatic heating rates.