Using statistical emulation to quantify microphysical uncertainties for the Andreas hailstorm in 2013

We investigate microphysical uncertainties in hailstorms using statistical emulation in a single model framework with the objective to disentangle the relative contributions from aerosols, microphysical parameters and environmental conditions to the uncertainty in cloud-, precipitation- and hail-related parameters.

Our selected case study is the Andreas hailstorm on 28 July 2013 in the Neckar Valley and over the Swabian Jura in Southwest Germany. We perform model simulations on cloud-resolving scale with the numerical weather prediction model ICON coupled with the aerosol module ART (ICON-ART). We use a two-moment cloud microphysics scheme with a representation of ice nucleation by dust aerosols.
We generated a perturbed parameter ensemble (PPE) to sample uncertainties in cloud-, precipitation- and hail related parameters. Six parameters from the categories aerosols, microphysics and environmental conditions were jointly perturbed, namely the cloud condensation nuclei (CCN) and ice nuclei (IN) concentrations, the riming efficiency of graupel and hail, the convective available potential energy (CAPE) and vertical wind shear. The defined parameter ranges are based on forecast analysis and literature. We used the maximin Latin hypercube algorithm to distribute the parameters well-spaced in the six-dimensional parameter uncertainty space. For these six parameters, an ensemble of 90 members was generated and in addition a smaller independent ensemble of 45 members serves for validation.

We used the Gaussian process emulation and developed emulators for hail- and precipitation related output variables. To quantify contributions to the uncertainty in the output variables from the perturbed parameters individually as well as interactions between them, a variance-based sensitivity analysis was performed. We will present first results, which reveal the importance of the CCN concentration for controlling the number concentration of hail particles as well as the CCN concentration and environmental conditions for controlling the amount of hail and precipitation in the model. The geographical distribution of hail and precipitation shows a large variety among the ensemble members, with storm tracks shifted further to the north or south compared to the reference simulation. The path of the storm track is thereby mainly controlled by CAPE and the vertical wind shear, however, aerosol parameters seem to be important for the development of multiple storm tracks.