| The representation of cloud microphysics in numerical weather prediction models contributes significantly to the uncertainty of weather forecasts. Particularly difficult is the simulation of convective precipitation, due to their small scale and rapid error growth (Selz and Craig, 2015; Hohenegger and Schär, 2007). In phase 1, we provided a setup to systematically evaluate the performance of 5 cloud microphysics schemes (Table 1) in a numerical weather model (WRF, 400 m grid spacing) by comparison to polarimetric radar observations on a statistical basis over 30 days. This setup is applied in phase 2 with a focus on the distribution of precipitation within convective systems, which was shown before to be ill represented in weather models with respect to the partitioning between weaker stratiform and more intense convective precipitation areas (Han et al., 2019, Shrestha et al., 2022, Quian et al., 2018). By applying the automatic cell-tracking algorithm "tobac" (Sokolowsky et al., 2024), we objectively define convective cores as well as their stratiform surroundings. This allows for a statistical analysis of the distribution as well as of the microphysical properties within these regions. | The representation of cloud microphysics in numerical weather prediction models contributes significantly to the uncertainty of weather forecasts. Particularly difficult is the simulation of convective precipitation, due to their small scale and rapid error growth (Selz and Craig, 2015; Hohenegger and Schär, 2007). In phase 1, we provided a setup to systematically evaluate the performance of 5 cloud microphysics schemes (Table 1) in a numerical weather model (WRF, $400\, \text{m}$ grid spacing) by comparison to polarimetric radar observations on a statistical basis over 30 days. This setup is applied in phase 2 with a focus on the distribution of precipitation within convective systems, which was shown before to be ill represented in weather models with respect to the partitioning between weaker stratiform and more intense convective precipitation areas (Han et al., 2019, Shrestha et al., 2022, Quian et al., 2018). By applying the automatic cell-tracking algorithm "tobac" (Sokolowsky et al., 2024), we objectively define convective cores as well as their stratiform surroundings. This allows for a statistical analysis of the distribution as well as of the microphysical properties within these regions. |