Current state-of-the-art regional numerical weather prediction (NWP) models employ kilometer-scale horizontal grid resolutions, thereby simulating convection within the grey zone. Increasing resolution leads to resolving the 3D motion field and has been shown to improve the representation of clouds and precipitation. Using a hectometer-scale model in forecasting mode on a large domain therefore offers a chance to study processes that require the simulation of the 3D motion field at small horizontal scales, such as deep summertime moist convection, a notorious problem in NWP. We use the ICOsahedral Nonhydrostatic weather and climate model in large-eddy simulation mode (ICON-LEM) to simulate deep moist convection and distinguish between scattered, large-scale dynamically forced, and frontal convection. We use different ground- and satellite-based observational data sets, which supply information on ice water content and path, ice cloud cover, and cloud-top height on a similar scale as the simulations, in order to evaluate and constrain our model simulations. We find that the timing and geometric extent of the convectively generated cloud shield agree well with observations, while the lifetime of the convective anvil was, at least in one case, significantly overestimated. Given the large uncertainties of individual ice water path observations, we use a suite of observations in order to better constrain the simulations. ICON-LEM simulates a cloud ice water path that lies between the different observational data sets, but simulations appear to be biased towards a large frozen water path (all frozen hydrometeors). Modifications of parameters within the microphysical scheme have little effect on the bias in the frozen water path and the longevity of the anvil. In particular, one of our convective days appeared to be very sensitive to the initial and boundary conditions, which had a large impact on the convective triggering but little impact on the high frozen water path and long anvil lifetime bias. Based on this limited set of sensitivity experiments, the evolution of locally forced convection appears to depend more on the uncertainty of the large-scale dynamical state based on data assimilation than of microphysical parameters. Overall, we judge ICON-LEM simulations of deep moist convection to be very close to observations regarding the timing, geometrical structure, and cloud ice water path of the convective anvil, but other frozen hydrometeors, in particular graupel, are likely overestimated. Therefore, ICON-LEM supplies important information for weather forecasting and forms a good basis for parameterization development based on physical processes or machine learning.
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