Large-eddy simulations (LES) with the newThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. R. Heinze et al.at building confidence in the model's ability to simulate small-to mesoscale variability in turbulence, clouds and precipitation. The results are encouraging: the high-resolution model matches the observed variability much better at small-to mesoscales than the coarser resolved reference model. In its highest grid resolution, the simulated turbulence profiles are realistic and column water vapour matches the observed temporal variability at short time-scales. Despite being somewhat too large and too frequent, small cumulus clouds are well represented in comparison with satellite data, as is the shape of the cloud size spectrum. Variability of cloud water matches the satellite observations much better in ICON than in the reference model. In this sense, it is concluded that the model is fit for the purpose of using its output for parametrization development, despite the potential to improve further some important aspects of processes that are also parametrized in the high-resolution model.

ICON (ICOsahedral Nonhydrostatic) is a unified modeling system for global numerical weather prediction (NWP) and climate studies. Validation of its dynamical core against a test suite for numerical weather forecasting has been recently published by Z€ angl et al. (2014). In the present work, an extension of ICON is presented that enables it to perform as a large eddy simulation (LES) model. The details of the implementation of the LES turbulence scheme in ICON are explained and test cases are performed to validate it against two standard LES models. Despite the limitations that ICON inherits from being a unified modeling system, it performs well in capturing the mean flow characteristics and the turbulent statistics of two simulated flow configurations-one being a dry convective boundary layer and the other a cumulustopped planetary boundary layer.

We present a novel Monte‐Carlo ice microphysics model, McSnow, to simulate the evolution of ice particles due to deposition, aggregation, riming, and sedimentation. The model is an application and extension of the super‐droplet method of Shima et al. (2009) to the more complex problem of rimed ice particles and aggregates. For each individual super‐particle, the ice mass, rime mass, rime volume, and the number of monomers are predicted establishing a four‐dimensional particle‐size distribution. The sensitivity of the model to various assumptions is discussed based on box model and one‐dimensional simulations. We show that the Monte‐Carlo method provides a feasible approach to tackle this high‐dimensional problem. The largest uncertainty seems to be related to the treatment of the riming processes. This calls for additional field and laboratory measurements of partially rimed snowflakes.

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