The Novel Particle-based Microphysical Model McSnow: 1D and 3D Results

Clouds and precipitation processes still constitute one of the largest uncertainties in current weather prediction and climate models. With increasing model resolutions also the microphysical parametrizations need to be improved. To better understand the interaction of the microphysical processes sedimentation, aggregation, riming, ice multiplication, vapor diffusion, melting, shedding and breakup, we developed the novel Monte-Carlo microphysics model, McSnow [1]. Within this super-particle model, for each individual particle the ice mass, rime mass, rime volume, number of monomers and liquid mass is predicted establishing a five-dimensional particle size distribution. While being computationally more efficient than a corresponding high-dimensional bin model, this approach also enables to look at the growth history of individual precipitation particles. McSnow can be run as stand-alone in sounding-based one-dimensional simulations, with which we show that the Monte-Carlo method provides a feasible approach to tackle this complex problem. Additionally, McSnow is integrated into the non-hydrostatic model ICON and thus can also be run for three-dimensional, highly dynamic cases. We analyze the McSnow results of idealized deep convective events to gain an in-depth understanding of the microphysical processes with the aim to improve their parametrizations in numerical weather prediction and climate models.

[1] S. Brdar and A. Seifert 2017, McSnow – A Monte-Carlo particle model for riming and aggregation of ice particles in a multidimensional microphysical phase space, Journal of Advances in Modeling Earth Systems 10, 10.1002/2017MS001167