1. Motivation for the visit

PRISTINE (University of Cologne) and FRAGILE (LMU) are two independent projects with different scientific aims under SPP2115, though we share similarities regarding using scientific tools and methods. In particular, the McSnow cloud model is one of the pivotal methods used for scientific research purposes in both projects. For this reason, sharing common knowledge about the best practices for using modeling tools effectively and planning strategies to avoid doubling the work effort is beneficial. The members of the projects already meet regularly online; however, to foster cooperation and allow for a more effective exchange of ideas, an in-person meeting was scheduled.

2. McSnow Simulation

During the short one-week visit at the Ludwig-Maximilians-University of Munich (LMU), I discussed radar polarimetry and started working on the McSnow simulation with the help of Dr. Leonie Von Terzi (FRAGILE, LMU) and Dr. Stefan Kneifel (FRAGILE, LMU). The McSnow model was developed by Brdar and Seifert (Brdar and Seifert, 2018). McSnow is a (1D box model) Lagrangian particle model that simulates the evolution of ice particles via depositional growth, aggregation, riming, melting, and warm phase processes. To reduce computational effort, a super droplet method (Shima et al., 2009) has been used in McSnow for the evolution of multi-property ice particles. Each super-particle represents multiple real particles with the same attributes and position, and each super-particle has a multiplicity (number of real particles represented by it). Note here that no two real particles (true ice particles) have exactly the same position and attributes, and in this sense, a super-particle is a kind of coarse-grained view of particles both in real space and attribute space. McSnow is a 1D box model with no horizontal resolution. We consider a five-square-meter grid-box area with several height layers inside the McSnow simulation. Thus, a super-particle is more of a representation of an ensemble of real particles within a given volume. The super-particle's evolution is then tracked to get aggregation history. In McSnow, ice mass, rime mass, volume, number of monomers, and shape of the monomers are tracked for each super-particle. The model has been improved during the PROM Phase 1-project IMPRINT to explicitly model frozen particle shapes and shape-dependent ice processes. Modeling of ice particle structures with a much higher level of detail is required for advanced scattering simulations in project PRISTINE.

3. Simulated results from the preliminary test run

McSnow simulates the properties of monomer and aggregate particles as a function of height and temperature starting from the domain top. Figure (a) is an example of the number concentration of real particles (\( number / m^3 \)) for monomers and aggregates. It is observed from the figure that the number concentration for monomers (green line) decreases from \( 7000 \) to \( 1000 \) starting from the domain top of \( 8000m \) to the domain bottom. On the other hand, the number concentration for aggregates is \( 0 \) at the domain top and increases as the monomers start forming aggregates through the aggregation process.

Figure (a): Number concentration per \( m^3 \) for monomers and aggregates as simulated by McSnow.

Figures \( (b) \) and \( (c) \) represent concentration per \( m^3 \) of monomers and aggregates at a selected height of \( 2918m \) as a function of size and mass. For example, at \( 2918m \) height, the concentration of monomers is highest for around \( 1mm \) of size, while for aggregates, the peak is a bit shifted to larger sizes (Fig. b). Similar features are observed in figure \( (c) \) as a function of mass.

Figure (b): Number concentration of monomers and aggregates per \( m^3 \) as a function of size.

Figure (c): Number concentration of monomers and aggregates per \( m^3 \) as a function of mass.

4. Summary and future application

During the five days of a research visit to LMU, I got hands-on experience in McSnow model run with some basic simulation setups. I will further use McSnow to select realistic shapes of monomers using aggregation history, which is an essential part of the project PRISTINE. ICON thermodynamic profiles will be used as input to McSnow to compare ICON output with McSnow for the TRIPEx campaign period (Dec 2018 - Jan 2019).