| 21 Jul 2026
Exploring Cloud Microphysics Modeling Concepts the Pythonic Way
Abstract. We share with the community a concise Pythonic codebase for hands-on exploration of cloud microphysics modeling concepts. It includes an adiabatic air-parcel model simulating diffusional droplet growth – leading to condensation, activation, evaporation, and ripening; as well as a box model resolving collisional growth of particles using the Super-Droplet Method Monte-Carlo scheme. Leveraging Pythonic abstractions and programmatic unit handling, the code combines pseudo-code-level readability, auditability, and dimensional-correctness enforcement with high performance through just-in-time (JIT) compilation, which generates native machine code on the fly, avoiding bytecode interpretation overhead for compute-heavy routines. With the goal of illustrating how concise yet complete implementations support both teaching and research software engineering, the paper includes and narrates the entire code, plotting logic included. The code runs with a single click "in the cloud" – on Google Colab or other Jupyter hubs, and thus constitutes a suitable resource for self-study or for a short course in microphysics modeling. Presented technical solutions originate from the PySDM particle-based simulation package, but are applicable in a wider scope and independently of PySDM as exemplified here. The paper includes section-wise exploratory exercises designed to guide further application of the methods and concepts discussed.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Geoscientific Model Development.
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