New developments in PySDM and PySDM-examples v2: collisional breakup, immersion freezing, dry aerosol initialization, and adaptive time-stepping

Abstract

PySDM and the accompanying PySDM-examples packages are open-source modeling tools for computational studies of atmospheric clouds, aerosols, and precipitation. The project hinges on the particle-based microphysics modeling approach and Pythonic code design. The eponymous SDM refers to the Super Droplet Method -a Monte-Carlo algorithm introduced in Shima et al. (2009) to represent the coagulation of particles in modeling frameworks such as Large-Eddy Simulations (LES) of atmospheric flows. Recent efforts have culminated in the "v2" release line, which includes representation of a variety of new processes for both liquid and ice-phase particles, performance enhancements such as adaptive time-stepping, as well as a broadened suite of examples which demonstrate, test, and motivate the use of the SDM for cloud modeling research.