HAPpy Hour Seminar : Impacts of Collision-induced Raindrop Breakup on Evolution of Droplet Size Distributions using Lagrangian Superdroplet Method

Abstract: Cloud microphysical processes that influence cloud dynamics are impacted by how raindrop size distributions are represented in numerical cloud models. Collision-induced drop breakup is considered a key mechanism that inhibits the formation of very large raindrops and modifies drop size distributions. However, the scientific understanding of drop collisional breakup is still largely incomplete, which introduces significant uncertainties in parameterization schemes and inadequate representation of warm-rain cloud microphysics in numerical models.

In this study, we investigate the impact of drop breakup on evolution of drop size distributions (DSDs) by implementing drop breakup parameterization schemes within an idealized zero-dimensional box model. The model represents drop breakup in three different modes – filament, sheet, disk – along with all-or nothing stochastic collision-coalescence based on a Lagrangian approach. Lagrangian particle-based approaches have gained popularity in recent years due to their advantages over traditional Eulerian bulk and bin microphysics schemes, including improved representation of sub-grid scale variability and elimination of numerical diffusion.

To sample fragment size distributions resulting from breakup, we rigorously test and intercompare two different modeling strategies: (i) single-mode sampling and (ii) all-mode sampling and merging based on specified tolerance factors. The results include sensitivity analyses of DSD evolution to a wide range of modeling parameters, such as initial number of super droplets (N SD ), initial DSD characteristics, model time steps, number of realizations, minimum fragment size, rain rates, and so on. We also investigate equilibrium DSDs in the presence of both coalescence and breakup using both sampling approaches and compare the results with existing literature.

These numerical experiments offer valuable insights to support the implementation of drop breakup in higher-dimensional models such as CM1-SDM and can be used to explore key cloud microphysical and dynamical processes, including cold pool formation.