A Lagrangian Particle Tracking Framework for the Super-Droplet Method: Development, Implementation, and Application of Backward and Forward Algorithms in SCALE-SDM 5.2.6-2.3.1

Abstract. Understanding the complete lifecycle of cloud hydrometeors is fundamental to advancing cloud microphysics, yet a formally documented and computationally efficient framework for tracking individual particles through complex processes like coalescence within parallelized cloud models has been lacking. This study addresses this methodological gap by presenting the detailed design, implementation, and application of two complementary super-droplet tracking algorithms—a backward "lineage" tracking algorithm and a forward "tagging" tracking algorithm—developed within a Large-Eddy Simulation model coupled with the Super-Droplet Method (SDM). The backward algorithm establishes a direct historical link for every super-droplet, enabling efficient Ο(1) lookup for reconstructing a particle's complete microphysical history. The forward algorithm employs a stratified random sampling method to select and assign persistent identifiers to a representative cohort of super-droplets, allowing for detailed prognostic analysis with manageable data storage costs. A key feature of both algorithms is the comprehensive method for recording and outputting detailed information on coalescence events. The utility and power of these algorithms are demonstrated in a case study of marine stratocumulus. The framework enabled a quantitative, process-level validation of the critical 15–20 µm radius range for the onsite of efficient warm rain initiation. Furthermore, the lineage-tracing capability mechanistically confirmed the classical formation pathway of large droplets within cloud-base updrafts, directly linking large-scale turbulent structures to the lifecycle of individual precipitation embryos. In conclusion, the tracking algorithms presented here provide the scientific community with a powerful and versatile toolset to investigate the intricate lifecycle of cloud particles with unprecedented detail and offer a robust methodology for evaluating and improving microphysical parameterizations in larger-scale weather and climate models.