Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 20
Articles | Volume 19, issue 20
https://doi.org/10.5194/gmd-19-9993-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-9993-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 20
Development and technical paper
 | 
20 Oct 2026
Development and technical paper |  | 20 Oct 2026

A Lagrangian particle tracking framework for the super-droplet method in SCALE-SDM 5.2.6-2.3.1: implementation and computational evaluation

Chongzhi Yin, Shin-Ichiro Shima, and Chunsong Lu

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Short summary

We developed and tested a way to trace selected cloud particles in a simulation, including how they grow and merge. One run identifies particles that meet chosen conditions, and a second reconstructs only their saved histories. In a three-dimensional test, the saved data were estimated to be one-fifth of what would be needed to keep all particle histories. Larger reductions are possible when fewer particles are selected, making such studies more practical in large simulations.

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