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.
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.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
20 Oct 2026
Development and technical paper |
| 20 Oct 2026
| 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
China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory/Collaborative Innovation Centre on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing 210044, China
Shin-Ichiro Shima
CORRESPONDING AUTHOR
Graduate School of Information Science, University of Hyogo, Kobe 6500047, Japan
RIKEN Center for Computational Science, Kobe 6500047, Japan
Chunsong Lu
CORRESPONDING AUTHOR
China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory/Collaborative Innovation Centre on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing 210044, China
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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.
We developed and tested a way to trace selected cloud particles in a simulation, including how...
