A Lagrangian particle tracking framework for the super-droplet method in SCALE-SDM 5.2.6-2.3.1: implementation and computational evaluation
- 1University of Hyogo (Kobe, Japan)
- 2RIKEN Center for Computational Science (Kobe, Japan)
- 3Nanjing University of Information Science and Technology (Nanjing, China)
Abstract
Lagrangian particle histories provide a useful way to diagnose cloud-microphysical pathways in particle-based models, but their practical use in massively parallel super-droplet simulations depends on how particle identifiers, event records, output files, and post-processing workflows are implemented. This paper documents and evaluates a Lagrangian particle-tracking framework in SCALE-SDM 5.2.6-2.3.1. The framework includes forward tracking, backward tracking, and Two-Pass Hybrid Tracking (TPHT). Forward tracking saves the later states of super-droplets selected before or during a simulation. Backward tracking saves the rank and record location of each selected super-droplet's predecessor at the previous output time, so that post-processing can retrieve that record with an 𝒪(1) lookup instead of searching all super-droplets. Both methods use a common procedure to select all valid super-droplets, a random or height–radius-stratified sample, or an externally supplied target list. TPHT uses two integrations over the same physical time interval. The first records super-droplet identifiers when a chosen radius or coalescence condition is met. After duplicate identifiers are removed, the second starts from the same physical and random-number state and writes backward links only for this reduced set. Controlled computational tests are used to characterise runtime, peak random-access memory (RAM), storage (disk-output volume), the effect of output interval on file volume and mean write time, and sampling error and seed-to-seed variability. A three-dimensional DYCOMS-II RF02 stratocumulus case demonstrates TPHT. In this demonstration, the target criteria selected 19.3 % of the estimated valid super-droplet identities, corresponding to an estimated 5.2-fold reduction in retained backward output after temporary identifier files were removed. This factor is specific to the broad criteria used in this test; much larger reductions are possible for problems with sparser target populations. Agreement of the recorded coalescence events between the two integrations provided evidence of reproducibility under the tested configuration. The case study shows that TPHT can identify particles of interest and reconstruct their saved histories while reducing retained output. In future, the same approach could be applied to other history- or event-based questions, including entrainment and mixing, sedimentation, and ice-particle growth.
Versions
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BibTeX
@article{yin2026lagrangian,
title = {{A Lagrangian particle tracking framework for the super-droplet method in SCALE-SDM 5.2.6-2.3.1: implementation and computational evaluation}},
author = {Chongzhi Yin and Shin-Ichiro Shima and Chunsong Lu},
journal = {Geoscientific Model Development},
year = {2026},
month = oct,
doi = {10.5194/gmd-19-9993-2026},
url = {https://gmd.copernicus.org/articles/19/9993/2026/},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A Lagrangian particle tracking framework for the super-droplet method in SCALE-SDM 5.2.6-2.3.1: implementation and computational evaluation
AU - Chongzhi Yin
AU - Shin-Ichiro Shima
AU - Chunsong Lu
PY - 2026
DA - 2026/10/20
JO - Geoscientific Model Development
DO - 10.5194/gmd-19-9993-2026
UR - https://gmd.copernicus.org/articles/19/9993/2026/
AB - Lagrangian particle histories provide a useful way to diagnose cloud-microphysical pathways in particle-based models, but their practical use in massively parallel super-droplet simulations depends on how particle identifiers, event records, output files, and post-processing workflows are implemented. This paper documents and evaluates a Lagrangian particle-tracking framework in SCALE-SDM 5.2.6-2.3.1. The framework includes forward tracking, backward tracking, and Two-Pass Hybrid Tracking (TPHT). Forward tracking saves the later states of super-droplets selected before or during a simulation. Backward tracking saves the rank and record location of each selected super-droplet's predecessor at the previous output time, so that post-processing can retrieve that record with an 𝒪(1) lookup instead of searching all super-droplets. Both methods use a common procedure to select all valid super-droplets, a random or height–radius-stratified sample, or an externally supplied target list. TPHT uses two integrations over the same physical time interval. The first records super-droplet identifiers when a chosen radius or coalescence condition is met. After duplicate identifiers are removed, the second starts from the same physical and random-number state and writes backward links only for this reduced set. Controlled computational tests are used to characterise runtime, peak random-access memory (RAM), storage (disk-output volume), the effect of output interval on file volume and mean write time, and sampling error and seed-to-seed variability. A three-dimensional DYCOMS-II RF02 stratocumulus case demonstrates TPHT. In this demonstration, the target criteria selected 19.3 % of the estimated valid super-droplet identities, corresponding to an estimated 5.2-fold reduction in retained backward output after temporary identifier files were removed. This factor is specific to the broad criteria used in this test; much larger reductions are possible for problems with sparser target populations. Agreement of the recorded coalescence events between the two integrations provided evidence of reproducibility under the tested configuration. The case study shows that TPHT can identify particles of interest and reconstruct their saved histories while reducing retained output. In future, the same approach could be applied to other history- or event-based questions, including entrainment and mixing, sedimentation, and ice-particle growth.
SN - 1991-9603
ER -