1. Broadening of adiabatic droplet spectra through eddy hopping. Polluted versus pristine environments
Wojciech W. Grabowski, Kamal Kant Chandrakar, and Hugh Morrison
MMM Laboratory, NSF National Center for Atmospheric Research, Boulder, Colorado
Abstract:
The observed widths of cloud droplet spectra in adiabatic volumes of natural clouds have been a conundrum in cloud physics from the early days of in-situ cloud observations. Observed spectral widths are often in the range of 1 to 2 microns, whereas adiabatic parcel calculations suggest widths up to only a few tenths of 1 micron. We use a 1D Eulerian updraft model with Lagrangian particle–based microphysics (Grabowski et al. J. Atmos. Sci. 2025) to study the impact of cloud turbulence on droplet formation and diffusional growth. The focus is on contrasting adiabatic spectral broadening in pristine and polluted environments. The model either includes or excludes effects of cloud turbulence. The impact of turbulence is simulated using a stochastic model of vertical velocity fluctuations that drive supersaturation fluctuations experienced separately by each superdroplet. The specific setup considers shallow cumulus clouds growing from a turbulent convective boundary layer and featuring cloud base updrafts between 1 and 4 m s-1. Turbulence significantly impacts CCN activation and droplet diffusional growth above the cloud base and leads to an increased adiabatic spectral width aloft. The impact is moderate for polluted clouds, but spectral widths in pristine conditions are up to several times larger than those without turbulence. In contrast, adiabatic simulations without turbulence typically feature wider droplet spectra in polluted clouds. The difference comes from a larger range of activated CCN and a larger magnitude of supersaturation fluctuations for the same vertical velocity fluctuations because of a larger phase relaxation time in pristine conditions.

2. A Lagrangian Particle Tracking Framework for the Super-Droplet Method: Development, Implementation, and Application of Backward and Forward Algorithms in SCALE-SDM
Chongzhi Yin¹, Shin-ichiro Shima^2,3, Chunsong Lu¹
1. State Key Laboratory of Climate System Prediction and Risk Management/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
2. Graduate School of Information Science, University of Hyogo, Kobe 6500047, Japan
3. RIKEN Center for Computational Science, Kobe 6500047, Japan
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 O(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 ~15 µm droplet radius threshold for 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.

3. Sensitivity of Cumulonimbus Clouds to Background Aerosol Levels: Insights from the Super-Droplet Method
Manhal Alhilali¹, Shin-ichiro Shima^1,2, Seiya Nishizawa², Soumya Samanta³, Sachin Patade³, Neelam Malap³, Kulkarni Gayatri³, Thara Prabhakaran³, and Wojciech W. Grabowski⁴
1. Graduate School of Information Science, University of Hyogo, Kobe, Japan
2. RIKEN Center for Computational Science, Kobe, Japan
3. Indian Institute of Tropical Meteorology, Pune, India
4. MMM Laboratory, NSF National Center for Atmospheric Research, Boulder, Colorado
Abstract:
We use the Super-Droplet Method in a nonhydrostatic model (SCALE-SDM) to run idealized cumulonimbus simulations constrained by CAIPEEX campaign observations from the rain-shadow region of the Western Ghats. Starting from a realistic baseline aerosol distribution, we systematically vary background CCN from pristine to polluted and quantify the resulting microphysical–dynamical responses. Diagnostics include activation, condensational growth, collision–coalescence, and ice-phase pathways; droplet/ice size spectra; supersaturation and wind profiles; condensate partitioning (cloud/rain/ice); cloud-top height; precipitation onset; peak rain rate; and cell lifetime. The results identify the aerosol regimes that most strongly modulate precipitation efficiency and storm evolution, providing process-level constraints directly relevant to nowcasting, hazard guidance, and realistic evaluation of weather-modification strategies.

4. Aerosol Sensitivity Experiments on an Isolated Cumulonimbus under a Typhoon Environment Using the Super-Droplet Method
*Takuya Tobara¹, Hironori Fudeyasu¹, Ryuji Yoshida¹, Manhal Alhilali², Shinichiro Shima², Hiroaki Yoshioka³
1 Yokohama National University, Japan
2 University of Hyogo, Japan
3 Rera Tech Inc., Japan
Abstract:
Under the Moonshot Research and Development Program Goal 8, we are exploring typhoon modification techniques such as aerosol injection (cloud seeding). Since bulk or bin schemes rely on parameterized microphysics, they struggle to represent supercooled liquid water accurately. The Super-Droplet Method (SDM), which explicitly solves the governing equations for individual particle processes, offers a promising alternative. This study applies SDM to investigate the response of isolated cumulonimbus clouds in a typhoon environment to changes in aerosol concentration.
We first reproduced Typhoon Hagibis (2019) with the nonhydrostatic model SCALE-RM (ver. 5.4.5). Thermodynamic profiles were azimuthally averaged within a 3°–5° radius from the typhoon center during its developing stage and used as the environmental sounding. Idealized warm-bubble experiments were then performed with SCALE-SDM, using a horizontal and vertical resolution of 200 m. The control run assumed a soluble aerosol number concentration of 105 cm⁻³, and sensitivity experiments increased this concentration by factors of 2–5.
Results showed that higher aerosol concentrations delayed the peak of precipitation intensity between 10–40 min after convection initiation, although accumulated rainfall after 60 min did not display a systematic trend. Supercooled liquid water content within the freezing layer increased with aerosol concentration, suggesting that droplet growth by collision–coalescence and riming was suppressed due to smaller droplet sizes in the high-CCN cases.
These findings demonstrate that in a typhoon environment, elevated aerosol concentrations increase cloud droplet number in the lower troposphere and enhance supercooled liquid water in the upper troposphere. This highlights the potential role of aerosol loading in modifying storm microphysics. Future work will focus on combining CCN increases with enhanced freezing aloft to improve the scientific basis and accuracy of cloud seeding strategies for typhoon modification.
Acknowledgement:
This study is supported by JST Moonshot R&D Program (Grant Number: JPMJMS　2282-03 and JPMJMS2283-13).

5. Line Shape Converging systems representation across scales from Direct Numerical Simulations, Large Eddy Simulations, and Mesoscale Simulations
Konduru Rakesh Teja¹ and Shin-ichiro Shima²
1. Earth Observation Research Center, First Space Technology Directorate, JAXA, Tsukuba, Japan.
2. Graduate School of Information Science, University of Hyogo, Kobe, Japan.
Abstract:
Line-shaped converging precipitation systems are prominent mesoscale features in the Japanese atmosphere, often associated with intense precipitation and convective activity. Two primary types are observed in Japan: the BB (Back-to-Back) type and the BSB (Back-Side-Back) type. This study focuses on the BB type, characterized by lateral wind convergence under a prevailing ambient flow, forming a distinct convergence line. The key feature is that the convergence is symmetrical and lateral, without a strong frontal boundary, distinguishing it from BSB-type systems which involve more complex interactions with frontal zones. To investigate the origin and multiscale representation of such systems, we conducted simulations across three scales: Direct Numerical Simulation (DNS), Large Eddy Simulation (LES), and mesoscale modeling (MESO). DNS was performed using the newly developed DNSr v2.0, a high-resolution solver built from scratch to resolve fine-scale turbulence and flow structures. The DNS successfully captured the lateral convergence mechanism intrinsic to BB-type systems, offering insights into the microscale dynamics. LES and mesoscale simulations were conducted using the SCALE-SN14 model, which allowed us to simulate an ideal case of BB-type convergence. By comparing results across scales, this study reveals how line-shaped converging systems manifest from turbulence-resolving scales to mesoscale dynamics. DNS helps to isolate the fundamental physical mechanisms driving line formation, free from parameterization errors. The multiscale approach enhances our understanding of the physical processes governing the formation and maintenance of BB-type convergence lines. These findings contribute to deepen our understanding of convective organization, which can be useful for its prediction.

Keywords: Direct Numerical Simulation, Large Eddy Simulation, Mesoscal Simulation, line-shaped precipitation system, convergence-line.

6. Holographic particle tracking for cloud microphysics: from 3D trajectories to collision kernel statistics
Dai Nakai, Fumiya Iwatani, Yohsuke Tanaka
Kyoto Institute of Technology, Kyoto, Japan
Abstract:
Inline holography enables the simultaneous reconstruction of particle size and 3D position within a sampling volume from a single 2D hologram, facilitating particle tracking velocimetry (PTV) and direct observation of collision–coalescence via high-speed imaging. We leverage this capability to investigate how turbulence accelerates cloud droplet growth by visualizing collision–coalescence events and quantifying the collision kernel. Our research is divided into three stages: (1) improving a holographic PTV algorithm and constructing a dedicated wind tunnel; (2) validating the system by measuring collision kernels in laminar flow and benchmarking them against prior studies; and (3) measuring collision kernels under controlled turbulence. In this talk, We will first survey the relevant experimental literature and outline current challenges, then introduce our holographic measurement methodology and experimental apparatus, present recent results, and discuss the strengths and limitations of our system.

7. Development and Challenges of the Building-Resolving Urban Meteorological Model “City-LES”: Approaches to Simulating Dry Mist Dispersion and Shallow Cumulus at O(m)-O(10m) Resolution
Hiroyuki Kusaka
University of Tsukuba, Tsukuba, Japan

8. A Model Intercomparison Study of Mixed-Phase Clouds in a Laboratory Chamber,
A. Wang¹, S. Chen², S. Krueger³, P. Dziekan⁴, K. Enokido⁵, F. Hoffmann⁶, *A. Makulska⁴, B. Mehlig⁷, G. Sardina⁸, G. Sarnitsky⁷, S. Schmalfuß⁹, S. Shima⁵, F. Yang¹⁰, M. Ovchinnikov¹, R. Shaw¹¹
1. Pacific Northwest National Laboratory, Richland, Washington, USA
2. National Center for Atmospheric Research, Boulder, Colorado, USA
3. The University of Utah, Salt Lake City, Utah, USA
4. University of Warsaw, Faculty of Physics, Institute of Geophysics, Warsaw, Poland
5. University of Hyogo, Kobe, Hyogo, Japan
6. Ludwig Maximilian University, Munich, Germany
7. University of Gothenburg, Gothenburg, Sweden
8. Chalmers University of Technology, Gothenburg, Sweden
9. Leibniz Institute for Tropospheric Research, Leipzig, Germany
10. Brookhaven National Laboratory, Upton, NY, USA
11. Michigan Technological University, Houghton, Michigan, USA
Abstract:
Mixed-phase clouds, in which supercooled liquid water and ice coexist, play a crucial role in weather and climate systems. They remain difficult to represent in models due to uncertainties in the description of microphysics and limited observational constraints. To address this, we present a model intercomparison study using laboratory data from the Michigan Technological University Π Chamber, which provides a controlled environment for generating steady-state mixed-phase clouds. Ten model configurations were evaluated, ranging from bulk and stochastic models to direct numerical simulations (DNS) and largeeddy simulations (LES) with bin and Lagrangian microphysics. Models’ responses to different ice injection rates were tested, focusing on cloud glaciation and supersaturation dynamics. Models diverged in the mixed-phase evolution. Differences in grid resolution, microphysical schemes, and wall-forcing treatments influenced glaciation rates and liquid persistence. Models with coarse resolution or simplified microphysics tended toward rapid, sometimes complete, glaciation, whereas higher-resolution Lagrangian and bin microphysics schemes sustained mixed-phase states which were more consistent with chamber observations. The results emphasize the importance of chamber experiments as benchmarks for numerical models.

9. Lagrangian Particle–Based Simulation of Aerosol-Dependent Vertical Variation of Cloud Microphysics in a Laboratory Convection Cloud Chamber,
Inyeob La¹, Wojciech W. Grabowski², Yongjoon Kim³, Sanggyeom Kim⁴, and Seong Soo Yum⁴,¹
1. Climate and Environmental Research Institute, Korea Institute of Science and Technology, Seoul, Republic of Korea
2. MMM Laboratory, NSF National Center for Atmospheric Research, Boulder, CO, USA
3. Glocal M&S Co., Ltd., Seoul, South Korea
4. Department of Atmospheric Sciences, Yonsei University, Seoul, Republic of Korea
Correspondence to: Inyeob La (iyna481@kist.re.kr) and Seong Soo Yum (ssyum@yonsei.ac.kr)
Abstract:
We investigate vertical variability of cloud microphysics in a turbulent convection cloud chamber using large-eddy simulations coupled to a Lagrangian super-droplet model. Experiments emulate the KIST chamber and use realistic aerosol distributions from VOCALS and Seoul. Cloud water mixing ratio (qc) increases with height via activation and growth, but the gradient weakens as aerosol concentration rises. Enhanced loading intensifies vapor competition, shortens phase-relaxation time, and reduces supersaturation variability, producing more uniform qc profiles. Lagrangian trajectories show ascent strongly promotes activation in clean conditions, but this influence diminishes in polluted conditions. Results clarify aerosol-dependent structure and inform parameterizations and aerosol-cloud interactions.

10. A study on bifurcation and stability in the co-condensation dynamics of Venus’s acid clouds using a box model
Hiroki Ando¹, Satoru Nakano², Shin-ichiro Shima³, Masahiro Takagi¹, Hideo Sagawa¹
1. Faculty of Science, Kyoto Sangyo University, Kyoto, Japan
2. Graduate School of Simulation Studies, University of Hyogo, Kobe, Japan
3. Graduate School of Information Science, University of Hyogo, Kobe, Japan
Abstract:
Venus is globally covered by a thick cloud layer, primarily composed of sulfuric acid droplets. This thick cloud plays a key role in controlling the thermal structure and dynamics of the Venusian atmosphere. However, the mechanisms by which the cloud forms, grows, and is maintained over long timescales remain poorly understood. In this presentation, we will describe the bifurcation structure of droplet growth dynamics driven by the co-condensation of water and sulfuric acid, and we will discuss the conditions under which Venusian cloud particles can stably exist from the perspective of a saddle-node bifurcation. If time permits, we would also like to discuss the results of our investigation on how simulated temperature perturbations, representing atmospheric waves, affect the structure of Venusian clouds.

11. Immersion freezing in particle-based cloud microphysics models
Sylwester Arabas (AGH University of Kraków); Jeff Curtis, Matt West, Nicole Riemer (illinois.edu); Israel Silber (pnnl.gov); Ann Fridlind (nasa.gov); Daniel Knopf (stonybrook.edu)
Abstract:
The talk will present a probabilistic perspective on singular and time-dependent models of the immersion freezing process, which is one of the ways ice forms in clouds. Immersion freezing occurs when an ice nucleus immersed in a liquid water droplet triggers freezing. Without presence of the nuclei (e.g., grains of minerals), freezing requires a lower temperature. In a recent study (Arabas et al. 2025, JAMES, https://doi.org/10.1029/2024MS004770), we have focused on the recurrent question in this field of research, namely the role of time in the freezing process. Using super-droplet simulation and box- and prescribed-flow setups, we have investigated how the time-dependent and the time-independent parameterisations perform in idealized scenarios and in flow regimes relevant to ambient cloud conditions (the latter posing a challenge for singular models). In the presentation, I will demonstrate how to reproduce all of the presented simulations using Google Colab cloud-computing platform.

12. Applying a Radar Simulator to the Super-Droplet Method: Current Status with BIN-type Data
Yutaro Nirasawa¹, Manhal Alhilali², and Shin-ichiro Shima², 
1. Atmosphere and Ocean Research Institute, The University of Tokyo, Tokyo, Japan
2. Graduate School of Information Science, University of Hyogo, Kobe, Japan
Abstract: A persistent obstacle in model-observation evaluation is that radars observe electromagnetic scattering (e.g., Z, ZDR, Kdp), whereas models predict hydrometeor fields; radar simulators bridge this gap but are largely tailored to Eulerian bulk and bin microphysics. In contrast, a standard pathway from Lagrangian schemes, specifically the Super-Droplet Method (SDM), to simulator-ready inputs is still missing. We are developing such a pathway. Our approach aggregates super-droplets within each grid cell to form particle size distributions (and associated phase/habit metadata) that existing simulators can ingest, enabling comparison with radar data. This presentation reports the method design, implementation status, and preliminary results, and outlines next steps toward direct scattering from SDM ensembles (including attenuation and Doppler moments) to reduce mapping biases and sampling noise.

13. Multiscale inertial clustering of cloud droplets in high Reynolds number turbulence
Keigo Matsuda (JAMSTEC), Katsunori Yoshimatsu (Nagoya University), Kai Schneider (Aix-Marseille University)
Abstract:
Inertiual clustering of cloud droplets is one of important phenomena that promote the droplet collision in turbulence. To examine the clustering in high Reynolds number turbulence, such as cloud turbulence, three-dimensional direct numerical simulation is performed up to Reλ=648 and with 3.2×10^9 Lagrangian particles for several Stokes numbers. The number density spectra show that the slope at scales in the inertial subrange is dependent on the Stokes number, in contrast to past theoretical predictions. It is elucidated that the slope changes depending on the scale-dependent Stokes number Str, and the Str dependence is due to the modulation of clustering formation for number density conservation.

14. Homogeneous freezing in particle-based aerosol-cloud microphysics
Tim Lüttmer¹, Sylwester Arabas², Peter Spichtinger¹
1. Institute for Atmospheric Physics, Johannes Gutenberg University, Mainz, Germany
2. Environmental Physics Group, AGH University of Krakow, Krakow, Poland
Abstract:
We introduce an ice-phase extension of the PySDM Python package, which uses the super droplet method with Monte Carlo–type solvers to represent cloud microphysical processes including collisions, condensation, sedimentation, freezing, and vapour deposition on ice.
Our focus is on stochastic homogeneous freezing in two thermodynamic regimes. In the regime, of mixed-phase to pure ice-phase transition, we investigate the temperature threshold below which all pure water droplets freeze, and how it depends on the choice of nucleation rate, updraft speeds, and cloud processes such as vapour deposition and coalescence. At colder, subsaturated conditions (with respect to water) relevant for cirrus formation, we study the homogeneous freezing of solution droplets. Here we compare results with bulk-model approaches, and assess sensitivity to updraft speeds, initial temperature, and solution droplet sampling.

15. Bimodal raindrop size distributions with a bin microphysics mode,
Megumi Okazaki (Graduate School of Engineering, Kyoto University, Japan)
Eiichi Nakakita (Kyoto University, Japan)
Kentaroh Suzuki (Atmosphere and Ocean Research Institute, The University of Tokyo, Kashiwa, Japan)
Tomoro Yanase (Graduate School of Information Science, University of Hyogo, Kobe, Japan)
Yousuke Sato (Graduate School of Engineering, The University of Osaka, Suita, Japan)
Kosei Yamaguchi (Disaster Prevention Research Institute, Kyoto University, Uji, Japan)
Abstract:
The mechanism behind the bimodal raindrop size distribution (RDSD) remains unclear. Numerous studies have suggested that bimodal RDSDs form when collision-coalescence and breakup processes reach equilibrium. Conversely, observations using radar and rain gauges indicate that convective environments are necessary for their formation. A comprehensive understanding of bimodal RDSDs formation, encompassing both cloud dynamics and microphysics, is insufficient. This study approaches this subject through numerical simulations using the spectral bin method as a cloud microphysics parameterization. The formation of the bimodal RDSD was attributed to differences in advection processes generated by variations in particle fall velocities. Furthermore, the bimodal RDSD based on collision-coalescence and breakup processes and those based on advection processes could be classified according to their formation environments.

16. Two-Stage Freezing Model of Wet and Dry Growth in Riming for the Super-Droplet Method,
Hayatomo Ohashi and Shin-ichiro Shima
Graduate School of Information Science, University of Hyogo, Kobe, Japan
Abstract:
Shima et al. (2020) developed a model based on the super-droplet method (SDM) that explicitly predicts ice particle shape, but wet growth remains unimplemented. While the bin method determines critical temperature from riming rate, it cannot be applied to SDM because collisions are discrete. This study proposes a method to implement wet growth into SDM based on the theory of initial ice freezing. In addition, since the formulation of the melting/freezing process of ice particles with a liquid layer proposed by Mason (1956) is based on a spherical assumption, we extend it to an ellipsoidal particle to represent ice particle evolution more realistically.

17. Quantum Computing for Stochastic Cloud Representation,
Kazumasa Ueno and Hiroaki Miura
School of Science, The University of Tokyo, Tokyo, Japan
Abstract:
Quantum computing is emerging as a new tool for addressing computational challenges in scientific computing. In atmospheric research, it may help reduce the cost of simulating large and complex systems, though practical applications are still limited. This study investigates a quantum algorithm for the collision–coalescence process of cloud droplets, a key mechanism of particle growth in cloud microphysics. Building on methods from financial engineering, we formulate the problem with a master equation and use quantum amplitudes to encode droplet mass distributions. Expected droplet numbers are then obtained via amplitude estimation. A resource analysis shows that the number of T gates, which are basic operations in quantum computing, scales as O(N²) with respect to the number of mass bins. This represents a major improvement compared with the exponential scaling of classical methods. These results indicate that collision–coalescence is a promising candidate for quantum applications in the atmospheric science.

18. Toward a Mechanistic Understanding of the Ocean Biological Carbon Pump
Anna Rufas¹ and Samar Khatiwala²
1. Department of Earth Sciences, University of Oxford, Oxford, UK
2. School of International Liberal Studies, Waseda University, Tokyo, Japan
Abstract:
Photosynthetic production of organic matter by phytoplankton absorbs atmospheric carbon dioxide from the atmosphere. This organic matter subsequently aggregates into sinking particles that are consumed by microbes and zooplankton, remineralizing it back into CO2 which can stay dissolved in the ocean for centuries. These processes are collectively known as the biological carbon pump (BCP) and the depth to which the particles sink controls its “efficiency” in sequestering carbon in the ocean. However, despite its importance for the carbon cycle and climate, the BCP is poorly constrained by sparse observations, and current biogeochemical models embedded within the climate models used to predict future climate change do not have a mechanistic representation of the complex, small-scale processes that drive the BCP, often reducing them to one or two globally uniform parameters. The ability of climate models to respond to environmental changes and realistically project how the BCP will evolve in the future is therefore limited. Here, we present a novel mechanistic model – Stochastic Lagrangian Aggregate Model of Sinking particles (SLAMS) – that explicitly simulates and tracks the formation, interactions and transformations of a large number of biogenic particles in the BCP. Uniquely, the fundamental characteristics of the BCP, such as its sequestration efficiency, are “emergent” properties of this model and not simply prescribed through fixed parameterisation as in existing biogeochemical models. SLAMS uses numerical methods developed in the fields of astrophysics and cloud microphysics to make the computation tractable. We will briefly describe the architecture of SLAMS, preliminary results from a global-ocean simulation, and future plans for how SLAMS can be used to better understand the BCP in the present-day climate and its response to future climate change.