Session at the AMS Annual Meeting in Baltimore

Probabilistic Particle-Based Methods in Aerosol-Cloud Microphysics Modeling

(Jointly within the 16th Symposium on Aerosol-Cloud-Climate Interactions and the First Symposium on Cloud Physics)

During the 104th AMS Annual Meeting held from 28 January to 1 February 2024 in Baltimore, Maryland, at the Baltimore Convention Center, Shima co-chaired a joint session on Probabilistic Particle-Based Methods in Aerosol-Cloud Microphysics Modeling. This session showcased a  lineup of 12 presentations, including two distinguished invited speakers and 10 participants who shared their work. Among the presenters were our lab members: Manhal Alhilali, Anu Gupta, Clara Bayley, and Ruyi Zhang.

About the session

  • Date:       Monday, January 29, 2024 and Tuesday, January 30, 2024
  • Time:      8:30 AM – 10:00 AM
  • Venue:   329 (The Baltimore Convention Center)


Particle-based microphysics models differ significantly from the conventional computational fluid dynamics approaches of representing aerosol, cloud and precipitation particle populations as separate categories of trace constituents modeled with continuous density fields (so-called bulk or bin models). The particle-based microphysics modeling techniques are also termed super-particle or super-droplet methods, because the simulation particles (or SIPs) represent a significant multiplicity of simulated aerosol/cloud/precipitation particles. The super-particles constitute a coarse-grained view of the aerosol particles, droplets and hydrometeors in both real and attribute space. The probabilistic aspects of the particle-based techniques are multifaceted and, depending on the model formulation, refer to randomly populating the phase space with super-particles and/or Monte-Carlo type representation of stochastic-in-nature processes such as coagulation, nucleation or small-scale motions. The aim of this session is to spin-up interactions among, on the one hand, research groups working on the development of particle-based aerosol/cloud simulation tools and, on the other hand, wider modeling community as well as experimentalists performing measurements relevant to model formulation and validation. We thus welcome contributions highlighting applications of the particle-based methodology in both aerosol-cloud interactions research and neighboring domains, as well as pitches for tackling open problems with particle-based methods. Session topics include (but are not limited to): (i) applications of particle-based microphysics models; (ii) comparisons with bulk and bin simulations; (iii) performance and scaling analyses including CPU/GPU usage strategies; (iv) flow-particle coupling aspects including subgrid scale motions; (v) phase-space sampling strategies; (vi) Monte-Carlo algorithms for particle-resolved models; (vii) coupling radiative-transfer models with particle-based microphysics; (viii) aerosol mixing state and biochemical properties in cloud simulations; (ix) mixed-phase particle-based models; (x) model validation against ambient and laboratory measurements.


Sylwester Arabas 

AGH Univ.
Kraków, Poland

Shin-ichiro Shima 

Univ. of Hyogo
Kobe, Hyogo, Japan

David Richter 

Univ. of Notre Dame
Notre Dame, IN, USA

Nicole Riemer 

Univ. of Illinois Urbana-Champaign
Urbana, IL, USA

Emily Katherine de Jong 

California Institute of Technology
Pasadena, CA, USA


Are Turbulence Effects on Droplet Collision-Coalescence a Key to Understanding Observed Rain Formation in Clouds? (Invited Presentation)

Kamal Kant Chandrakar, PhD, NCAR, Boulder, CO; and H. Morrison, W. W. Grabowski, and P. Lawson

Assessing Aerosol CCN Structural Uncertainty With a Regional Particle-resolved Model

Jeffrey Henry Curtis, Univ. of Illinois at Urbana-Champaign, Urbana, IL; and N. Riemer and M. West

A particle-based microphysics study of isotope exchanges in a single-column rain-shaft model

Sylwester Arabas, AGH Univ., Kraków, Poland; and K. Różański

Understanding the Fate of Spray, Aerosols, and Fluxes in the Tropical Cyclone Boundary Layer Via a Lagrangian Superdroplet Method

David H. Richter, Univ. of Notre Dame, Notre Dame, IN; and G. H. Bryan, J. Sun, J. Dennis, S. Mickelson, and C. Wainwright

Spectrum of Supersaturation Fluctuations and Lagrangian-Eulerian Computation

Toshiyuki Gotoh, Nagoya Institute of Technology, Nagoya, Japan; and I. Saito and T. Watanabe

The Small-Alpha Method: Sublinear Sampling for Enhanced Superdroplet Resolution in Lagrangian Cloud Models

Emma Ware, Univ. of California, Davis, Davis, CA; Univ. of California Davis, Davis, CA; and O. Sturm and A. L. Igel

Exploring the Role of Fragmentation of Ice Particles by Combining Super-Particle Modeling, Laboratory Studies, and Polarimetric Radar Observations (Invited Presentation)

Leonie von Terzi, Ludwig-Maximillians-Univ. in Munich, München, Germany; and S. Kneifel, A. Seifert, C. Siewert, S. Wald, M. Szakall, S. Yadav, and P. Grzegorczyk

Investigation of Coastal Orographic Snow Clouds Microphysics with the Super-Droplet Method

Anu Gupta, Univ. of Hyogo, Kobe, Japan; and R. Taniguchi, S. I. Shima, and A. Hashimoto

Evaluation of the Effect of Electro-Coalescence and Anti-Electro-Coalescence with Conducting Sphere Approximation on the Formation of Warm Cumulus Cloud and Stratocumulus Cloud

Ruyi Zhang, East China Normal Univ., Shanghai, China; and L. Zhou and S. I. Shima

Particle-Resolved Simulation of Immersion Freezing with Multi-Species Ice-Nucleating Particles

Wenhan Tang, Univ. of Illinois at Urbana-Champaign, Urbana, IL; and N. Riemer, J. H. Curtis, M. West, S. Arabas, and D. A. Knopf

Introducing CLEO: A New Superdroplet Model with Collisional Breakup

Clara Bayley, MPI for Meteorology, Hamburg, Germany; and T. Kölling, A. K. Naumann, R. Vogel, S. I. Shima, and B. Stevens

Evaluation of the Super-Droplet Method for Enhanced Cloud Microphysics Simulations of Deep Convective Clouds

Manhal Alhilali, Univ. of Hyogo, Kobe, Japan; and S. I. Shima, S. Samanta, and T. Prabhakaran

Highlights from the Session

The joint session on Probabilistic Particle-Based Methods in Aerosol-Cloud Microphysics Modeling at the 104th AMS Annual Meeting unveiled groundbreaking research in atmospheric science, focusing on the complexities of aerosol-cloud interactions and their implications for weather and climate modeling. Here’s a summary of key presentations:

  • Turbulence Effects on Rain Formation: Kamal Kant Chandrakar and team from NCAR presented compelling evidence on how turbulence significantly impacts rain formation in cumulus congestus clouds. Their study, contrasting high-resolution observations with large-eddy simulations, underscored the importance of turbulence in accurately representing raindrop size distributions and rain initiation, challenging previous assumptions about the role of large aerosols.
  • Aerosol CCN Structural Uncertainty: Jeffrey Henry Curtis and colleagues introduced a novel approach to assessing aerosol-cloud condensation nuclei (CCN) interactions using a particle-resolved model coupled with the WRF model. Their findings highlighted the critical role of aerosol mixing state in determining cloud microphysical properties, offering insights into previously unquantified structural uncertainties in atmospheric models.
  • Isotope Exchanges in Rain-Shaft Modeling: Sylwester Arabas and K. Różański revisited isotopic fractionation in rain shafts through a particle-based microphysics lens. Their innovative application of the PySDM framework to study water isotopic composition offers a fresh perspective on understanding moisture profiles in atmospheric models.
  • Spray, Aerosols, and Fluxes in Tropical Cyclones: David H. Richter’s exploration of sea spray’s role in tropical cyclone intensity via a Lagrangian superdroplet method revealed counterintuitive effects on air-sea fluxes. This direct approach sheds new light on the complex interactions at play in high-wind marine environments.
  • Supersaturation Fluctuations and Cloud Microphysics: Toshiyuki Gotoh’s analysis of supersaturation variance spectrum offers theoretical and numerical insights into droplet-water vapor interactions. This work is pivotal for understanding cloud microphysics and enhancing model predictions.
  • Enhanced Superdroplet Resolution in Cloud Models: Emma Ware’s study on the small-alpha method presents a promising technique for improving superdroplet sampling in Lagrangian cloud models. This approach holds potential for reducing computational demands while maintaining high fidelity in collision-coalescence processes.
  • Fragmentation of Ice Particles: Leonie von Terzi combined super-particle modeling with radar observations and laboratory studies to examine ice particle fragmentation. This interdisciplinary approach provides valuable clues to secondary ice processes, crucial for refining weather prediction models.
  • Coastal Orographic Snow Cloud Microphysics: Anu Gupta’s investigation into snow cloud microphysics using the Super-Droplet Method (SDM) highlighted the influence of riming ratios and aerosol species on snowfall patterns. This study offers a detailed analysis of snow cloud dynamics, contributing to our understanding of precipitation processes.
  • Electro-Coalescence in Cloud Formation: Ruyi Zhang’s evaluation of electro-coalescence effects using the Conducting Sphere method in cumulus and stratocumulus clouds presents a novel perspective on cloud microphysics. Their findings underscore the significance of electrostatic forces in cloud formation and precipitation enhancement.
  • Immersion Freezing and Aerosol Mixing State: Wenhan Tang and team’s particle-resolved simulation of immersion freezing illuminated the impact of aerosol mixing state on ice nucleation. This research advances our comprehension of ice crystal formation in clouds, bridging gaps in current atmospheric models.
  • CLEO: Collisional Breakup in Cloud Microphysics: Clara Bayley introduced CLEO, a superdroplet model focusing on collisional breakup. This innovative model challenges existing paradigms and offers new insights into raindrop formation dynamics.
  • Enhanced Cloud Microphysics Simulations: Manhal Alhilali’s work on improving the Super-Droplet Method for deep convective cloud simulations demonstrated the method’s efficacy in capturing complex cloud processes. This study validates the SDM’s potential for precise weather forecasting and understanding aerosol-cloud interactions.

These presentations not only showcased the latest advancements in probabilistic particle-based methods but also set the stage for future research directions in aerosol-cloud microphysics modeling. The session, was a testament to the collaborative efforts in pushing the boundaries of atmospheric science.

Networking and Collaboration: The Super-Droplet Dinner

In addition to the enlightening sessions and presentations, the spirit of collaboration and community was further celebrated through a special dinner organized by session cochair, Sylwester Arabas. Continuing the cherished tradition of AMS super-droplet outings, this year’s gathering took place at the Hard Rock Cafe, located within the iconic Power Plant Building at the Inner Harbor, a mere 10-minute walk from the conference venue. The event was held on Tuesday at 8 pm, offering participants a relaxed atmosphere to unwind after a day of intensive discussions.

This dinner provided an invaluable opportunity for attendees to foster deeper connections, discuss cutting-edge research in a more informal setting, and share insights and experiences beyond the confines of the conference halls. The gathering underscored the importance of community and informal networking in the scientific process, allowing for the exchange of ideas and the formation of collaborations in a more personal and engaging environment. It was a testament to the conference’s role not only as a forum for presenting research but also as a catalyst for building relationships and sparking innovative collaborations in the field of atmospheric sciences.

Concluding Thoughts

The session highlighted the cutting-edge advancements and persistent challenges in modeling the intricate interactions within cloud microphysics. The inclusion of diverse studies—from the impact of turbulence on rain formation to the nuanced role of aerosols in cloud development—underscored the conference’s pivotal role in advancing our understanding of atmospheric processes.

Looking forward, the insights and discussions sparked at this year’s AMS meeting are set to influence future trends in cloud modeling. The probabilistic particle-based methods and the exploration of new modeling techniques address critical gaps in our understanding and simulation capabilities. These advancements are likely to shape research directions, inform policymaking, and enhance predictive models, tackling some of the most pressing challenges in weather forecasting and climate science.

As we reflect on the success and insights garnered from this year’s meeting, we encourage all attendees and the wider community to remain engaged with the evolving dialogue in our field. The Particle-Based Cloud Modelling Network ( serves as an excellent resource for staying connected with the latest research developments, upcoming events, and opportunities for collaboration.