Sisi Chen

Supercooled liquid water often persists in mixed‐phase clouds for hours to days, even though a simple, well‐mixed Wegener‐Bergeron‐Findeisen model predicts rapid depletion of liquid water in the presence of ice. The persistence is well documented, but the relative importance of the maintaining mechanisms remains uncertain. This model intercomparison study addresses this issue by comparing results from 10 model configurations against observations from the Pi Cloud Chamber at Michigan Technological University, which produces cloudy Rayleigh‐Bénard convection. To simplify the problem, we exclude ice‐nucleating particle processes and assume that the ice crystals are spherical. Each model was first tuned to reproduce the observed steady liquid‐cloud state and then run with ice injected at a range of rates. The evolution of spherical ice crystals is consistent across models, but differences emerge in the liquid phase. Most models that simulate the full chamber tend to retain liquid droplets for the entire simulation, because persistent supersaturation near the bottom activates new droplets that replace those that mix into the core and evaporate. In contrast, models that assume a well‐mixed domain, exclude the near‐wall region, or use coarse resolution do not sustain droplets and undergo complete glaciation. Models that use Lagrangian microphysics expose particles to greater supersaturation variability than bin schemes. Overall, these results demonstrate that liquid persistence in mixed‐phase clouds is highly sensitive to the representation of spatial heterogeneity and mixing, and provide guidance for improving microphysical parameterizations in atmospheric models.

This study presents a liquid propane (LP) seeding parameterization in the Weather Research and Forecasting (WRF) model. Two formulas derived from laboratory experiments express ice production as a function of temperature and LP release rate. The seeding impacts on clouds and precipitation are evaluated through idealized two-dimensional simulations spanning two mountain heights, four environmental soundings, and four seeding scenarios. The simulations reveal an environment-dependent microphysical response in which the ice conversion process near the seeding site varies with the natural rain efficiency, from a snow-dominated regime where riming is limited to a riming-dominated regime in efficient-rain conditions. The largest enhancement occurs in the low mountain, where supercooled liquid water (SLW) persists and natural precipitation remains weak. Seeding impacts in the high mountain are weaker, with a mild reduction in total precipitation in the case where natural rain process is the most efficient. Snow enhancement dominates the net total precipitation increase. Compared with AgI seeding in prior idealized studies, LP is weaker in both magnitude and spatial extent because LP-generated ice requires a continuous SLW cloud layer for dispersion from the surface to clouds. Nevertheless, LP is effective at temperatures warmer than -6 °C where AgI is less active, suggesting complementary roles of the two seeding agents. These simulations provide a physical basis for understanding LP seeding responses and for future three-dimensional real-case simulations, field evaluation, and direct comparison with AgI seeding simulations.

This study presents a liquid propane (LP) seeding parameterization in the Weather Research and Forecasting (WRF) model. Two formulas derived from laboratory experiments express ice production as a function of temperature and LP release rate. The seeding impacts on clouds and precipitation are evaluated through idealized two-dimensional simulations spanning two mountain heights, four environmental soundings, and four seeding scenarios. The simulations reveal an environment-dependent microphysical response in which the ice conversion process near the seeding site varies with the natural rain efficiency, from a snow-dominated regime where riming is limited to a riming-dominated regime in efficient-rain conditions. The largest enhancement occurs in the low mountain, where supercooled liquid water (SLW) persists and natural precipitation remains weak. Seeding impacts in the high mountain are weaker, with a mild reduction in total precipitation in the case where natural rain process is the most efficient. Snow enhancement dominates the net total precipitation increase. Compared with AgI seeding in prior idealized studies, LP is weaker in both magnitude and spatial extent because LP-generated ice requires a continuous SLW cloud layer for dispersion from the surface to clouds. Nevertheless, LP is effective at temperatures warmer than -6 °C where AgI is less active, suggesting complementary roles of the two seeding agents. These simulations provide a physical basis for understanding LP seeding responses and for future three-dimensional real-case simulations, field evaluation, and direct comparison with AgI seeding simulations.

This study presents a liquid propane (LP) seeding parameterization in the Weather Research and Forecasting (WRF) model. Two formulas derived from laboratory experiments express ice production as a function of temperature and LP release rate. The seeding impacts on clouds and precipitation are evaluated through idealized two-dimensional simulations spanning two mountain heights, four environmental soundings, and four seeding scenarios. The simulations reveal an environment-dependent microphysical response in which the ice conversion process near the seeding site varies with the natural rain efficiency, from a snow-dominated regime where riming is limited to a riming-dominated regime in efficient-rain conditions. The largest enhancement occurs in the low mountain, where supercooled liquid water (SLW) persists and natural precipitation remains weak. Seeding impacts in the high mountain are weaker, with a mild reduction in total precipitation in the case where natural rain process is the most efficient. Snow enhancement dominates the net total precipitation increase. Compared with AgI seeding in prior idealized studies, LP is weaker in both magnitude and spatial extent because LP-generated ice requires a continuous SLW cloud layer for dispersion from the surface to clouds. Nevertheless, LP is effective at temperatures warmer than -6 °C where AgI is less active, suggesting complementary roles of the two seeding agents. These simulations provide a physical basis for understanding LP seeding responses and for future three-dimensional real-case simulations, field evaluation, and direct comparison with AgI seeding simulations.

The potential for cloud seeding to induce dynamic changes that alter cloud structure beyond basic ice formation processes has remained theoretical. While previous research hypothesized the presence of dynamic responses in seeded clouds, this study presents the first direct observational evidence that seeding can generate buoyant forces strong enough to deepen and deform clouds. In situ aircraft measurements and W‐band radar analysis shows how the buoyant force increased cloud tops by hundreds of meters and induced secondary circulations that altered the cloud and precipitation structure. These dynamic changes triggered additional ice formation and precipitation not captured in current conceptual models. The results demonstrate that dynamic responses can be induced through glaciogenic seeding, representing foundational research that will significantly improve understanding of seeding mechanisms and precipitation formation in commonly seeded clouds.

Cloud droplet temperature plays a key role in fundamental cloud microphysical and radiative processes. The supercooled droplet temperature and lifetime can impact cloud ice and precipitation formation via homogeneous freezing and activation of ice-nucleating particles through contact and immersion freezing. While most observational and modeling studies often assume droplet temperature to be spatially uniform and equal to the ambient temperature (Ta), this assumption may not always be valid, particularly when droplets experience strong relative humidity (RH) gradients at cloud boundaries.For a wide range of ambient conditions, we model the coupled heat and mass transfer between the droplet and its environment and quantify the decrease in droplet temperature (ΔT) from that of the far-away ambient temperature (Ta), and the increase in droplet lifetime due to reduced droplet surface temperatures, compared to Maxwellian diffusion-limited evaporation estimates. ΔT is found to increase with Ta, and decrease with increase in ambient relative humidity (RH), and pressure (P). For a prescribed environment and assuming the droplet has infinite thermal heat conductivity, ΔT was typically 1-5°C lower than Ta, with highest values (~10.3°C) for very low RH, low P, and Ta closer to 0ºC. For higher RH and larger droplets, droplet lifetimes can increase by more than 100s compared to the diffusion-limited evaporation approach, which ignores droplet cooling. The steady state temperature of evaporating droplets can be approximated by environmental thermodynamic wet-bulb temperature. Radiation was found to play a minor role in influencing droplet temperatures, except for larger droplets in environments close to saturation. If we resolve the spatiotemporally varying thermal and vapor density gradients near the evaporating droplet, results demonstrate a higher subsaturation-dependent decrease in the droplet temperature as well as the envelope of air in the vicinity of the droplet surface. For an ambient environment specified far away, with Ta = -5°C, RH = 10%, 40%, and 70%, the decrease in droplet temperatures due to evaporative cooling is ~ 24, 11, and 5°C, respectively and the evaporatively cooled droplets survive longer compared to previous estimates. The implications of evaporative cooling and increased lifetimes of supercooled cloud droplets on potential enhancement of ice nucleation near evaporating cloud edges, such as cloud-top generating cells, and especially for moderately supercooled ambient temperatures, are discussed. The importance of using accurate droplet temperatures to improve activated ice nuclei number concentrations from existing primary ice nucleation parameterization schemes, especially in sub-saturated environments, is highlighted. Finally, using high-resolution direct numerical simulations of moderately supercooled cloud boundaries, we discuss the impacts of droplet evaporative cooling on the evolution of supercooled droplet size distributions, which critically impacts ice nucleation.

Observations from recent field campaigns investigating glaciogenic cloud seeding demonstrate the process of silver iodide (AgI) dispersion through ice nucleation, crystal growth, then enhanced snowfall at the surface. These observations, combined with numerical simulations, were used to quantify seeding’s impact on enhancing precipitation in targeted regions. With the microphysical chain of events established, fundamental knowledge gaps remain on the mechanisms by which seeding modifies the cloud dynamics, structure, and precipitation enhancement. This study presents the first direct observational evidence that glaciogenic seeding generates buoyant forces in wintertime orographic clouds that elevate cloud tops and secondary circulations that alter the cloud structure. In this study, we analyze dynamic responses induced from seeding in the Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE) and CLOUDLAB field campaigns. The SNOWIE cases occurred in the Payette mountains in presence of widespread supercooled liquid conditions and low natural ice number concentrations. Ground-based X-band radars tracked the development and evolution of cloud and precipitation from five seeding legs. Distinct cells, directly attributable to airborne seeding, developed from smaller weaker echoes (10 dBZ) at the natural cloud top and rapidly intensified to produce precipitation with echoes >30 dBZ. The key observed processes were dynamic responses induced by the latent heat released from seeding that led to enhancing cloud top by 350 m compared to the natural cloud. An airborne W-band Dual-Doppler cross-section illustrates the detailed dynamic structure for one cell consisting of a central updraft, divergence near cloud top, and toroidal circulations along its periphery in an observed moist-neutral environment. In situ measurements show distinct microphysical regimes in the elevated cloud top, with seeding generated ice number concentrations up to 580 L-1. A WRF-WxMod ensemble shows the evolution of dynamic responses, the microphysical characteristics, and precipitation enhancement up to 200 km downwind of release. We combine these results with preliminary observations from the 2025-2026 CLOUDLAB field campaign that further investigate the roles each step in a dynamic response has on seeded cloud microphysical properties. We show the evolution of seeded cloud from Ka-band cloud radars, combined with in-situ measurements from a holographic imager, to show dynamic response impact on microphysical structure and cloud properties.

Previous studies have noted that cities enhance cloud cover, but the mechanisms of urban morphological types on cloud formation remain elusive. Observations of cloud climatology from 44 major U.S. cities show that cloud enhancement increases with the street-canyon aspect ratio and decreases with building density. Here, to explain these observations, we conducted numerical experiments using urban morphology-resolving large-eddy simulations. In these simulations, urban and rural surfaces retain their respective heat-flux differences, while the moisture sources and background atmospheric water vapor are prescribed to be identical, allowing us to isolate the morphological controls on moist convection. Results show that urban morphology influences cloud formation through two mechanisms: taller buildings intensify urban-breeze circulations at the urban-rural interface, while denser buildings, acting as momentum sinks, reduce vertical turbulent transport at the urban core. These vertical motions modify the transport of moisture in the urban atmospheric boundary layer, causing different cloud amounts across different urban morphology. This study highlights the mechanistic link between urban form, vertical motions, and cloud enhancement, thus providing a basis for city-specific boundary-layer convective parameterizations in large-scale weather and climate models.

Mixed-phase clouds, composed of supercooled liquid droplets and ice crystals, play a critical role in weather and climate systems. Their complex microphysical interactions and coupling with turbulence at microscales govern the cloud properties at macroscales, yet remain challenging to observe and quantify under atmospheric conditions. This model intercomparison study utilizes ten model configurations to simulate mixed-phase cloud evolution in the Michigan Technological University's Pi Chamber. The models span a range of frameworks, including box models, direct numerical simulation, and large-eddy simulation models, and incorporate both bin and Lagrangian microphysics. Each model was tuned to reproduce the observed liquid-phase steady state prior to ice injection. Ice particles were then introduced into the domain at various rates to examine cloud glaciation behavior. By the intercomparison design, all models successfully reproduced the observed mean droplet radius and number concentration during the liquid-phase stage. Increasing ice particle injection rates led to consistent qualitative trends across models: depletion of liquid water, reduced total water content, and a shift in particle size distributions toward larger radii. However, quantitative differences arose due to variations in model treatment in dynamics and microphysics, including subgrid-scale turbulence parameterizations, wall forcing, and particle removal parameterizations. Most models that simulate the full chamber retained liquid droplets near the lower boundary, where supersaturation forcing is strongest and droplets are replenished before mixing into the core region. These surviving liquids droplets were absent in simulations assuming a well-mixed domain, excluding the near-wall region, or using coarse grid spacing.

This study presents the first model intercomparison of aerosol‐cloud‐turbulence interactions in a controlled cloudy Rayleigh‐Bénard Convection chamber environment, utilizing the Pi Chamber at Michigan Technological University. We analyzed simulated cloud chamber‐averaged statistics of microphysics and thermodynamics in a warm‐phase, cloudy environment under steady‐state conditions at varying aerosol injection rates. Simulation results from seven distinct models (DNS, LES, and a 1D turbulence model) were compared. Our findings demonstrate that while all models qualitatively capture observed trends in droplet number concentration, mean radius, and droplet size distributions at both high and low aerosol injection rates, significant quantitative differences were observed. Notably, droplet number concentrations varied by over two orders of magnitude between models for the same injection rates, indicating sensitivities to the model treatments in droplet activation and removal and wall fluxes. Furthermore, inconsistencies in vertical relative humidity profiles and in achieving steady‐state liquid water content suggest the need for further investigation into the mechanisms driving these variations. Despite these discrepancies, the models generally reproduced consistent power‐law relationships between the microphysical variables. This model intercomparison underscores the importance of controlled cloud chamber experiments for validating and improving cloud microphysical parameterizations. Recommendations for future modeling studies are also highlighted, including constraining wall conditions and processes, investigating droplet/aerosol removal (including sidewall losses), and conducting simplified experiments to isolate specific processes contributing to model divergence and reduce model uncertainties.