Aaron Wang

Forest canopies modulate turbulent fluxes to the atmospheric boundary layer, yet canopy heterogeneity is often underrepresented in coarse-grid atmospheric models. This study uses idealized large-eddy simulations with the Energy Research and Forecasting (ERF) model, informed by observations from Bankhead National Forest, to examine a hierarchy of configurations, from a homogeneous canopy without terrain to a realistically heterogeneous canopy with terrain. Neutral and stable conditions are considered, with the stable cases based on radiosonde profiles. Temporally and horizontally averaged wind profiles are similar between the homogeneous- and heterogeneous-canopy cases, whereas local turbulence structures are sensitive to canopy heterogeneity. The simulated turbulence statistics are comparable in magnitude to observations, with the closest overall agreement for the stable, real canopy, terrain case using the 10-min analysis window. Canopy heterogeneity and terrain modify shear stress, TKE, and heat flux, with responses depending on stability and location. The clearing exhibits higher wind speed and TKE and stronger downward flow into the canopy. Lagrangian particles are transported away from the canopy more efficiently with a realistic canopy because of vertical motions induced by canopy heterogeneity. Overall, canopy heterogeneity can substantially affect local turbulence structures and the mean vertical transport of passive particles. These results highlight the need for caution when comparing local observations with models that do not resolve canopy heterogeneity and suggest that neglecting canopy heterogeneity can reduce modeled vertical transport, although whether this effect extends to much coarser model resolutions requires further investigation.

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.

Droplet collision–coalescence is a crucial process in cloud physics, but accurately representing this process under different dynamical conditions remains challenging. A proposed future convective‐cloud chamber aims to investigate this key process, but the method for observing it remains unclear, even though it is theoretically established that collision‐coalescence will occur. This study serves as a proof‐of‐concept demonstration of how knowledge‐informed deep learning, combined with measurement data from local sensors in the chamber, can be used to estimate the collision kernels, which determine how the droplet size distribution evolves during collision‐coalescence. In addition to estimating the collision kernel, we also address wall fluxes, another uncertain but important process that acts as a source of heat and moisture in the chamber. Ensemble runs of large‐eddy simulations are conducted by scaling the wall fluxes and the collision kernel, while the measured flow and cloud properties are used as inputs for a neural network. Results indicate that this approach successfully maps the scaling of wall fluxes and the collision kernel with biases of approximately 1% or less relative to the range of the target data. This proof‐of‐concept lays the groundwork for future applications; when the real measurements are available, real sensor data combined with the trained model presented in this work will enable estimation of the actual wall fluxes and collision kernel.

Stratocumulus-topped boundary layers play a crucial role in influencing daily weather and earth energy balance. Entrainment at the stratocumulus cloud top affects the cloud’s lifetime, precipitation, and radiative properties, but our understanding remains limited due to the lack of resolution in both field observations and numerical simulations. A recently proposed convection-cloud chamber with detailed control of sidewall temperatures can provide a unique opportunity to explore this mechanism in a laboratory setting. In this work, we use numerical simulations to demonstrate that this design can produce a cloud top that mimics the entrainment interfacial layer in a stratocumulus cloud. Our results show that a steady-state cloud can be formed by cooling the lower portions of the sidewalls and warming the bottom surface, while a temperature inversion at the cloud top can be generated by keeping the upper sidewalls and top surface warmer than the bottom. The turbulent kinetic energy profile and budget are similar to those found in a convective boundary layer, and inhomogeneous mixing near the cloud top can be observed. These findings significantly enhance the scientific value of constructing the tall convection-cloud chamber.

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.

A large convection cloud chamber has been proposed for exploring aerosol–cloud–drizzle interactions under well‐controlled turbulent conditions. Recent theoretical and numerical studies suggest that a convection cloud chamber with two heated and two cooled sidewalls can significantly enhance the liquid water content and thus benefit drizzle initiation. However, a chamber with such a sidewall configuration develops stable stratification and extremely weak turbulence therein. In this study, we conduct large‐eddy simulations of a tall convection chamber with five different sidewall configurations consisting of alternating warm and cold patches. For each configuration, the total surface area of warm patches equals that of cold patches, resulting in the same expected cloud‐free supersaturation based on a flux budget model. Results show that changing the sidewall configuration, while keeping all other factors constant, can substantially enhance turbulent mixing and improve the uniformity of thermodynamic and cloud microphysical properties in the bulk region of the chamber. In addition, turbulence strength is positively correlated with liquid water content and negatively correlated with cloud droplet number concentration, consistent with theoretical predictions. Our results highlight the advantage of building a large cloud chamber using modular patches with individually controllable temperature and humidity to achieve well‐mixed conditions.

Droplet collision-coalescence is a crucial yet poorly understood process in cloud physics. A proposed future convective-cloud chamber aims to investigate this key process, but the method for observing it remains unclear, even though it is theoretically established that collision-coalescence will occur. This study serves as a proof-of-concept demonstration of how knowledge-informed deep learning, combined with measurement data from local sensors in the chamber, can be used to estimate the collision kernels, which determine how the droplet size distribution evolves during collision-coalescence. In addition to estimating the collision kernel, we also address wall flux, another poorly understood but important process that acts as the source of heat and moisture in the chamber. Ensemble runs of large-eddy simulations are conducted by scaling the wall flux and the collision kernel, while the measured flow and cloud properties are used as inputs for the neural network. Results indicate that this approach successfully maps the scaling of wall flux and the collision kernel with biases of approximately 1\% or less relative to the range of the target data. This proof-of-concept lays the groundwork for future applications; when the real measurements are available, real sensor data combined with the trained model presented in this work will enable estimation of the actual wall flux and collision kernel.

Thermal convection in a closed chamber is driven by a warm bottom, a cold top, and side walls at various temperatures. Although wall fluxes are the source of convection energy, accurately modeling these fluxes (i.e., the wall model) is challenging. In large-eddy simulations (LESs), many wall models are traditionally derived from the canonical boundary layer, which may be unsuitable for thermal convection bounded by both horizontal and vertical walls. This study conducts a model intercomparison of dry convection in a cubic-meter chamber using three direct numerical simulations (DNSs) and four LESs with different wall models. The LESs employ traditional wall models, a new wall model employing physics-aware neural networks, and a refined grid near the walls. The experiment involves four cases with varying sidewall temperatures. Our results show that LESs capture the main flow features and the trends of mean fluxes. The physics-aware neural networks and refined wall grids can improve the temporally averaged local fluxes when the large-scale circulation has a preferred direction. Even without the local improvement of wall fluxes, the LES flow quantities (temperature and velocities) can still largely match those in DNSs, provided the mean flux largely matches the DNSs. Additionally, DNSs reveal that a variation in corner treatments has minimal impacts on the flow quantities away from corners. Finally, LESs underestimate the mean fluxes of the entire wall due to their inability to resolve corner regions, but their mean flux away from the corner can better match DNS.

It is known that aqueous haze particles can be activated into cloud droplets in a supersaturated environment. However, haze–cloud interactions have not been fully explored, partly because haze particles are not represented in most cloud-resolving models. Here, we conduct a series of large-eddy simulations (LESs) of a cloud in a convection chamber using a haze-capable Eulerian-based bin microphysics scheme to explore haze–cloud interactions over a wide range of aerosol injection rates. Results show that the cloud is in a slow microphysics regime at low aerosol injection rates, where the cloud responds slowly to an environmental change and droplet deactivation is negligible. The cloud is in a fast microphysics regime at moderate aerosol injection rates, where the cloud responds quickly to an environmental change and haze–cloud interactions are important. More interestingly, two more microphysics regimes are observed at high aerosol injection rates due to haze–cloud interactions. Cloud oscillation is driven by the oscillation of the mean supersaturation around the critical supersaturation of aerosol due to haze–cloud interactions. Cloud collapse happens under weaker forcing of supersaturation where the chamber transfers cloud droplets to haze particles efficiently, leading to a significant decrease (collapse) in cloud droplet number concentration. One special case of cloud collapse is the haze-only regime. It occurs at extremely high aerosol injection rates, where droplet activation is inhibited, and the sedimentation of haze particles is balanced by the aerosol injection rate. Our results suggest that haze particles and their interactions with cloud droplets should be considered, especially in polluted conditions.