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DOE OSTI2025

Tropical Cyclone Super Resolution using conditional diffusion denoising probabilistic model from mesoscale simulation to LES

Accurate modeling of tropical cyclone wind fields is essential for the design, risk assessment, and operational planning of offshore energy infrastructure. While mesoscale simulations are widely used thanks to their computational efficiency, they lack the necessary resolution to capture key features such as wind shear and veer profiles as well as the distribution turbulent kinetic energy (TKE). High-fidelity large-eddy simulation (LES) models on the other hand, can resolve turbulent structures and provide a more accurate representation of the complex wind field, albeit at a higher computational cost. To address this modeling gap, we introduce a two-part generative framework to enhance the resolution and physics-capturing ability of mesoscale simulations. First, a reduced-order model based on Karhunen–Loève (KL) decomposition is used to extract dominant spatial modes from one-dimensional mean wind profiles. A multilayer perceptron (MLP) is trained to map mesoscale mode weights to their LES counterparts, enabling accurate reconstruction of vertical velocity profiles. Second, a conditional Diffusion Denoising Probabilistic Model (DDPM) is developed to super-resolve coarse and low-fidelity mesoscale velocity fields, recovering fine-scale turbulence structures and stress distributions. The framework is evaluated across different tropical cyclone intensity categories defined by the Saffir–Simpson scale and demonstrates strong performance in both interpolation and extrapolation tasks. The generated fields accurately reproduce spatial coherence, stress distributions, and spectral energy characteristics observed in LES data. By bridging the fidelity gap between mesoscale and LES outputs, this approach offers a scalable, data-driven solution for enhancing the representation of tropical cyclone wind fields, enabling more robust offshore energy infrastructure systems design in tropical-cyclone-prone areas.

17 WIND ENERGY↗

DOE OSTI2026

Evaluating the Collision‐Coalescence Process in Idealized Cloud Convection Using Large‐Eddy Simulations With Lagrangian Microphysics

Drizzle initiation through the collision and coalescence of cloud droplets plays a crucial role in warm cloud precipitation. Recent theoretical studies suggest that the influence of collisional growth on the droplet size distribution can be quantified by a non-dimensional drizzle number (Dz). Here, large-eddy simulations with Lagrangian microphysics are employed to evaluate the theory by simulating a tall convection-cloud chamber under various conditions. Results show that the smaller the Dz, the larger the impact of collisions on the right tail of the droplet size distribution, consistent with the theory. The simulations confirm that the collision rate can be estimated from the droplet size distribution interacting only with cloud droplets of the same size at the mode radius. This suggests that the idealized theory can be a useful tool to design a cloud chamber for drizzle investigation, as well as to represent drizzle formation in models of real atmospheric clouds.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Convective Biases in the US DOE Global Storm‐Resolving Model: Insights From Regionally Refined Simulations During the CACTI Campaign

Accurately simulating convective processes in complex terrain remains a critical challenge for global storm-resolving models (GSRMs). This study systematically evaluates moist convective biases in the Regionally Refined Mesh configuration of the U.S. Department of Energy Simple Cloud-Resolving E3SM Atmosphere Model (RRM-SCREAM) using comprehensive observations and large-eddy simulations from the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) campaign in the mountainous area of central Argentina. Comparisons of simulations with high-resolution observations and reanalysis data indicate that RRM-SCREAM effectively captures large-scale meteorological patterns, including regional atmospheric gradients and diurnal variability. However, RRM-SCREAM disproportionately produces smaller precipitation clusters referred to as “popcorn convection,” and exaggerated rainfall intensities compared to observations and reference models. Detailed examination of a representative orographic shallow-to-deep convective transition case shows that RRM-SCREAM delays initial shallow convection growth due to lower-tropospheric dryness and sustained convective inhibition, but once triggered, deep convection becomes overly vigorous with excessively strong vertical velocities and elevated cloud ice content, linked to a thermodynamic structure characterized by suppressed low-level moistening and excessive upper-level moisture retention. Our results highlight specific deficiencies in the model representation of convective vertical velocity, cloud microphysical processes, and convective precipitation organization within RRM-SCREAM. Addressing these biases is essential for improving the predictions of convective clouds and precipitation in the global high-resolution atmospheric models.

Su, Tianning [Lawrence Livermore National Laborato↗

DOE OSTI2026

Slow Wake Recovery and Low Turbulence Behind Wind Farms Parameterized in Mesoscale Simulations

Numerical weather prediction (NWP) and climate models equipped with wind-farm parameterizations (WFPs) can simulate cluster wake effects affecting downstream wind farms in both onshore and offshore environments. This study evaluates wake recovery behind a wind farm represented by the NWP-WFP approach in the Weather Research and Forecasting (WRF) model using either the Fitch et al. (2012) or Ma et al. (2022a, b) WFPs. Results are benchmarked against large-eddy simulations (LES) of an idealized offshore wind farm with aligned and staggered layouts under neutral atmospheric stability. Near-farm wake recovery is underestimated in NWP-WFP simulations due to its representation on a coarse mesoscale grid. This limitation leads to slow wake recovery through two interconnected mechanisms: (i) spatial gradients in the wind velocity field are weaker compared to LES and (ii) turbulence kinetic energy (TKE) remains low not because of excessive dissipation but due to insufficient shear production caused by these weakened gradients. For the scenario considered here, a wind-speed bias develops in the near-farm wake and persists into the far wake. Differences between the NWP-WFP simulations and LES emerge within a short distance downstream of the farm exit, where the mesoscale simulations recover too slowly. This reduced recovery contributes approximately 0.15-0.50 m s-1 to the near-farm wind-speed bias. The bias established in this region is not subsequently compensated for downstream but instead propagates into the far wake, where wind-speed differences of approximately 0.4-0.6 m s-1 remain up to 50 km downstream. Higher-resolution mesoscale simulations partially reduce this bias. Increasing turbine-added TKE or including subgrid wake effects provides additional improvement, but neither fully addresses the underlying cause. The slow wake recovery is not caused by limitations of the WFPs themselves, as it also occurs outside their region of influence, and adding subgrid wake effects does not significantly impact recovery. Rather, the slow wake recovery is a consequence of mesoscale flow representation. This behavior is not limited to regions downstream of the wind farm but is less visible within the farm, where wake recovery occurs simultaneously with turbine-induced momentum extraction. These results highlight the need for improved representations of wake recovery both within and downstream of wind farms. While enhanced subgrid modeling, shear-driven TKE production, and refined WFP formulations may improve intra-farm dynamics, accurately capturing near-farm wake recovery downstream remains challenging, as WFPs do not act in this region.

17 WIND ENERGY↗

DOE OSTI2026

Prognostic simulations of mixed-phase clouds with model AC-1D v1.0: the impact of aerosol types and freezing parameterizations on ice crystal budgets

Mixed-phase clouds at high latitudes contribute to the uncertainty in predicting cloud feedbacks and climate sensitivity, mainly due to the complexity of microphysical processes that influence the partitioning between the supercooled liquid and ice phases, and hence, cloud radiative effects on regional scales. Particularly in Arctic mixed-phase clouds, the activation of ice-nucleating particles (INPs) from various aerosol populations remains a leading source of uncertainty. We developed an aerosol-cloud one-dimensional (AC-1D) model, which provides a novel framework to prognostically treat INP and ice crystal budgets while explicitly accounting for polydisperse and multicomponent aerosol that activate INPs following different freezing parameterizations. The AC-1D model is informed by large-eddy simulations to probe the impact of INP representation on predicted ice crystal number concentrations (N i ) and ice crystal budgets in mixed-phase Arctic stratus. We apply three immersion freezing (IMF) parameterizations, two time-independent (singular) and one time-dependent (classical nucleation theory), to predict the evolution of the INP reservoir and resulting ice crystal budget from polydisperse mineral dust, organic (humic-like substances), and sea spray aerosol particle size distributions. Our analysis focuses on how variations in aerosol number concentration and cloud system parameters such as cloud cooling rate, cloud-top entrainment rate, and ice crystal fall speed influence the INP reservoir and ice crystal budgets. Furthermore, this study investigates the competitive ice nucleation dynamics in mixed aerosol environments and provides a process-level quantification of the INP budget terms, which directly controls ice crystal budgets. For all studied case scenarios, the aerosol types and associated particle size distributions significantly impact INP and N i , and the choice between a time-dependent and a singular freezing description yields orders-of-magnitude differences in the predicted INP and N i over the 10 h simulation time, reflecting typical cloud lifetimes. Our results show that the influence of cloud cooling, INP entrainment, and sedimentation varies significantly depending on the chosen freezing parameterization. These findings underscore the critical need for robust IMF parameterizations and precise cloud system observations to enhance the accuracy of models in predicting mixed-phase cloud structure and evolution.

Arctic clouds↗

DOE OSTI2026

Revisiting the validity of eddy viscosity models for predicting airflow over water waves

In this study, we revisit the validity of eddy viscosity models for predicting wave-induced airflow disturbances over ocean surface waves. We first derive a turbulence curvilinear model for the phase-averaged Navier–Stokes equations, extending the work of Cao, Deng & Shen (2020 J. Fluid Mech. 901, A27), by incorporating turbulence stress terms previously neglected in the linearised viscous curvilinear model. To verify our formulation, we perform a priori tests by numerically solving the model using mean wind and turbulence stress profiles from large-eddy simulations (LES) of airflow over waves across various wave ages. Results show that including turbulence stress terms improves wave-induced airflow predictions compared with the previous viscous curvilinear model. We further show that using a standard mixing-length eddy viscosity yields inaccurate predictions at certain wave ages, as it fails to capture wave-induced turbulence, which fundamentally differs from mean shear-driven turbulence. The LES data show that accurate representations of wave-induced stresses require a complex-valued eddy viscosity. The maximum magnitude of this eddy viscosity scales as ∼𝑢 𝜏 ⁢𝜁 𝑖𝑛𝑛𝑒𝑟 , where 𝑢 𝜏 is the friction velocity and 𝜁 𝑖𝑛𝑛𝑒𝑟 is the inner-layer thickness, the height at which the eddy-turnover time matches the wave advection time scale. This scaling aligns with the prediction by Belcher & Hunt (1993 J. Fluid Mech. 251, 109–148). Overall, the findings demonstrate that traditional eddy viscosity models are inadequate for capturing wave-induced turbulence. More sophisticated turbulence models are essential for the accurate prediction of airflow disturbances and form drag in wind–wave interaction models.

16 TIDAL AND WAVE POWER↗

DOE OSTI2026

Modeling Flow in a Microreactor Core: From High-Fidelity CFD to Subchannel Analysis

The accurate prediction of pressure drop in tightly packed, low–Reynolds number (Re) bare rod bundles is essential for the thermal-hydraulic design of the Microreactor Applications Research Validation and EvaLuation (MARVEL) reactor and other microreactor concepts. However, existing friction factor correlations, particularly the upgraded Cheng-Todreas (UCTD) correlation, have limited validation for the small pitch-to-diameter ratios (P/Ds) and transitional flow conditions characteristic of these systems. In this work, we perform high-fidelity large-eddy simulations (LESs) of both an infinite bare rod bundle and a finite bare 37-pin scalloped rod bundle across the range 1000≤ 𝑅𝑒 ≤5500. The simulations reveal strong gap vortex–driven transitional behavior and indicate that the UCTD may underpredict the friction factor by up to 28% at 𝑃/𝐷 = 1.05. Using the LES-calculated pressure drops, we formulate a new friction factor correlation that follows the Cheng-Todreas functional form but is calibrated for low-Re and tightly packed geometries representative of a MARVEL-like reactor. The correlation is implemented in the MOOSE (Multiphysics Object-Oriented Simulation Environment) subchannel module and compared against both the LES and UCTD predictions. Across all subchannel types, the proposed model reduces the streamwise velocity differences from as high as 44% (UCTD) to below 9%, and decreases the pressure gradient differences from 13% to 25% (UCTD) to 0.7% to 7% relative to the LES results. These results suggest that the new correlation has the potential to improve the pressure drop and flow field predictions for such geometries, highlighting the importance of high-fidelity simulations in supporting microreactor thermal-hydraulic model development and motivating future pressure drop experiments for compact rod bundles to further validate these findings.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗

DOE OSTI2025

Shallow cumulus entrainment: observational retrieval, physical interpretation, and climate impacts (Final grant report)

This document constitutes a final technical report for DE-SC0020083, a research grant awarded by the US Department of Energy Office of Science, to Prof. Daniel Kirshbaum at McGill University. The original research proposal, entitled "Shallow cumulus entrainment: observational retrieval, physical interpretation, and climate impacts", defined four scientific objectives that synthesized ARM observations with turbulence-permitting large-eddy simulations (LES) to quantify and understand the process of cumulus entrainment, whereby a cumulus cloud ingests surrounding air through its lateral edges. These objectives included developing observational climatologies of cumulus bulk entrainment rate at two ARM observatories, physical interpretation of these findings using LES, quantification of the impacts of subcloud circulations on entrainment, and the climate impacts of flow-dependent entrainment variations. In the six years that have elapsed since the grant was awarded (three funded years and three no-cost annual extensions), all four of the objectives were successfully addressed. Four personnel were trained (one B.Sc., one M.Sc. to completion, five years of Ph.D. training, and 2.5 years of postdoctoral training), eight peer-reviewed articles were published in high-impact journals, and 20 presentations were delivered (three invited).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Final DOE-ASR Report for the Project “Using LASSO to bridge the gap between model and observations and to learn about atmospheric convection”

Atmospheric convection spans a wide range of spatial and temporal scales and involves complex interactions with the surrounding dynamic and thermodynamic environment, particularly over tropical continental regions. These processes remain a major source of uncertainty in weather and climate models, including persistent biases in the diurnal cycle of convective precipitation that directly affect estimates of climate sensitivity. Addressing these challenges requires the combined use of high-resolution observations and cloud-resolving modeling frameworks. In this context, the DOE Atmospheric Radiation Measurement (ARM) program’s Large-Eddy Simulation ARM Symbiotic Simulation and Observation (LASSO) activity provides a powerful platform that pairs comprehensive observations with numerical simulations to enable process-level understanding of atmospheric convection. Within this context, this Research and Development Partnership Pilot (RDPP) project was designed to initiate and expand DOE ARM/ASR research capacity at minority-serving institutions, while advancing scientific understanding of convective processes over the Amazon rainforest. Consistent with the RDPP mission, the project emphasized partnership development, training, and workforce capacity building alongside exploratory research activities. On the scientific side, the project produced two peer-reviewed journal articles, and one manuscript currently under review (see list in section 3.1). Together, these studies combine long-term ARM observations and cloud-resolving and convection-permitting modeling to investigate the environmental controls on the shallow-to-deep convective transition during the Amazon wet season. The results demonstrate the central role of early-day moisture preconditioning and large-scale dynamical forcing in regulating isolated deep convection, provide mechanistic insight into convective evolution, and establish physically informed modeling frameworks for future sensitivity experiments. These scientific outcomes are described in sections 2.1 to 2.3 and were disseminated in 8 conference presentations (see section 3.2) and 5 invited talks (see section 3.3), reflecting broad engagement with our community. Equally important, the project achieved its RDPP capacity-building objectives (see section 2.4). A sustained research partnership was established among the University of Maryland, Baltimore County (UMBC), Morgan State University (MSU), and Howard University (HU), and extended to include collaboration with Pacific Northwest National Laboratory (PNNL). The project organized multiple multi-day training events focused on ARM data, LASSO simulations, and quantitative analysis methods, directly engaging students, postdoctoral researchers, and faculty across institutions. These activities broadened participation in ASR research and led to independent adoption of LASSO workflows by students beyond the immediate project team. Finally, the project successfully positioned the participating institutions to pursue future DOE research. Preliminary scientific results, coupled with strengthened partnerships and technical capacity, enabled the submission of follow-on proposals to DOE ASR funding opportunities. In this way, the project fulfilled the RDPP goal of seeding durable research capacity and laying the foundation for larger-scale, sustained engagement with DOE ARM and ASR programs.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

A robust aerosol impact on clouds along the subtropical to tropical transition

Marine clouds undergo a transition from subtropical stratocumulus (Sc) to shallow cumulus (Cu) and eventually to deep convective (DC) systems as air masses advect from the subtropics towards the deep tropics. How aerosols modulate this Lagrangian cloud evolution remains largely uncertain. Here we use both 5 years of satellite observations mapped along 8 d Lagrangian trajectories and complementary large-eddy simulations from 9 initiation locations across the Northeast Pacific, Southeast Pacific, and Southeast Atlantic. This Lagrangian framework allows us to quantify the aerosol effect and its co-variability with meteorological conditions on cloud microphysics, macrophysics, and top-of-atmosphere radiation through the full Sc-Cu-DC transition. We show that increasing aerosol concentrations leads to deeper and more reflective clouds throughout this cloud transition. Examining the thermodynamic evolution along the trajectory indicates a well-known trend: enhanced moistening near the boundary-layer top and lower free troposphere under polluted conditions, suggesting that part of the co-variability between aerosol and meteorological conditions may be internally driven. The agreement between model simulations and satellite data alongside the multi-basin coherence of the results indicates that aerosols systematically amplify cloud depth and reflectivity during the subtropical–to–tropical cloud transition.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

The remarkable inefficiency of stratocumulus

Marine stratocumulus clouds play a central role in Earth's climate system by reflecting incoming solar radiation and exerting a strong cooling effect. Their organization into open and closed mesoscale cellular morphologies can be thought of as an example of bistable dynamics driven by aerosol–cloud interactions and mesoscale processes. From the perspective of non-equilibrium thermodynamics, these structures are an example of a far-from-equilibrium open system that continuously produces and exports entropy. While entropy production has been studied in idealized deep convective systems, it has not yet been quantified for shallow clouds. Here, we compute and decompose the internal entropy production of open- and closed-cell stratocumulus using an ensemble of large-eddy simulations. We show that the overall entropy production of stratocumulus is low, reflecting the limited vertical extent and corresponding reduced ability to utilize the energy fluxes at the system's boundaries. Moist processes dominate the overall irreversibility, which, combined with their low entropy production, leads to a mechanical efficiency about an order of magnitude smaller than in deep convective systems. Although the dominant irreversible processes differ between open- and closed-cell regimes, the distributions of total entropy production largely overlap across the ensemble, limiting the ability to distinguish the dynamics of individual cases based solely on total entropy production.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Zooming in: SCREAM at 100 m using regional refinement over the San Francisco Bay Area

Pushing global climate models to large-eddy simulation (LES) scales over complex terrain has remained a major challenge. This study presents the first known implementation of a global model – SCREAM (Simple Cloud-Resolving E3SM Atmosphere Model) – at 100 m horizontal resolution using a regionally refined mesh (RRM) over the San Francisco Bay Area. Two hindcast simulations were conducted to test performance under both strong synoptic forcing and weak, boundary-layer-driven conditions. We demonstrate that SCREAM can stably run at LES scales while realistically capturing topography, surface heterogeneity, and coastal processes. The 100 m SCREAM-RRM substantially improves near-surface wind speed, temperature, humidity, and pressure biases compared to the baseline 3.25 km simulation, and better reproduces fine-scale wind oscillations and boundary-layer structures. These advances leverage SCREAM's scale-aware SHOC turbulence parameterization, which transitions smoothly across scales without tuning. Performance tests show that while CPU-only simulations remain costly, GPU acceleration with SCREAMv1 on NERSC's Perlmutter system enables two-day hindcasts to complete in under two wall-clock days. Our results open the door to LES-scale studies of orographic flows, boundary-layer turbulence, and coastal clouds within a fully comprehensive global modeling framework.

Geosciences↗

DOE OSTI2026

Wake-Resolving Acoustic Tomography: Advances through Numerical Covariance Methods

Acoustic tomography offers path-integrated measurements of atmospheric velocity and temperature fluctuations with high spatial resolution. Classical implementations of time-dependent stochastic inversion rely on homogeneous, isotropic covariance models that are poorly suited to the anisotropic structure of wind turbine wakes. By directly estimating heterogeneous covariances from large-eddy simulations (LESs) into the time-dependent stochastic inversion operator, we relax implicit assumptions in the analytical models used historically. Retrievals using these LES-informed models improve agreement with true fields in variance, turbulent kinetic energy, and spectral content compared to analytical and precursor-based covariance models. The results indicate that LES-informed covariance models can enhance the accuracy of acoustic tomography retrievals in complex, anisotropic flows such as wind turbine wakes in some cases and highlight instances where analytical models still offer competitive performance, despite their simplifying assumptions.

17 WIND ENERGY↗

DOE OSTI2026

Power output of turbines mounted on tension-leg platforms subjected to fully developed ocean gravity waves

A concern in the deployment of large wind turbines on ocean floating platforms is the effect of floating-platform motions on their electrical power generation. Further, it is not clear how floating motions influence waking, which might affect the combined power generation of collections of turbines. We examine the average power output of a single and a collection of NREL 5 MW wind turbines mounted on a tension-leg platform (TLP) under the action of fully developed ocean wave motions, coupling floating motions with the large-eddy simulation (LES) of atmospheric and rotor dynamics. The ocean dynamics enter as fully developed waves derived from the Pierson–Moskowitz spectrum. To assess the influence of ocean motions, we performed simulations over the full range of wind speeds in the operational range of the turbine, reporting comparisons of average power output when the platforms are allowed to move to when they are held rigidly in place. In all simulations, we find that the effects of the TLP floating-platform's induced motions have a minor effect on single and multiple turbine power production and wake deficits. Even when using coherent and large amplitude harmonic-floating-induced perturbations, any significant wake modifications from floating motions are confined to the near-wake region, where downstream turbines are unlikely to be located. The relatively small amplitude of TLP motions relative to pre-existing turbulent fluctuations are the primary reason for low wake and power modifications downstream.

Restrepo, Juan [ORNL] (ORCID:0000000326092882)↗

DOE OSTI2026

Introduction to and comparison of deep learning and optimization approaches to analytical wake modeling of a tilted wind turbine

This paper introduces innovative optimization and deep learning techniques to enhance the prediction of complex wake dynamics in the downstream wind velocity of tilted wind turbines. Traditional methods for calibrating the Bastankhah wake model often lead to increased errors in wind velocity distribution due to overfitting of the local wake characteristics. To address this issue, we propose an additional global optimization step to reduce errors in wind velocity predictions with respect to various wake parameters. Despite this improvement, the Bastankhah model's axisymmetric Gaussian wake shape limits its accuracy for complex wake structures. Therefore, we also propose a deep learning approach, which demonstrates promising results by accurately modeling complex wake shapes across a broader range of tilt angles with minimal computational cost. The deep learning approach achieves near-identical predictions to high-fidelity large-eddy simulations, representing a promising advancement in wake modeling.

17 WIND ENERGY↗

DOE OSTI2026

Large Eddy Simulations of the Atmospheric Boundary Layer Over Satellite‐Sensed Sea Ice Maps

Surface heterogeneity in the marginal ice zone (MIZ) causes multiscale secondary atmospheric circulations that are challenging to model or observe. The absence or inadequate representation of these circulations in ocean‐atmosphere exchange schemes in climate models is partially responsible for the underestimation of Arctic sea ice loss. Observationally, such circulations obfuscate the interpretation of polar atmospheric chemistry measurements, among others. To address this open challenge, large‐eddy simulations are conducted over real‐world satellite‐sensed sea ice maps with an ice‐sea temperature contrast, as well as over idealizations of these maps that alter the ice pattern but conserve its fraction, showing that the ice fraction of a surface is not sufficient to predict the surface heat flux. In a second suite of simulations, three other heterogeneity metrics (representing the surface fragmentation, patch edge tortuosity, and patch size variability) are introduced to complement the ice fraction in describing the surface. Simulations varying these parameters suggest that they also significantly impact surface‐air interactions. A roughness contrast is then added to the surface temperature contrast, indicating that the contribution of roughness changes to the resulting atmospheric circulations is less pronounced than that of thermal heterogeneity. Based on these results, we illustrate, using a multi‐linear regression on these map features, that MIZ surface parameterizations in Earth Systems Models can be improved if they account for these various characteristics of the sea‐ice patterns.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Generating a stratocumulus-like cloud top in a convection-cloud chamber

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.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Inverse Mapping of the Collision Kernel and Wall Flux Scaling in a Tall Convection‐Cloud Chamber Using Local Sensors and Knowledge‐Informed Deep Learning

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.

cloud chamber↗