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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Nonparametric, data-based kernel interpolation for particle-tracking simulations and kernel density estimation

Traditional interpolation techniques for particle tracking include binning and convolutional formulas that use pre-determined (i.e., closed-form, parameteric) kernels. In many instances, the particles are introduced as point sources in time and space, so the cloud of particles (either in space or time) is a discrete representation of the Green’s function of an underlying PDE. As such, each particle is a sample from the Green’s function; therefore, each particle should be distributed according to the Green’s function. In short, the kernel of a convolutional interpolation of the particle sample “cloud” should be a replica of the cloud itself. This idea gives rise to an iterative method by which the form of the kernel may be discerned in the process of interpolating the Green’s function. When the Green’s function is a density, this method is broadly applicable to interpolating a kernel density estimate based on random data drawn from a single distribution. We formulate and construct the algorithm and demonstrate its ability to perform kernel density estimation of skewed and/or heavy-tailed data including breakthrough curves.

42 ENGINEERING↗
DOE OSTI2026

Model Sensitivities of Biomass-Burning Aerosol Chemical Aging, Sulfate Formation, and Cloud Droplet Activation in the Southeastern Atlantic Using CESM and E3SM

Biomass-burning smoke drives large uncertainty in climate projections of the Earth's radiative balance. This is due to the chemical and physical evolution of smoke and its impact on clouds and radiation. Here we focus on the southeastern Atlantic region and its inflow of African biomass-burning smoke during August 2017. We evaluate smoke properties and processes in two coupled earth-system models, the Energy Exascale Earth System Model (E3SM) and Community Earth System Model (CESM). These are compared against in situ aircraft observations from two field campaigns, ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) and CLoud–Aerosol–Radiation Interaction and Forcing: Year 2017 (CLARIFY-2017). Observations reveal an increase and subsequent decrease in smoke mean diameter, and a steady decrease in the mass ratio of organic aerosol (OA) to black carbon aerosol (BC) (OA:BC) over 4–12 days of aging, neither captured by the base models. Implementation of a photolytic loss scheme for secondary organic aerosol (SOA)—as a proxy for other heterogeneous volatilization chemistry—and a ∼1-day conversion for primary OA to SOA significantly improves the representation of this loss. In the boundary layer, both models show dimethyl sulfide driving a large increase in the sulfate aerosol mass fraction from the free troposphere, which is consistent with observations. Finally, models tend to underpredict cloud droplet number concentration partially due to weak modeled turbulent updraft strength, and model performance improves when the parameterized turbulent updraft strength is increased substantially. These results are expected to provide insights into future model development to reduce climate model uncertainties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Interactions Between Clouds and Wind-Driven Surface Heat Exchanges over Land

Earth system model experiments show that increasing horizontal resolution fundamentally alters the simulated soil-moisture-precipitation feedback. Kilometer-scale simulations often produce weaker or even negative feedback compared to coarse-resolution models. A key difference of kilometer-scale models is that they resolve mesoscale secondary circulations, including boundary layer horizontal rolls and cellular structures, in addition to cold pools and downdrafts associated with convective precipitation. However, because the relevant processes occur on yet-smaller scales, these circulations are often poorly resolved. This project demonstrated that boundary layer secondary circulations significantly affect surface heat exchanges and wind gusts, and that current model parameterizations can misrepresent these processes at kilometer-scale resolution. Using DOE Atmospheric Radiation Measurement (ARM) observations and targeted experiments with the DOE Energy Exascale Earth System Model (E3SM), we identified physically unrealistic wind gust and surface flux responses to secondary circulations, diagnosed a systematic overestimation of wind shear in convective cold pools, and uncovered a multivariate relationship between land surface fluxes and the scales of updrafts that form shallow cumulus clouds. These findings provide observation-based recommendations for improving parameterizations of surface fluxes and wind gusts in high-resolution Earth system models, thereby reducing uncertainty in convective storm prediction and land-atmosphere feedbacks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Enhancing Convection Parameterization for Next Generation E3SM (Final Technical Report)

This technical report summarizes the achievements during the project period. The general goal of the project is to enhance the representation of atmospheric deep convection in the DOE global climate model E3SM. By implementing a stochastic convection scheme into E3SM, we improved the simulation of precipitation intensity and frequency. As a result, it has a profound impact on the simulation of other fields in the model, including aerosol scavenging by precipitation and aerosol radiative effects. We also implemented a convective cloud microphysics scheme into E3SMv2 to represent more accurately the interaction between convection and grid-scale clouds. An extensive analysis of model simulations as part of the Next Generation Development (NGD) convection scheme assessment project found that including the convective microphysics scheme improved the simulation of Madden-Julian oscillations (MJO) in the model. To make convective parameterization schemes more scale-aware as the GCM resolution increases, we analyzed the output from cloud-resolving model (CRM) simulations of both organized and unorganized convection by coarse-graining the CRM data to different GCM resolutions. Using a novel approach to modify the convective closure, we demonstrated that the convection scheme used in E3SM (ZM scheme) can be made scale-aware.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Revisiting particle dry deposition and its role in radiative effect estimates

Wet and dry deposition remove aerosols from the atmosphere, and these processes control aerosol lifetime and thus impact climate and air quality. Dry deposition is a significant source of aerosol uncertainty in global chemical transport and climate models. Dry deposition parameterizations in most global models were developed when few particle deposition measurements were available. However, new measurement techniques have enabled more size-resolved particle flux observations. We combined literature measurements with data that we collected over a grassland in Oklahoma and a pine forest in Colorado to develop a dry deposition parameterization. We find that relative to observations, previous parameterizations overestimated deposition of the accumulation and Aitken mode particles, and underestimated in the coarse mode. These systematic differences in observed and modeled accumulation mode particle deposition velocities are as large as an order of magnitude over terrestrial ecosystems. As accumulation mode particles form most of the cloud condensation nuclei (CCN) that influence the indirect radiative effect, this model-measurement discrepancy in dry deposition alters modeled CCN and radiative forcing. We present a revised observationally driven parameterization for regional and global aerosol models. Using this revised dry deposition scheme in the Goddard Earth Observing System (GEOS)-Chem chemical transport model, we find that global surface accumulation-mode number concentrations increase by 62% and enhance the global combined anthropogenic and natural aerosol indirect effect by −0.63 W m −2 . Our observationally constrained approach should reduce the uncertainty of particle dry deposition in global chemical transport models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Deep learning to represent subgrid processes in climate models

The representation of nonlinear subgrid processes, especially clouds, has been a major source of uncertainty in climate models for decades. Cloud-resolving models better represent many of these processes and can now be run globally but only for short-term simulations of at most a few years because of computational limitations. Here we demonstrate that deep learning can be used to capture many advantages of cloud-resolving modeling at a fraction of the computational cost. We train a deep neural network to represent all atmospheric subgrid processes in a climate model by learning from a multiscale model in which convection is treated explicitly. The trained neural network then replaces the traditional subgrid parameterizations in a global general circulation model in which it freely interacts with the resolved dynamics and the surface-flux scheme. The prognostic multiyear simulations are stable and closely reproduce not only the mean climate of the cloud-resolving simulation but also key aspects of variability, including precipitation extremes and the equatorial wave spectrum. Furthermore, the neural network approximately conserves energy despite not being explicitly instructed to. Finally, we show that the neural network parameterization generalizes to new surface forcing patterns but struggles to cope with temperatures far outside its training manifold. Our results show the feasibility of using deep learning for climate model parameterization. In a broader context, we anticipate that data-driven Earth system model development could play a key role in reducing climate prediction uncertainty in the coming decade.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Dynamics, thermodynamics, radiation, and cloudiness associated with cumulus-topped marine boundary layers

The overall goal of this project was to improve the understanding of marine boundary clouds by using data collected at the Atmospheric Radiation Measurement (ARM) sites, so that they can be better represented in global climate models (GCMs). Marine boundary clouds are observed regularly over the tropical and subtropical oceans. They are an important element of the Earth’s climate system because they have substantial impact on the radiation budget together with the boundary layer moisture, and energy transports. These clouds also have an impact on large-scale precipitation features like the Inter Tropical Convergence Zone (ITCZ). Because these clouds occur at temporal and spatial scales much smaller than those relevant to GCMs, their effects and the associated processes need to be parameterized in GCM simulations aimed at predicting future climate and energy needs. Specifically, this project’s objectives were to (1) characterize the surface turbulent fluxes, boundary layer thermodynamics, radiation field, and cloudiness associated with cumulus-topped marine boundary layers; (2) explore the similarities and differences in cloudiness and boundary layer conditions observed in the tropical and trade-wind regions; and (3) understand similarities and differences by using a simple bulk boundary layer model. In addition to working toward achieving the project’s three objectives, we also worked on understanding the role played by different forcing mechanisms in maintaining turbulence within cloud-topped boundary layers We focused our research on stratocumulus clouds during the first phase of the project, and cumulus clouds during the rest of the project. Below is a brief description of manuscripts published in peer-reviewed journals that describe results from our analyses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Simulating Pyrocumulonimbus Clouds Using a Multiscale Wildfire Simulation Framework

Pyrocumulonimbus (pyroCb) clouds, driven by extreme fires under favorable meteorological conditions, can inject smoke into the stratosphere at magnitudes comparable to those of moderate volcanic eruptions, potentially altering the global radiative balance and atmospheric composition. However, simulating pyroCb is particularly challenging in Earth system models. Using the Energy Exascale Earth System Model (E3SM), we developed a novel global multiscale framework to model pyroCb events in California, which includes a high‐resolution fire radiative power time series, a one‐dimensional plume‐rise parameterization, a fire‐induced vertical water vapor transport scheme, and a surface wildfire sensible heat flux representation. Our simulation successfully reproduces many pyroCb features, including cloud height, spatiotemporal evolution, and convective intensity in comparison with satellite and ground‐based observations. Sensitivity experiments show that realistic pyroCb simulation depends on vertical water vapor transport. These advances provide a basis for future exploration of pyroCb impacts at regional and global scales within climate models.

E3SM↗
DOE OSTI2021

The influence of fire aerosols on surface climate and gross primary production in the Energy Exascale Earth System Model (E3SM)

Fire-emitted aerosols play an important role in influencing Earth’s climate, directly by scattering and absorbing radiation and indirectly by influencing cloud microphysics. The quantification of fire-aerosol interactions, however, remains challenging and subject to uncertainties in emissions, plume parameterizations, and aerosol properties. Here we optimized fire-associated aerosol emissions in the Energy Exascale Earth System Model (E3SM) using the Global Fire Emissions Database (GFED) and AERONET aerosol optical depth (AOD) observations during 1997-2016. Furthermore, we distributed fire emissions vertically using smoke plume heights from Multi-angle Imaging SpectroRadiometer (MISR) satellite observations. From the optimization, we estimate that global fires emit 45.5 Tg y -1 of primary particulate organic matter and 3.9 Tg y -1 of black carbon. We then performed two climate simulations with and without the optimized fire emissions. We find that fire aerosols significantly increase global AOD by 14 ± 7% and contribute to a reduction in net shortwave radiation at the surface (-2.3 ± 0.5 W m -2 ). Together, fire-induced direct and indirect aerosol effects cause annual mean global land surface air temperature to decrease by 0.17 ± 0.15°C, relative humidity to increase by 0.4 ± 0.3%, and diffuse light fraction to increase by 0.5 ± 0.3%. In response, GPP declines by 2.8 Pg C y -1 , as a result of large positive drivers (decreases in temperature and increases in humidity and diffuse light) nearly cancelling out large negative drivers (decreases shortwave radiation and soil moisture). Our analysis highlights the importance of fire aerosols in modifying surface climate and photosynthesis across the tropics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

ARM Data-Oriented Metrics and Diagnostics Package for Climate Model Evaluation

A Python-based metrics and diagnostics package is currently being developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Infrastructure Team at Lawrence Livermore National Laboratory (LLNL) to facilitate the use of long-term, high-frequency measurements from the ARM Facility in evaluating the regional climate simulation of clouds, radiation, and precipitation. This metrics and diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots for comparing the model simulation with ARM observational data. The Coupled Model Intercomparison Project (CMIP) model data sets are also included in the package to enable model intercomparison as demonstrated in Zhang et al. (2017). The mean of the CMIP model can serve as a reference for individual models. Basic performance metrics are computed to measure the accuracy of mean state and variability of climate models. The evaluated physical quantities include cloud fraction, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, and radiative fluxes, with plan to extend to more fields, such as aerosol and microphysics properties. Process-oriented diagnostics focusing on individual cloud- and precipitation-related phenomena are also being developed for the evaluation and development of specific model physical parameterizations. The version 1.0 package is designed based on data collected at ARM’s Southern Great Plains (SGP) Research Facility, with the plan to extend to other ARM sites. The metrics and diagnostics package is currently built upon standard Python libraries and additional Python packages developed by DOE (such as CDMS and CDAT). The ARM metrics and diagnostic package is available publicly with the hope that it can serve as an easy entry point for climate modelers to compare their models with ARM data. In this report, we first present the input data, which constitutes the core content of the metrics and diagnostics package in section 2, and a user's guide documenting the workflow/structure of the version 1.0 codes, and including step-by-step instruction for running the package in section 3.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

ARM data-oriented metrics and diagnostics package for climate model evaluation (ARM-DIAGS-V3) version 3

A Python-based metrics and diagnostics package is currently being developed by the ARM Infrastructure Team at Lawrence Livermore National Laboratory to facilitate the use of long-term high frequency measurements from the ARM program in evaluating the regional climate simulation of clouds, radiation, precipitation, and aerosols. This metrics and diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots for comparing the model simulation with ARM observational data. The CMIP model data sets are also included in the package to enable model inter-comparison as demonstrated in Zhang et al. (2018) and Zhang et al. (2020). The mean of the CMIP model can be served as a reference for individual models. Basic performance metrics are computed to measure the accuracy of mean state and variability of climate models. The evaluated physical quantities include cloud fraction, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, aerosol optical depth, and radiative fluxes, with plan to extend to more fields, such as the evaluation of model simulated aerosol physicochemical properties and cloud microphysics properties. Process-oriented diagnostics focusing on aerosol, cloud, and precipitation-related phenomena are also being developed for the evaluation and development of specific model physical parameterizations. In addition to the Southern Great Plains (SGP), North Slope of Alaska (NSA) and Tropical Western Pacific (TWP) atmospheric observatories in the ARMDIAGS version 2.0, the version 3.0 package have extended to the data collected at the ARM Eastern North Atlantic (ENA) atmospheric observatory and the Observation and Modeling of the Green Ocean Amazon (GOAMAZON) field campaign. The metrics and diagnostics package are currently built upon standard Python libraries and additional Python packages developed by DOE (CDAT). The ARM metrics and diagnostic package is available publicly with the hope that it can serve as an easy entry point for climate modelers to compare their models with ARM data. In this report, we first provide an overview of major metrics in section 2. The input data, which constitutes the core content of the metrics and diagnostics package, is summarized in section 3. A user's guide documenting the workflow/structure of the version 3.0 codes and including step-by-step instruction for running the package is described in section 4.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Haze–cloud correlations mediated by supersaturation fluctuations

Atmospheric aerosol particles that contain water-soluble components can absorb water vapor in humid environments and form either haze particles or cloud droplets, depending on supersaturation conditions. Laboratory and in situ measurements have shown that haze particles and cloud droplets often coexist and compete for available water vapor in shallow clouds and fogs, especially under polluted conditions. It is expected that more aerosol particles can form more haze particles and cloud droplets, so that the haze and cloud number concentrations are positively correlated. However, recent large-eddy simulations show that haze and cloud number concentration can be negatively correlated under extremely polluted conditions. Haze–cloud interactions across different environmental settings remain poorly understood. In this study, experiments in a convection cloud chamber with the same aerosol injection rate show that, as supersaturation forcing increases (i.e., changing from polluted to clean conditions), the covariance between haze and cloud droplet number concentration changes from negative to positive and finally to zero. Large-eddy simulations (LES) of the cloud chamber with a fixed supersaturation forcing but varying aerosol injection rates show a similar result for the haze–cloud correlation: near zero in clean, mean-supersaturation-dominated activation conditions; positive in moderate, supersaturation-fluctuation-influenced activation conditions; and negative in polluted, supersaturation-fluctuation-dominated activation conditions. A theoretical covariance framework was developed to interpret this behavior based on the relative magnitude and signs of the correlations between supersaturation and the populations of haze and cloud droplets. Significantly, experiments, LES, and theory all yield the same three-regime behavior for the sign of the haze–cloud covariance. Furthermore, our results show that the haze–cloud covariance remains robust and easily measurable, thereby providing a useful metric for regime identification in the atmosphere, improving regime-aware parameterizations, and informing aerosol interventions such as fog dispersion, rainfall enhancement, and albedo modification.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Solar Irradiance Nowcasting Case Studies near Sacramento

The Sun4Cast solar power forecasting system, designed to predict solar irradiance and power generation at solar farms, is composed of several component models operating on both the nowcasting (0–6 h) and day-ahead forecast horizons. The different nowcasting models include a statistical forecasting model (StatCast), two satellite-based forecasting models [the Cooperative Institute for Research in the Atmosphere Nowcast (CIRACast) and the Multisensor Advection-Diffusion Nowcast (MADCast)], and a numerical weather prediction model (WRF-Solar). It is important to better understand and assess the strengths and weaknesses of these short-range models to facilitate further improvements. To that end, each of these models, including four WRF-Solar configurations, was evaluated for four case days in April 2014. For each model, the 15-min average predicted global horizontal irradiance (GHI) was compared with GHI observations from a network of seven pyranometers operated by the Sacramento Municipal Utility District (SMUD) in California. Each case day represents a canonical sky-cover regime for the SMUD region and thus represents different modeling challenges. The analysis found that each of the nowcasting models perform better or worse for particular lead times and weather situations. StatCast performs best in clear skies and for 0–1-h forecasts; CIRACast and MADCast perform reasonably well when cloud fields are not rapidly growing or dissipating; and WRF-Solar, when configured with a high-spatial-resolution aerosol climatology and a shallow cumulus parameterization, generally performs well in all situations. Further research is needed to develop an optimal dynamic blending technique that provides a single best forecast to energy utility operators.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Sensitivity Study for Forecasting Variables of WRF-Solar Using a Tangent Linear Approach

Integrating solar generation in recent years has highlighted the need for improved accuracy in predicting solar power. Confidence in solar power forecasting can be achieved by designing an ensemble that provides reliable probabilistic information for solar radiation with reduced uncertainty and error. Ideally, ensemble members are created through the optimized perturbation of the initial conditions in numerical weather prediction (NWP) models. Tangent linear models are capable of efficiently investigating the sensitivity of solar radiation to model input parameters because they do not require individual perturbation of each variable. This sensitivity study using tangent linear models provide us the capability to identify the right variables to perturb in an ensemble prediction system. In this study, we developed tangent linear models for WRF-Solar modules that directly impact the computation of solar radiation and the simulation of cloud formation and dissipation including the Fast All-sky Model for Solar Applications (FARMS), the Noah land surface model (LSM), the Thompson microphysics, the Mello-Yamada-Nakanishi-Niino (MYNN) boundary layer parameterization, and the Deng scheme for a shallow-convection parameterization. A sensitivity analysis was conducted under various scenarios based on satellite observations and model simulations from the National Solar Radiation Data Base (NSRDB) and WRF-Solar, respectively. Critical forecasting variables that are highly sensitive to the forecasting of global horizontal irradiance (GHI), direct normal irradiance (DNI), cloud mixing ratio, cloud tendency, cloud fraction, and sensible and latent heat fluxes were determined using the relevant WRF-Solar module. This study will be used as a guidance on future research leading to high-quality probabilistic solar forecasting. In this presentation, we discuss the validation of tangent linear approach for WRF-Solar modules and illustrate how the sensitivity results are valuable in the improvement of probabilistic solar prediction.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Influence of explicit Phaeocystis parameterizations on the global distribution of marine dimethyl sulfide

Dimethyl sulfide (DMS) is a biogenic organosulfur compound which contributes strongly to marine aerosol mass and the determination of cloud condensation nuclei over the remote oceans. Since uncertainties in DMS flux to the atmosphere lead to large variations in climate forcing, the global DMS distribution has been the subject of increasingly complex dynamic simulations. DMS concentrations are directly controlled by marine ecosystems. Phaeocystis is a major DMS producer but is often omitted from global reduced sulfur mechanisms. Here we incorporate this phytoplankton group into the marine ecosystem-biogeochemical module of the Community Earth System Model. To examine its role in the ocean sulfur cycle, an earlier DMS model has been enhanced to include new knowledge gained over the last few years. Results from the baseline run show that simulated Phaeocystis biomass generally agrees with observations, with high concentrations near the Antarctic continent and between 50° and 60° north. Given the new explicit Phaeocystis representation, the DMS distribution shows significant improvements, especially regarding the amplitude and location of high-latitude peaks. The simulated global mean surface DMS value is 2.26 nM, comparable to an estimate of 2.34 nM from the latest climatology extrapolated based on observations. The total oceanic DMS source to the atmosphere is 20.4 Tg S/yr, on the low side of previous estimates. Comparisons with and without Phaeocystis show that the group dominates DMS distributions in temperate and cold waters, contributing 13% of the global flux. The proportion may increase as sea ice declines and should be considered in climate projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Effects of Pollen on Hydrometeors and Precipitation in a Convective System

Anemophilous (wind-driven) pollen is one type of primary biological aerosol particle, which can rupture under high humidity conditions and form smaller sub-pollen particles (SPPs). Both pollen and SPPs can reach the upper troposphere under convective conditions, acting as cloud condensation nuclei (CCN) and ice nucleating particles (INPs), thus influencing cloud formation and precipitation. However, the impacts of these biological aerosols on cold cloud formation and local climate remain unclear as there are large uncertainties on their emission flux and ice nucleating abilities. Here, we incorporate pollen emission and rupture processes in the Weather Research and Forecasting Model with Chemistry (WRF-Chem) simulations and update the Morrison microphysics scheme within WRF-Chem using aerosol-aware INP parameterizations to account for pollen in addition to other anthropogenic and biogenic aerosol. INP parameterizations for pollen and SPP are derived from laboratory experiments. When including pollen rupture rates as observed in a series of chamber studies, SPP concentrations increase, leading to an increase of cloud ice and water by up to 50% and potentially extending the duration of the convective system. Among all simulated hydrometeors, graupel and raindrops exhibit the largest enhancements from the inclusion of SPPs, with intensifying precipitation at the backside of the convective system and a greater spatial extent. Sensitivity simulations indicate that SPPs have a greater effect on cloud microphysical processes than whole pollen grains, and further observational evidence is needed to constrain these processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Designing a Convection‐Cloud Chamber for Collision‐Coalescence Using Large‐Eddy Simulation With Bin Microphysics

Abstract Collisional growth of cloud droplets is an essential yet uncertain process for drizzle and precipitation formation. To improve the quantitative understanding of this key component of cloud‐aerosol‐turbulence interactions, observational studies of collision‐coalescence in a controlled laboratory environment are needed. In an existing convection‐cloud chamber (the Pi Chamber), collisional growth is limited by low liquid water content and short droplet residence times. In this work, we use numerical simulations to explore various configurations of a convection‐cloud chamber that may intensify collision‐coalescence. We employ a large‐eddy simulation (LES) model with a size‐resolved (bin) cloud microphysics scheme to explore how cloud properties and the intensity of collision‐coalescence are affected by the chamber size and aspect ratio, surface roughness, side‐wall wetness, side‐wall temperature arrangement, and aerosol injection rate. Simulations without condensation and evaporation within the domain are first performed to explore the turbulence dynamics and wall fluxes. The LES wall fluxes are used to modify the Scalar Flux‐budget Model, which is then applied to demonstrate the need for non‐uniform side‐wall temperature (two side walls as warm as the bottom and the two others as cold as the top) to maintain high supersaturation in a tall chamber. The results of LES with full cloud microphysics reveal that collision‐coalescence is greatly enhanced by employing a taller chamber with saturated side walls, non‐uniform side‐wall temperature, and rough surfaces. For the conditions explored, although lowering the aerosol injection rate broadens the droplet size distribution, favoring collision‐coalescence, the reduced droplet number concentration decreases the frequency of collisions.

54 ENVIRONMENTAL SCIENCES↗