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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 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Challenges in constraining anthropogenic aerosol effects on cloud radiative forcing using present-day spatiotemporal variability

A large number of processes are involved in the chain from emissions of aerosol precursor gases and primary particles to impacts on cloud radiative forcing. Those processes are manifest in a number of relationships that can be expressed as factors dlnX/dlnY driving aerosol effects on cloud radiative forcing. These factors include the relationships between cloud condensation nuclei (CCN) concentration and emissions, droplet number and CCN concentration, cloud fraction and droplet number, cloud optical depth and droplet number, and cloud radiative forcing and cloud optical depth. The relationship between cloud optical depth and droplet number can be further decomposed into the sum of two terms involving the relationship of droplet effective radius and cloud liquid water path with droplet number. These relationships can be constrained using observations of recent spatial and temporal variability of these quantities. However, we are most interested in the radiative forcing since the preindustrial era. Because few relevant measurements are available from that era, relationships from recent variability have been assumed to be applicable to the preindustrial to present-day change. Our analysis of Aerosol Comparisons between Observations and Models (AeroCom) model simulations suggests that estimates of relationships from recent variability are poor constraints on relationships from anthropogenic change for some terms, with even the sign of some relationships differing in many regions. Proxies connecting recent spatial/temporal variability to anthropogenic change, or sustained measurements in regions where emissions have changed, are needed to constrain estimates of anthropogenic aerosol impacts on cloud radiative forcing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Are turbulence effects on droplet collision–coalescence a key to understanding observed rain formation in clouds?

Rain formation is a critical factor governing the lifecycle and radiative forcing of clouds and therefore it is a key element of weather and climate. Cloud microphysics–turbulence interactions occur across a wide range of scales and are challenging to represent in atmospheric models with limited resolution. Based on past experiments and idealized numerical simulations, it has been postulated that cloud turbulence accelerates rain formation by enhancing drop collision–coalescence. We provide substantial evidence for significant impacts of turbulence on the evolution of cloud droplet size distributions and rain formation by comparing high-resolution observations of cumulus congestus clouds with state-of-the-art large-eddy simulations coupled with a Lagrangian particle-based microphysics scheme. Turbulent coalescence must be included in the model to accurately represent the observed drop size distributions, especially for drizzle drop sizes at lower heights in the cloud. Turbulence causes earlier rain formation and greater rain accumulation compared to simulations with gravitational coalescence only. The observed rain size distribution tail just above cloud base follows a power law scaling that deviates from theoretical scalings considering either a purely gravitation collision kernel or a turbulent kernel neglecting droplet inertial effects, providing additional evidence for turbulent coalescence in clouds. In contrast, large aerosols acting as cloud condensation nuclei (“giant CCN”) do not significantly impact rain formation owing to their long timescale to reach equilibrium wet size relative to the lifetime of rising cumulus thermals. Overall, turbulent drop coalescence exerts a dominant influence on rain initiation in warm cumulus clouds, with limited impacts of giant CCN.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

NEGLECTED CLOUDS IN T AND Y DWARF ATMOSPHERES

As brown dwarfs cool, a variety of species condense in their atmospheres, forming clouds. Iron and silicate clouds shape the emergent spectra of L dwarfs, but these clouds dissipate at the L/T transition. A variety of other condensates are expected to form in cooler T dwarf atmospheres. These include Cr, MnS, Na{sub 2}S, ZnS, and KCl, but the opacity of these optically thinner clouds has not been included in previous atmosphere models. Here, we examine their effect on model T and Y dwarf atmospheres. The cloud structures and opacities are calculated using the Ackerman and Marley cloud model, which is coupled to an atmosphere model to produce atmospheric pressure-temperature profiles in radiative-convective equilibrium. We generate a suite of models between T{sub eff} = 400 and 1300 K, log g = 4.0 and 5.5, and condensate sedimentation efficiencies from f{sub sed} = 2 to 5. Model spectra are compared to two red T dwarfs, Ross 458C and UGPS 0722-05; models that include clouds are found to match observed spectra significantly better than cloudless models. The emergence of sulfide clouds in cool atmospheres, particularly Na{sub 2}S, may be a more natural explanation for the 'cloudy' spectra of these objects, rather than the reemergence of silicate clouds that wane at the L-to-T transition. We find that sulfide clouds provide a mechanism to match the near- and mid-infrared colors of observed T dwarfs. Our results indicate that including the opacity of condensates in T dwarf atmospheres is necessary to accurately determine the physical characteristics of many of the observed objects.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

An evolutionary model for collapsing molecular clouds and their star formation activity. II. Mass dependence of the star formation rate

We discuss the evolution and dependence on cloud mass of the star formation rate (SFR) and efficiency (SFE) of star-forming molecular clouds (MCs) within the scenario that clouds are undergoing global collapse and that the SFR is controlled by ionization feedback. We find that low-mass clouds (M {sub max} ≲ 10{sup 4} M {sub ☉}) spend most of their evolution at low SFRs, but end their lives with a mini-burst, reaching a peak SFR ∼10{sup 4} M {sub ☉} Myr{sup –1}, although their time-averaged SFR is only (SFR) ∼ 10{sup 2} M {sub ☉} Myr{sup –1}. The corresponding efficiencies are SFE{sub final} ≲ 60% and (SFE) ≲ 1%. For more massive clouds (M {sub max} ≳ 10{sup 5} M {sub ☉}), the SFR first increases and then reaches a plateau because the clouds are influenced by stellar feedback since earlier in their evolution. As a function of cloud mass, (SFR) and (SFE) are well represented by the fits (SFR) ≈ 100(1 + M {sub max}/1.4 × 10{sup 5} M {sub ☉}){sup 1.68} M {sub ☉} Myr{sup –1} and (SFE) ≈ 0.03(M {sub max}/2.5 × 10{sup 5} M {sub ☉}){sup 0.33}, respectively. Moreover, the SFR of our model clouds follows closely the SFR-dense gas mass relation recently found by Lada et al. during the epoch when their instantaneous SFEs are comparable to those of the clouds considered by those authors. Collectively, a Monte Carlo integration of the model-predicted SFR(M) over a Galactic giant molecular cloud mass spectrum yields values for the total Galactic SFR that are within half an order of magnitude of the relation obtained by Gao and Solomon. Our results support the scenario that star-forming MCs may be in global gravitational collapse and that the low observed values of the SFR and SFE are a result of the interruption of each SF episode, caused primarily by the ionizing feedback from massive stars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Physical effects on compact high-velocity clouds in the circumgalactic medium

ABSTRACT We numerically investigate the evolution of compact high-velocity clouds (CHVCs) passing through a hot, tenuous gas representing the highly ionized circumgalactic medium (CGM) by applying the adaptive-mesh refinement code flash. The model clouds start from both hydrostatic and thermal equilibrium and are in pressure balance with the CGM. Here, we present 14 models, divided into two mass categories and two metallicities each and different velocities. We allow for self-gravity and thermal conduction or not. All models experience mass diffusion, radiative cooling, and external heating leading to dissociation and ionization. Our main findings are (1) self-gravity stabilizes clouds against Rayleigh–Taylor instability, which is disrupted within 10 sound-crossing times without; (2) clouds can develop Jeans-instable regions internally even though they are initially below Jeans mass; (3) all clouds lose mass by ram pressure and Kelvin–Helmholtz instability; (4) thermal conduction substantially lowers mass-loss rates, by this, extending the clouds’ lifetimes, particularly, more than doubling the lifetime of low-mass clouds; (5) thermal conduction leads to continuous, filamentary stripping, while the removed gas is heated up quickly and mixes efficiently with the ambient CGM; (6) without thermal conduction the removed gas consists of dense, cool, clumpy fragments; (7) thermal conduction might prevent CHVCs from forming stars; and (8) clouds decelerated by means of drag from the ambient CGM form head-tail shapes and collapse after they reach velocities characteristic for intermediate-velocity clouds. Conclusively, only sophisticated modelling of CHVCs as non-homogeneous and non-isothermal clouds with thermal conduction and self-gravity explains observed morphologies and naturally leads to the suppression of star formation.

Sander, Bastian↗
DOE OSTI2023

Let’s Unleash the Network Judgment: A Self-Supervised Approach for Cloud Image Analysis

Accurate cloud type identification and coverage analysis are crucial in understanding the Earth’s radiative budget. Traditional computer vision methods rely on low-level visual features of clouds for estimating cloud coverage or sky conditions. Several handcrafted approaches have been proposed; however, scope for improvement still exists. Newer deep neural networks (DNNs) have demonstrated superior performance for cloud segmentation and categorization. These methods, however, need expert engineering intervention in the preprocessing steps—in the traditional methods—or human assistance in assigning cloud or clear sky labels to a pixel for training DNNs. Such human mediation imposes considerable time and labor costs. We present the application of a new self-supervised learning approach to autonomously extract relevant features from sky images captured by ground-based cameras, for the classification and segmentation of clouds. We evaluate a joint embedding architecture that uses self-knowledge distillation plus regularization. We use two datasets to demonstrate the network’s ability to classify and segment sky images—one with ~85,000 images collected from our ground-based camera and another with 400 labeled images from the WSISEG database. We find that this approach can discriminate full-sky images based on cloud coverage, diurnal variation, and cloud base height. Additionally, it semantically segments the cloud areas without labels. The approach shows competitive performance in all tested tasks, suggesting a new alternative for cloud characterization.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Aerosol-Cloud-Meteorology Interaction Airborne Field Investigations: Using Lessons Learned from the US West Coast in the Design of ACTIVATE off the US East Coast

We report on a multi-year set of airborne field campaigns (2005 – 2016) off the California coast to examine aerosols, clouds, and meteorology, and how lessons learned tie into the upcoming NASA Earth Venture Suborbital (EVS-3) campaign: Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) (2019–2023). The largest uncertainty in estimating global anthropogenic radiative forcing is associated with the interactions of aerosol particles with clouds, which stems from the variability of cloud systems and the multiple feedbacks that affect and hamper efforts to ascribe changes in cloud properties to aerosol perturbations. While past campaigns have been limited in flight hours and the ability to fly in and around clouds, efforts sponsored by the Office of Naval Research have resulted in 113 single aircraft flights (> 500 flight hours) in a fixed region with warm marine boundary layer clouds. All flights used nearly the same payload of instruments on a Twin Otter to fly below, in, and above clouds, producing an unprecedented dataset. We provide here (i) an overview of statistics of aerosol, cloud, and meteorological conditions encountered in those campaigns and (ii) quantification of model-relevant metrics associated with aerosol-cloud interactions leveraging the high data volume and statistics. Based on lessons learned from those flights, we describe the pragmatic innovation in sampling strategy (dual aircraft approach with combined in situ and remote sensing) that will be used in ACTIVATE to generate a dataset that can advance scientific understanding and improve physical parameterizations for Earth system and weather forecasting models, and for assessing next-generation remote sensing retrieval algorithms.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Deriving Arctic Cloud Microphysics at Barrow, Alaska. Algorithms, Results, and Radiative Closure

Cloud phase and microphysical properties control the radiative effects of clouds in the climate system and are therefore crucial to characterize in a variety of conditions and locations. An Arctic-specific, ground-based, multi-sensor cloud retrieval system is described here and applied to two years of observations from Barrow, Alaska. Over these two years, clouds occurred 75% of the time, with cloud ice and liquid each occurring nearly 60% of the time. Liquid water occurred at least 25% of the time even in the winter, and existed up to heights of 8 km. The vertically integrated mass of liquid was typically larger than that of ice. While it is generally difficult to evaluate the overall uncertainty of a comprehensive cloud retrieval system of this type, radiative flux closure analyses were performed where flux calculations using the derived microphysical properties were compared to measurements at the surface and top-of-atmosphere. Radiative closure biases were generally smaller for cloudy scenes relative to clear skies, while the variability of flux closure results was only moderately larger than under clear skies. The best closure at the surface was obtained for liquid-containing clouds. Radiative closure results were compared to those based on a similar, yet simpler, cloud retrieval system. These comparisons demonstrated the importance of accurate cloud phase classification, and specifically the identification of liquid water, for determining radiative fluxes. Enhanced retrievals of liquid water path for thin clouds were also shown to improve radiative flux calculations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Investigation of the Residual in Column-Integrated Atmospheric Energy Balance Using Cloud Objects

Observationally based atmospheric energy balance is analyzed using Clouds and the Earth’s Radiant Energy System (CERES)-derived TOA and surface irradiance, Global Precipitation Climatology Project (GPCP)-derived precipitation, dry static and kinetic energy tendency and divergence estimated from ERA-Interim, and surface sensible heat flux from SeaFlux. The residual tends to be negative over the tropics and positive over midlatitudes. A negative residual implies that the precipitation rate is too small, divergence is too large, or radiative cooling is too large. The residual of atmospheric energy is spatially and temporally correlated with cloud objects to identify cloud types associated with the residual. Spatially, shallow cumulus, cirrostratus, and deep convective cloud-object occurrence are positively correlated with the absolute value of the residual. The temporal correlation coefficient between the number of deep convective cloud objects and individual energy components, net atmospheric irradiance, precipitation rate, and the sum of dry static and kinetic energy divergence and their tendency over the western Pacific are 0.84, 0.95, and 0.93, respectively. However, when all energy components are added, the atmospheric energy residual over the tropical Pacific is temporally correlated well with the number of shallow cumulus cloud objects over tropical Pacific. Because shallow cumulus alters not enough atmospheric energy compared to the residual, this suggests the following: 1) if retrieval errors associated with deep convective clouds are causing the column-integrated atmospheric energy residual, the errors vary among individual deep convective clouds, and 2) it is possible that the residual is associated with processes in which shallow cumulus clouds affect deep convective clouds and hence atmospheric energy budget over the tropical western Pacific.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Sensitivity of Arctic Clouds to Ice Microphysical Processes in the NorESM2 Climate Model

Abstract Ice formation remains one of the most poorly represented microphysical processes in climate models. While primary ice production (PIP) parameterizations are known to have a large influence on the modeled cloud properties, the representation of secondary ice production (SIP) is incomplete and its corresponding impact is therefore largely unquantified. Furthermore, ice aggregation is another important process for the total cloud ice budget, which also remains largely unconstrained. In this study, we examine the impact of PIP, SIP, and ice aggregation on Arctic clouds, using the Norwegian Earth System Model, version 2 (NorESM2). Simulations with both prognostic and diagnostic PIP show that heterogeneous freezing alone cannot reproduce the observed cloud ice content. The implementation of missing SIP mechanisms (collisional breakup, drop shattering, and sublimation breakup) in NorESM2 improves the modeled ice properties, while improvements in liquid content occur only in simulations with prognostic PIP. However, results are sensitive to the description of collisional breakup. This mechanism, which dominates SIP in the examined conditions, is very sensitive to the treatment of the sublimation correction factor, a poorly constrained parameter that is included in the utilized parameterization. Finally, variations in ice aggregation treatment can also significantly impact cloud properties, mainly through their impact on collisional breakup efficiency. Overall, enhancement in ice production through the addition of SIP mechanisms and the reduction in ice aggregation (in line with radar observations of shallow Arctic clouds) result in enhanced cloud cover and decreased TOA radiation biases, compared to satellite measurements, especially during the cold months. Significance Statement Arctic clouds remain a large source of uncertainty in projections of the future climate due to the poor representation of the microphysical processes that govern their life cycle. Ice formation is among the least understood processes. While it is widely recognized that better constraints on primary ice production (PIP) are needed to improve existing parameterizations, we show that secondary ice production (SIP) and ice aggregation can have also a significant impact on ice number concentrations. Constraining ice formation through the addition of missing SIP mechanisms and reducing ice aggregation can improve the representation of the cloud macrophysical properties and enhance total cloud cover in the Arctic region, which in turn contributes to decreased TOA radiation biases in the cold months.

Meteorology & Atmospheric Sciences↗
DOE OSTI2020

Separating Cloud and Drizzle Signals in Radar Doppler Spectra Using a Parametric Time Domain Method

The ability to separate cloud and drizzle returns in active remote sensing observations is important for understanding the microphysics of clouds and precipitation. Yet, robust separations remain challenging in radar remote sensing. Prior methods for cloud and drizzle separation for radar observations use the properties of the Doppler spectra such as skewness. However, these methods have challenges when the drizzle becomes dominant in the observation volume. This paper presents a parametric time domain method (PTDM) that separates cloud and drizzle using the Doppler spectra measurements without assuming any prior properties of cloud and drizzle. The advantage of PTDM is that it can estimate the signal properties in the time domain and can obtain the cloud and drizzle estimates simultaneously. Based on our radar signal simulations, the uncertainty in estimated power and velocity from PTDM are within 2 dB and 0.02 m s -1 , respectively. We have also evaluated the PTDM algorithm using observations from the Atmospheric Radiation Measurement (ARM) Program W-band cloud radar in the Clouds, Aerosols, and Precipitation in the Marine Boundary Layer (CAP-MBL) campaign at the Azores in 2009–10. Two cases corresponding to light and moderate drizzling conditions are considered for the study. The statistics of the estimates obtained show that the PTDM method performs well in separating the cloud and drizzle returns. Finally, the estimated cloud and drizzle reflectivity from PTDM were used to retrieve their corresponding microphysical properties, showing that the retrieved liquid water path agrees to 25 g m -2 with the benchmark microwave method.

Engineering↗
DOE OSTI2014

Understanding and Improving CRM and GCM Simulations of Cloud Systems with ARM Observations

The works supported by this ASR project lay the solid foundation for improving the parameterization of convection and clouds in the NCAR CCSM and the climate simulations. We have made a significant use of CRM simulations and ARM observations to produce thermodynamically and dynamically consistent multi-year cloud and radiative properties; improve the GCM simulations of convection, clouds and radiative heating rate and fluxes using the ARM observations and CRM simulations; and understand the seasonal and annual variation of cloud systems and their impacts on climate mean state and variability. We conducted multi-year simulations over the ARM SGP site using the CRM with multi-year ARM forcing data. The statistics of cloud and radiative properties from the long-term CRM simulations were compared and validated with the ARM measurements and value added products (VAP). We evaluated the multi-year climate simulations produced by the GCM with the modified convection scheme. We used multi-year ARM observations and CRM simulations to validate and further improve the trigger condition and revised closure assumption in NCAR GCM simulations that demonstrate the improvement of climate mean state and variability. We combined the improved convection scheme with the mosaic treatment of subgrid cloud distributions in the radiation scheme of the GCM. The mosaic treatment of cloud distributions has been implemented in the GCM with the original convection scheme and enables the use of more realistic cloud amounts as well as cloud water contents in producing net radiative fluxes closer to observations. A physics-based latent heat (LH) retrieval algorithm was developed by parameterizing the physical linkages of observed hydrometeor profiles of cloud and precipitation to the major processes related to the phase change of atmospheric water.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE) Science Plan

Cumulus convection is an important component in the atmospheric radiation budget and hydrologic cycle over the Southern Great Plains and over many regions of the world, particularly during the summertime growing season when intense turbulence induced by surface radiation couples the land surface to clouds. Current convective cloud parameterizations contain uncertainties resulting in part from insufficient coincident data that couples cloud macrophysical and microphysical properties to inhomogeneities in boundary layer and aerosol properties. The Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE) campaign is designed to provide a detailed set of measurements that are needed to obtain a more complete understanding of the life cycle of shallow clouds by coupling cloud macrophysical and microphysical properties to land surface properties, ecosystems, and aerosols. HI-SCALE consists of 2, 4-week intensive observational periods, one in the spring and the other in the late summer, to take advantage of different stages and distribution of “greenness” for various types of vegetation in the vicinity of the Atmospheric Radiation and Measurement (ARM) Climate Research Facility’s Southern Great Plains (SGP) site as well as aerosol properties that vary during the growing season. Most of the proposed instrumentation will be deployed on the ARM Aerial Facility (AAF) Gulfstream 1 (G-1) aircraft, including those that measure atmospheric turbulence, cloud water content and drop size distributions, aerosol precursor gases, aerosol chemical composition and size distributions, and cloud condensation nuclei concentrations. Routine ARM aerosol measurements made at the surface will be supplemented with aerosol microphysical properties measurements. The G-1 aircraft will complete transects over the SGP Central Facility at multiple altitudes within the boundary layer, within clouds, and above clouds.

Fast, JD↗
DOE OSTI2019

3D Shortwave Radiative Kernels of Marine Boundary-layer Clouds Using Scanning Radar/Lidar and Array Spectroradiometer

This project set out to use synergetic measurements from the ARM program to observe 3D cloud field observations, and to investigate how inadequate treatment of the observed 3D characteristics of clouds in models may impact estimates of low-cloud feedback magnitude. The project comprises three components, including 1) retrieving cloud and precipitation properties, 2) developing fast radiative transfer code incorporating 3D cloud effects, and 3) estimating 3D effects/impacts on a global scale. This report documents our main research outcomes. Specifically, a novel cloud retrieval method is developed, allowing us to better characterize cloud population and sizes, drizzle properties and precipitation formation, and to better understand their interactions with aerosols. These observed 3D cloud fields were used in a fast radiative transfer code, helping discover non-negligible 3D radiative effects in the longwave spectral region. The retrieved cloud properties were also used to investigate several hypothesized cloud feedback processes.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Evaluation of NCAR CAM5 simulated marine boundary layer cloud properties using a combination of satellite and surface observations

Marine boundary layer (MBL) clouds are a strong modulator of Earth’s radiative energy budget. The feedback of MBL clouds in a changing climate remains one of the largest uncertainties in our understanding of climate sensitivity, and recent studies have also suggested that radiative feedbacks related to MBL clouds may play an important role in low-frequency climate variabilities such as the Atlantic multidecadal oscillation. Despite their importance in the climate system, simulating MBL clouds in Earth system models (ESMs) has proved to be extremely challenging. The overarching objective of this proposal is to evaluate the MBL cloud simulation skills of the CAM5 with different cloud parameterization schemes through systematic comparison with both ground- and satellite based cloud observations. In particular, we seek to understand the advantages and limitations of the newly developed subgrid cloud parameterization scheme CLUBB (Cloud Layers Unified By Binormals) for simulation MBL clouds through comparisons of CAM5-CLUBB simulations with ARM (CAP-MBL campaign data) and satellite observations, as well as the “conventional” CAM5 and a CAM5 with the so-called super-parameterization (i.e., CAM5-MMF). In the rest of this report, we will briefly highlight our major research achievements.

58 GEOSCIENCES↗
DOE OSTI2020

Improving GCM Predictability of Mixed-Phase Clouds and Aerosol Interactions at High Latitudes with ARM Observations

The overachieving goal of this project is to improve the predictability of mixed-phase clouds and aerosol interactions in the Community Atmosphere Model version 6 (CAM6) through comparison with the ARM observations. There are three main objectives of the proposed study: (1) Improve the representation of ice microphysical processes in mixed-phase clouds; (2) Test the performance of ice microphysics in CESM-CAM6 with the ARM observations in northern and southern high latitudes; and (3) Examine mixed-phase cloud microphysics-aerosol-turbulence-radiation interactions in CESM-CAM6. In this project, we have (1) Improved the representation of ice microphysical processes in mixed-phase clouds in CESM-CAM6 by implementing the marine organic aerosol (MOA) and treating the ice nucleating particles (INPs) from MOA and its impacts on mixed-phase clouds. We improved the treatment of ice depositional growth through the Wegener–Bergeron–Findeisen (WBF) process by considering the subgrid heterogeneous distributions between liquid droplets and ice crystals in mixed-phase clouds; (2) Tested the performance of ice microphysics in CESM-CAM6 with the ARM observations at high latitudes. We compared the simulated INP concentrations with the ARM observations, e.g., from M-PACE, ISDAC, INPOP, and other data (Mace Head, Zeppelin, CAPRICORN). We examined the impact of improved WBF treatment on model simulated Arctic mixed-phase clouds observed in the M-PACE field campaign. Seasonal variations of modeled mixed-phase cloud properties (LWO, IWP) are compared with the ground-based remote sensing retrievals at the ARM’s NSA $Utqia\dot{g}vik$ site; and (3) Examined mixed-phase cloud microphysics-aerosol-dynamics-radiation interactions in CESM-CAM6 that include the impacts of MOA INPs, and impacts of different model parameterizations (CLUBB versus UW turbulence & shallow convection schemes, MG2 versus MG1) on high-latitude mixed-phase cloud properties. Aerosol indirect effects of MOA through the liquid phase (droplet activation) and ice phase processes (e.g., the glaciation indirect effect) were investigated.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Beirut Explosion Yield and Mushroom Cloud Height - Effects of the Source Environment

I use crater dimensions to estimate the yield of the August 4th, 2020 Beirut explosion to be equivalent to approximately 1.4 kilotons of TNT with a lower bound of about 0.7 kilotons. Based on the amount of ammonium nitrate reported to have been stored at the Beirut harbor, I assume an upper bound for the yield of 2.75 kilotons. However, it is highly likely that the yield was less than 2.75 kilotons, since reported values for TNT equivalence of ammonium nitrate are typically much less than one hundred percent. The crater-size based yield estimates are based on crater radius estimates from satellite imagery and empirical curves and data for scaled crater radius from past chemical and nuclear explosions. I present evidence that suggests that the relatively large crater radius is due to a high degree of coupling of shock wave energy to the surrounding medium and a reduction of the effective stress because of a high level of saturation of the geologic media beneath the explosion. I provide yield estimates based on seismic body-wave magnitude and crater depth as corroborating evidence. I compare preliminary estimates for the maximum debris cloud height, based on cell phone videos/images, with predicted maximum heights for this yield range from empirical formulas and numerical cloud-rise models. Based on a preliminary analysis of cell phone footage, the observed maximum cloud height appears to be approximately 1600 m. This is much lower than that predicted using standard empirical formulas and buoyant cloud rise models. I present results from a modified buoyant cloud rise model that more accurately predicts the maximum cloud height by allowing for the inclusion of a fixed amount of air and/or water into the fireball at the start of cloud rise. The amount of mass that needs to be added at the start, to reproduce the observed maximum cloud height, is relatively small compared to the total mass entrained during cloud rise. A much greater amount of dry air or debris is required, relative to water, for an equivalent reduction in maximum cloud height. The ammonium nitrate is one possible source for water in the fireball since it was being stored in a very humid environment and ammonium nitrate is known to be hygroscopic. The ground beneath the explosion, especially if it were saturated, and the nearby harbor could also have been sources for water or debris in the fireball.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

Three-Channel Sunphotometer Cloud Mode Value-Added Product Report

A primary source of uncertainty in Earth system model (ESM) predictions is the representation of cloud processes and associated cloud feedback. Several fundamental cloud properties critical to the understanding of aerosol-cloud interactions are poorly constrained by observations, with key deficiencies in our observations of cloud and precipitation droplet sizes and cloud optical depth. Observations of these cloud properties are often challenging to estimate from remote-sensing platforms and costly to obtain from in situ aircraft. Nevertheless, observations of boundary-layer clouds, and improved knowledge of stratocumulus cloud (Sc) processes, are especially important to ESM advancement. This is because these clouds have extensive coverage and exert controls on boundary-layer dynamics and the global radiative energy balance.

54 ENVIRONMENTAL SCIENCES↗