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At least 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Chitosan Coating Incorporated with Carvacrol Improves Postharvest Guava (Psidium guajava) Quality

Guava (Psidium guajava L.) is an important economic crop grown widely in tropical and subtropical regions. Guava exhibits fast ripening and senescence as a climacteric fruit, causing a short shelf life and quality deterioration. Chitosan–essential oil nanoemulsions can be an edible coating used to improve postharvest quality attributes. In this study, chitosan was mixed with carvacrol to generate a nano-emulsoid solution containing 0.1 and 0.2% (v/v) carvacrol, using a sonic dismembrator. Guava fruit were coated with the above emulsion and postharvest quality parameters were measured during storage at 20 ± 1 °C and RH = 80 ± 5% for 8 days. The result illustrated that the particle size of the chitosan–carvacrol emulsions was nanoscale, and their high stability was demonstrated by the zeta potential and polydispersity index. Chitosan coating (2%, w/v, 310–375 kDa) containing 0.2% (v/v) carvacrol maintained postharvest quality compared to chitosan alone, with higher firmness, soluble solid content, total acid, and total phenol content, and lower weight loss and pericarp browning. The collective data were further verified by principal component analysis. A chitosan coating containing carvacrol can reduce postharvest losses. It can be applied as an effective strategy to improve postharvest fruit quality.

Agriculture↗
DOE OSTI2020

Advances in Atomic Layer Deposition (ALD) Nanolaminate Synthesis of Thermoelectric Films in Porous Templates for Improved Seebeck Coefficient

Thermoelectrics is a green renewable energy technology which can significantly contribute to power generation due to its potential in generating electricity out of waste heat. The main challenge for the development of thermoelectrics is its low conversion efficiency. One key strategy to improve conversion efficiency is reducing the thermal conductivity of thermoelectric materials. In this paper, the state-of-the-art progresses made in improving thermoelectric materials are reviewed and discussed, focusing on phononic engineering via applying porous templates and ALD deposited nanolaminates structure. The effect of nanolaminates structure and porous templates on Seebeck coefficient, electrical conductivity and thermal conductivity, and hence in figure of merit zT of different types of materials system, including PnCs, lead chalcogenide-based nanostructured films on planar and porous templates, ZnO-based superlattice, and hybrid organic-inorganic superlattices, will be reviewed and discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

On the Nature of Extra-Framework Aluminum Species and Improved Catalytic Properties in Steamed Zeolites

Steamed zeolites exhibit improved catalytic properties for hydrocarbon activation (alkane cracking and dehydrogenation). The nature of this practically important phenomenon has remained a mystery for the last six decades and was suggested to be related to the increased strength of zeolitic Bronsted acid sites after dealumination. We now utilize state-of-the-art infrared spectroscopy measurements and prove that during steaming, aluminum oxide clusters evolve (due to hydrolysis of Al out of framework positions with the following clustering) in the zeolitic micropores with properties very similar to (nano) facets of hydroxylated transition alumina surfaces. The Bronsted acidity of the zeolite does not increase and the total number of Bronsted acid sites decreases during steaming. O 5 Al(VI)-OH surface sites of alumina clusters dehydroxylate at elevated temperatures to form penta-coordinate Al 1 O 5 sites that are capable of initiating alkane cracking by breaking the first C-H bond very effectively with much lower barriers (at lower temperatures) than for protolytic C-H bond activation, with the following reaction steps catalyzed by nearby zeolitic Bronsted acid sites. This explains the underlying mechanism behind the improved alkane cracking and alkane dehydrogenation activity of steamed zeolites: heterolytic C-H bond breaking occurs on Al-O sites of aluminum oxide clusters confined in zeolitic pores. Our findings explain the origin of enhanced activity of steamed zeolites at the molecular level and provide the missing understanding of the nature of extra-framework Al species formed in steamed/dealuminated zeolites.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Integrating NDVI-Based Within-Wetland Vegetation Classification in a Land Surface Model Improves Methane Emission Estimations

Earth system models (ESMs) are a common tool for estimating local and global greenhouse gas emissions under current and projected future conditions. Efforts are underway to expand the representation of wetlands in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) by resolving the simultaneous contributions to greenhouse gas fluxes from multiple, different, sub-grid-scale patch-types, representing different eco-hydrological patches within a wetland. However, for this effort to be effective, it should be coupled with the detection and mapping of within-wetland eco-hydrological patches in real-world wetlands, providing models with corresponding information about vegetation cover. In this short communication, we describe the application of a recently developed NDVI-based method for within-wetland vegetation classification on a coastal wetland in Louisiana and the use of the resulting yearly vegetation cover as input for ELM simulations. Processed Harmonized Landsat and Sentinel-2 (HLS) datasets were used to drive the sub-grid composition of simulated wetland vegetation each year, thus tracking the spatial heterogeneity of wetlands at sufficient spatial and temporal resolutions and providing necessary input for improving the estimation of methane emissions from wetlands. Our results show that including NDVI-based classification in an ELM reduced the uncertainty in predicted methane flux by decreasing the model’s RMSE when compared to Eddy Covariance measurements, while a minimal bias was introduced due to the resampling technique involved in processing HLS data. Our study shows promising results in integrating the remote sensing-based classification of within-wetland vegetation cover into earth system models, while improving their performances toward more accurate predictions of important greenhouse gas emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

MTL_TX: A Multi-Task Transformer Model for Improved Radiation Time-Series Estimation

Controlling radiation doses at potential radioactive facilities is critical to ensuring the safety of both personnel and the public. At the Thomas Jefferson National Accelerator Facility (JLab), multiple sensors are deployed around the three experimental halls to monitor key parameters, including single-beam current, energy levels, current leakage, and radiation values during accelerator operations. In this study, we developed a Multi-task Transformer model, MTL_TX, to accurately estimate radiation doses at sensor locations based on historical data, with the aim of enhancing safety in accelerator facilities and surrounding public areas. To improve estimation accuracy, we integrated two innovative components into the proposed model: hierarchical feature embedding (HFE) and multi-level decomposition attention (MDA). Additionally, the multi-task learning (MTL) framework effectively leverages correlations among multiple sensors, enabling individual estimations for each sensor. MTL_TX achieved outstanding results on data collected in 2018, with an MSE of 0.1464, an RMSE of 0.2353, and an R 2 score of 0.8584. Furthermore, when trained on 2018 data, MTL_TX exhibited excellent generalization capability to unseen datasets from 2016 to 2019, achieving an MSE of 0.1407, an RMSE of 0.2263, and an R 2 score of 0.8831. These results demonstrate a significant improvement over existing state-of-the-art models.

Transformer↗
DOE OSTI2025

A Pentavalent HIV-1 Subtype C Vaccine Containing Computationally Selected gp120 Strains Improves the Breadth of V1V2 Region Responses

Background: HIV-1 envelope (Env) variable loops 1 and 2 (V1V2) directed non-neutralizing antibodies were a correlate of decreased transmission risk in the RV144 vaccine trial. Thus, the elicitation and breadth of antibody responses against the V1V2 of HIV-1 Env are important considerations for HIV-1 vaccine candidates. The V1V2 region’s highly variable nature and the extensive diversity of subtype C HIV-1 Envelopes (Envs) make the V1V2 response breadth a high priority for HIV-1 vaccine regimens aiming for V1V2-mediated protection in Southern Africa. Here, we determined whether the breadth of the anti-V1V2 vaccine response can be broadened by including HIV-1 Env strains computationally designed to enhance the coverage of subtype C V1V2 sequence diversity. Methods: Three subtype C Env strains were selected to maximize antibody binding coverage while complementing subtype C vaccine gp120s that were given in human clinical trials in South Africa, as well as to improve epitope accessibility. Humoral immunogenicity of a novel trivalent gp120 vaccine immunogen, a bivalent gp120 boost already in clinical trials (1086C and TV1), and a pentavalent (all five gp120s combined) were evaluated in a preclinical immunization study in guinea pigs. The pentavalent combination was further evaluated with alum versus glucopyranosyl lipid adjuvants formulated in squalene-in-water emulsion (GLA-SE) adjuvants in non-human primates. The breadth of the anti-V1V2 response was assessed using an array of cross-subtype variable loops 1&2 (V1V2) scaffold proteins and linear V2 peptides. Results: The breadth of the IgG response against V1V2 antigens of the trivalent and pentavalent groups was comparable, and both were greater than the breadth of the bivalent group. Linear epitope mapping showed that two linear epitopes in V2 were targeted by the vaccinated animals: the V2 hotspot focused at 169K that potentially correlated with decreased HIV-1 risk in RV144 and the V2.2 site (179LDV/I181) that is part of the integrin α4β7 binding site. The bivalent vaccine elicited a significantly higher magnitude of binding to the V2 hotspot compared to the trivalent vaccine whereas the trivalent vaccine elicited significantly higher binding to the V2.2 epitope compared to the bivalent vaccine, while the pentavalent recognized both regions. Conclusions: These results demonstrate that the three new computationally selected subtype C Envs successfully complemented 1086C and TV1 for broader V1V2 antibody responses, and, in concert with adjuvants that stimulate V1V2 responses, can be considered as part of a rationale immunogen design to improve V1V2 IgG coverage in future vaccine trials in South Africa.

Immunology↗
DOE OSTI2024

Methodology for Exploring Water and Hydropower Operating Criteria That Simultaneously Improve Economic and Environmental Considerations

Despite the reliability and flexibility of hydropower, the operation of hydroelectric power plants may have significant impacts on the downstream river system, including its water stage, sediment transport, and water temperature, ultimately affecting the ecology. To address these challenges, there is a need to identify water scheduling patterns that improve both hydropower economics and the environment relative to current operations. This paper presents a new methodology to explore promising operational criteria/rules that can achieve such improvements. Typical environmental impact statements and relicensing processes generally perform detailed site-specific analyses of a few alternatives that focus on reservoir water release operating rules and their associated environmental impacts. In contrast, the methodology presented in this article uses a widely applicable approach that explores a much larger solution space. This large set of potential alternatives can be represented in a multidimensional space for which one axis represents the economic value and the other axis quantify individual environmental impacts (e.g., sediment transport and fish growth), and they are explored via two approaches: a Monte Carlo simulation that identifies “win–win” alternatives and a multi-objective optimization problem that identifies Pareto-optimal alternatives.

13 HYDRO ENERGY↗
DOE OSTI2023

L-dwarf Detection from SDSS Images using Improved Faster R-CNN

We present a data-driven approach to automatically detect L dwarfs from Sloan Digital Sky Survey (SDSS) images using an improved Faster R-CNN framework based on deep learning. The established L-dwarf automatic detection (LDAD) model distinguishes L dwarfs from other celestial objects and backgrounds in SDSS field images by learning the features of 387 SDSS images containing L dwarfs. Applying the LDAD model to the SDSS images containing 93 labeled L dwarfs in the test set, we successfully detected 83 known L dwarfs with a recall rate of 89.25% for known L dwarfs. Several techniques are implemented in the LDAD model to improve its detection performance for L dwarfs, including the deep residual network and the feature pyramid network. As a result, the LDAD model outperforms the model of the original Faster R-CNN, whose recall rate of known L dwarfs is 80.65% for the same test set. The LDAD model was applied to detect L dwarfs from a larger validation set including 843 labeled L dwarfs, resulting in a recall rate of 94.42% for known L dwarfs. The newly identified candidates include L dwarfs, late M and T dwarfs, which were estimated from color (i – z) and spectral type relation. The contamination rates for the test candidates and validation candidates are 8.60% and 9.27%, respectively. The detection results indicate that our model is effective to search for L dwarfs from astronomical images.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

The Twins Embedding of Type Ia Supernovae. II. Improving Cosmological Distance Estimates

We show how spectra of Type Ia supernovae (SNe Ia) at maximum light can be used to improve cosmological distance estimates. In a companion article, we used manifold learning to build a three-dimensional parameterization of the intrinsic diversity of SNe Ia at maximum light that we call the "Twins Embedding."In this article, we discuss how the Twins Embedding can be used to improve the standardization of SNe Ia. With a single spectrophotometrically calibrated spectrum near maximum light, we can standardize our sample of SNe Ia with an rms of 0.101 0.007 mag, which corresponds to 0.084 0.009 mag if peculiar velocity contributions are removed and to 0.073 0.008 mag if a larger reference sample were obtained. Our techniques can standardize the full range of SNe Ia, including those typically labeled as peculiar and often rejected from other analyses. We find that traditional light-curve width + color standardization such as SALT2 is not sufficient. The Twins Embedding identifies a subset of SNe Ia, including, but not limited to, 91T-like SNe Ia whose SALT2 distance estimates are biased by 0.229 0.045 mag. Standardization using the Twins Embedding also significantly decreases host-galaxy correlations. We recover a host mass step of 0.040 0.020 mag compared to 0.092 0.026 mag for SALT2 standardization on the same sample of SNe Ia. These biases in traditional standardization methods could significantly impact future cosmology analyses if not properly taken into account.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

SALT3: An Improved Type Ia Supernova Model for Measuring Cosmic Distances

Abstract A spectral-energy distribution (SED) model for Type Ia supernovae (SNe Ia) is a critical tool for measuring precise and accurate distances across a large redshift range and constraining cosmological parameters. We present an improved model framework, SALT3, which has several advantages over current models—including the leading SALT2 model (SALT2.4). While SALT3 has a similar philosophy, it differs from SALT2 by having improved estimation of uncertainties, better separation of color and light-curve stretch, and a publicly available training code. We present the application of our training method on a cross-calibrated compilation of 1083 SNe with 1207 spectra. Our compilation is 2.5× larger than the SALT2 training sample and has greatly reduced calibration uncertainties. The resulting trained SALT3.K21 model has an extended wavelength range 2000–11,000 Å (1800 Å redder) and reduced uncertainties compared to SALT2, enabling accurate use of low- z I and iz photometric bands. Including these previously discarded bands, SALT3.K21 reduces the Hubble scatter of the low- z Foundation and CfA3 samples by 15% and 10%, respectively. To check for potential systematic uncertainties, we compare distances of low (0.01 < z < 0.2) and high (0.4 < z < 0.6) redshift SNe in the training compilation, finding an insignificant 3 ± 14 mmag shift between SALT2.4 and SALT3.K21. While the SALT3.K21 model was trained on optical data, our method can be used to build a model for rest-frame NIR samples from the Roman Space Telescope. Our open-source training code, public training data, model, and documentation are available at https://saltshaker.readthedocs.io/en/latest/ , and the model is integrated into the sncosmo and SNANA software packages.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Improving White Dwarfs as Chronometers with Gaia Parallaxes and Spectroscopic Metallicities

White dwarfs (WDs) offer unrealized potential in solving two problems in astrophysics: stellar age accuracy and precision. WD cooling ages can be inferred from surface temperatures and radii, which can be constrained with precision by high-quality photometry and parallaxes. Accurate and precise Gaia parallaxes along with photometric surveys provide information to derive cooling and total ages for vast numbers of WDs. Here we analyze 1372 WDs found in wide binaries with main-sequence (MS) companions and report on the cooling and total age precision attainable in these WD+MS systems. The total age of a WD can be further constrained if its original metallicity is known because the MS lifetime depends on metallicity at fixed mass, yet metallicity is unavailable via spectroscopy of the WD. We show that incorporating spectroscopic metallicity constraints from 38 wide binary MS companions substantially decreases internal uncertainties in WD total ages compared to a uniform constraint. Averaged over the 38 stars in our sample, the total (internal) age uncertainty improves from 21.04% to 16.77% when incorporating the spectroscopic constraint. Higher mass WDs yield better total age precision; for eight WDs with zero-age MS masses ≥2.0 M ⊙ , the mean uncertainty in total ages improves from 8.61% to 4.54% when incorporating spectroscopic metallicities. We find that it is often possible to achieve 5% total age precision for WDs with progenitor masses above 2.0 M ⊙ if parallaxes with ≤1% precision and Pan-STARRS g, r, and i photometry with ≤0.01 mag precision are available.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Improved Models of R Coronae Borealis Stars

We present an improved numerical method to model subsolar He+CO-WD merger progenitors of R Corona Borealis stars that builds on our previous work. These improvements include a smooth entropy transition from the core to the envelope of the post-merger, inclusion of single-zone nucleosynthesis to mimic the effects of burning during the merger event, and post-processing the models with a larger nuclear network for analysis of s-process nucleosynthesis. We perform a parameter study to understand the effects of the entropy transition, peak temperature, and overshooting on our models. The models that best agree with observations of R Corona Borealis stars are processed with a much larger nuclear network to investigate s-process nucleosynthesis and the dredge-up of s-process products into the outer envelope in detail. We present a model with a significant enhancement in s-process elements, which also agrees with observed surface abundances and isotopic ratios of 16 O/ 16 O and C/O between 1 and 10. Finally, we find that the neutron exposure and initial neutron densities this model requires to obtain such an enhancement are much more consistent with i-process nucleosynthesis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Improved Spectral Classification of Local K/M Dwarfs in SDSS Surveys. I. Chemodynamic Validation and Metallicity Calibration

An examination of 109,276 spectra of low-mass stars in the Sloan Digital Sky Survey (SDSS) data archive, collected pre-2010, provides a broad collection of K/M (sub)dwarfs tracing the local (d ≲ 250 pc) population of the thin-disk, thick-disk, and halo, based on 3D kinematics. These populations have distinct metallicities and kinematics, which should be reflected in lower-mass members. One complication is measuring metallicities of M dwarfs from low-resolution spectroscopy or photometry alone remains a challenge, even with the availability of Gaia data. To better characterize physical parameters of low-mass stars observed by SDSS, we define a set of 536 improved, empirical K/M dwarf classification templates to more accurately determine spectral subtypes (K5.5–M8.5) and morphology classes (MCs) (0.5–12.5). We select the best-fit template to every archival SDSS spectrum in the range 5000–8000 Å based on minimum χ 2 value. We then use secondary metallicity estimators to calibrate the most likely [Fe/H] values corresponding to the assigned MC. We confirm that the most metal-rich K/M dwarfs have thin-disk kinematics, and we observe the known effect of Galactic radial metallicity migration, which we confirm is strongly imprinted in the chemodynamics of the local disk M dwarfs. Confirmation of these chemodynamic trends validates the precision of our improved templates for estimating [Fe/H] for M (sub)dwarfs, notably at the low-metallicity end (–3 < [Fe/H] < –1). Substructure in the chemodynamics of the most metal-poor K/M subdwarfs begins to distinguish between local Gaia-Enceladus and local in situ halo objects. This methodology opens the door to use the ubiquitous, local K/M (sub)dwarfs to trace local Galactic chemodynamics with the highest possible resolution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Improving Photometric Redshift Estimates with Training Sample Augmentation

Abstract Large imaging surveys will rely on photometric redshifts (photo- z 's), which are typically estimated through machine-learning methods. Currently planned spectroscopic surveys will not be deep enough to produce a representative training sample for Legacy Survey of Space and Time (LSST), so we seek methods to improve the photo- z estimates that arise from nonrepresentative training samples. Spectroscopic training samples for photo- z 's are biased toward redder, brighter galaxies, which also tend to be at lower redshift than the typical galaxy observed by LSST, leading to poor photo- z estimates with outlier fractions nearly 4 times larger than for a representative training sample. In this Letter, we apply the concept of training sample augmentation, where we augment simulated nonrepresentative training samples with simulated galaxies possessing otherwise unrepresented features. When we select simulated galaxies with ( g - z ) color, i -band magnitude, and redshift outside the range of the original training sample, we are able to reduce the outlier fraction of the photo- z estimates for simulated LSST data by nearly 50% and the normalized median absolute deviation (NMAD) by 56%. When compared to a fully representative training sample, augmentation can recover nearly 70% of the degradation in the outlier fraction and 80% of the degradation in NMAD. Training sample augmentation is a simple and effective way to improve training samples for photo- z 's without requiring additional spectroscopic samples.

Moskowitz, Irene (ORCID:0000000222068589)↗
DOE OSTI2020

Improving Heat Conduction of Insulated Metal Substrate with Thermal Pyrolytic Graphite Core for SiC Power Module Packaging

TPG-core IMS concept was jointly explored in this study. Integrating high thermally conductive TPG graphite core into IMS is expected to simultaneously achieve high thermal conductivity from TPG and electrical functionalities from IMS. Nearly 2x thermal conductivity and 30% weight saving was demonstrated on TPG-core IMS compared to conventional Cu-core IMS. Significant junction temperature reduction (11°C) in steady state and power cycling was revealed by the thermal analysis as the result of improved thermal spreading in plane and through the thickness. The study also proved the manufacturability and compatibility of this TPG-core IMS structure to the existing IMS production and power module assembling. Finally, the integration of TPG and IMS paves a new packaging route to increase heat load, improve reliability, simplify module design and reduce assembling cost and number of steps.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Ginsparg-Wilson Hamiltonians with Improved Chiral Symmetry

We construct a family of Ginsparg-Wilson Hamiltonians with improved chiral properties, starting from a construction of Creutz-Horvath-Neuberger that provides a doubler-free Hamiltonian lattice regularization for Dirac fermions in even spacetime dimensions. We use a higher-order generalization of the Ginsparg-Wilson relation due to Fujikawa, which yields an order-$k$ Hamiltonian overlap operator for each integer $k \geq 0$, with an exactly conserved but nonquantized chiral charge that becomes quantized as $k \to \infty$. Our construction provides physical insight into how Fujikawa's higher-order Ginsparg-Wilson relation improves chiral symmetry while reproducing the anomaly, highlighting the trade-offs inherent in any Hamiltonian lattice realization of an anomalous chiral symmetry. This class of Hamiltonian lattice regularizations, with their tunable chiral symmetry properties, offers potential advantages for quantum and tensor-network simulations.

Singh, Hersh [Fermilab]↗
DOE OSTI2023

Substantially positive contributions of new particle formation to cloud condensation nuclei under low supersaturation in China based on numerical model improvements

New particle formation (NPF) and subsequent particle growth are important sources of condensation nuclei (CN) and cloud condensation nuclei (CCN). While many observations have shown positive contributions of NPF to CCN at low supersaturation, negative NPF contributions were often simulated in polluted environments. Using the observations in a coastal city of Qingdao, Beijing, and Gucheng in north China, we thoroughly evaluate the simulated number concentrations of CN and CCN using an NPF-explicit parameterization embedded in the WRF-Chem model. For CN, the initial simulation shows large biases of particle number concentrations at 10–40 and 40–100 nm. By adjusting the process of gas–particle partitioning, including the mass accommodation coefficient (MAC) of sulfuric acid, the phase changes in primary organic aerosol emissions, and the condensational amount of nitric acid, the improvement of the particle growth process yields substantially reduced overestimation of CN. Regarding CCN, secondary organic aerosol (SOA) formed from the oxidation of semi-volatile and intermediate-volatility organic compounds (S/IVOCs) is called SI-SOA, the yield of which is an important contributor. At default settings, the SI-SOA yield is too high without considering the differences in precursor oxidation rates. Lowering the SI-SOA yield under linear H 2 SO 4 nucleation scheme results in much-improved CCN simulations compared to observations. On the basis of the bias-corrected model, we find substantially positive contributions of NPF to CCN at low supersaturation (~ 0.2 %) over broad areas of China, primarily due to competing effects of increasing particle hygroscopicity, a result of reductions in SI-SOA amount, surpassing that of particle size decreases. The bias-corrected model is robustly applicable to other schemes, such as the quadratic H 2 SO 4 nucleation scheme, in terms of CN and CCN, though the dependence of CCN on SI-SOA yield is diminished likely due to changes in particle composition. This study highlights potentially much larger NPF contributions to CCN on a regional and even global basis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Improving the Confidence in Retrievals of Vertical Distributions of Cloud Condensation Nuclei Number Concentration from ARM Supported by Aircraft In Situ Observations

Accurate quantification of the vertical distribution of cloud condensation nuclei (CCN) number concentrations is critical for improving our understanding of aerosol–cloud interactions. Ground-based Raman lidars operated by the Atmospheric Radiation Measurement (ARM) program, together with surface CCN measurements, are used to retrieve vertically resolved CCN number concentrations (Retrieved Number concentration of CCN, RNCCN). These retrievals rely on several assumptions, including that aerosol composition is vertically homogeneous. To assess this assumption, we developed and tested a framework to infer the dominant aerosol classes/types at different altitudes. This was done by applying a k-Nearest-Neighbors (kNN) algorithm to lidar ratio and linear depolarization ratio measurements from Raman lidar. We evaluated the framework using aircraft aerosol and CCN measurements from the ARM Holistic Interactions of Shallow Clouds, Aerosols, and Land Ecosystems (HI-SCALE) field campaign. The results show that RNCCN performance degrades as vertical aerosol complexity increases, i.e., RNCCN agrees with the aircraft CCN in vertically homogeneous conditions, but closure decreases in layered aerosol structures. To generalize beyond individual examples, we introduce a metric (heterogeneity index) that quantifies the vertical complexity by assessing the variation of inferred aerosol classes/types. Case-level statistics show a tendency for RNCCN and aircraft differences to increase with this metric. By detecting retrievals that are likely compromised by aerosol vertical heterogeneity, the proposed framework improves the interpretability and effective use of RNCCN used for long-term evaluation of models and aerosol–cloud interactions.

Tian, Jingjing↗