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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Achieving record hot spot energies with large HDC implosions on NIF in HYBRID-E

HYBRID-E is an inertial confinement fusion implosion design that increases energy coupled to the hot spot by increasing the capsule scale in cylindrical hohlraums while operating within the current experimental limits of the National Ignition Facility. HYBRID-E reduces the hohlraum scale at a fixed capsule size compared to previous HYBRID designs, thereby increasing the hohlraum efficiency and energy coupled to the capsule, and uses the cross-beam energy transfer (CBET) to control the implosion symmetry by operating the inner (23° and 30°) and outer (44° and 50°) laser beams at different wavelengths (Δλ> 0). Small case to capsule ratio designs can suffer from insufficient drive at the waist of the hohlraum. We show that only a small amount of wavelength separation between the inner and outer beams (Δλ1-2 Å) is required to control the symmetry in low-gas-filled hohlraums (0.3 mg/cm3 He) with enough drive at the waist of the hohlraum to symmetrically drive capsules 1180 μm in outer radius. This campaign is the first to use the CBET to control the symmetry in 0.3 mg/cm3 He-filled hohlraums, the lowest gas fill density yet fielded with Δλ> 0. We find a stronger sensitivity of hot spot P2 in μm per Angstrom (40–50 μm/Å wavelength separation) than observed in high-gas-filled hohlraums and previous longer pulse designs that used a hohlraum gas fill density of 0.6 mg/cm3. There is currently no indication of transfer roll-off with increasing Δλ, indicating that even longer pulses or larger capsules could be driven using the CBET in cylindrical hohlraums. We show that the radiation flux symmetry is well controlled during the foot of the pulse, and that the entire implosion can be tuned symmetrically in the presence of the CBET in this system, with low levels of laser backscatter out of the hohlraum and low levels of hot electron production from intense laser–plasma interactions. Radiation hydrodynamic simulations can accurately represent the early shock symmetry and be used as a design tool, but cannot predict the late-time radiation flux symmetry during the peak of the pulse, and semi-empirical models are used to design the experiments. Deuterium–tritium (DT)-layered tests of 1100 μm inner radius implosions showed performance close to expectations from simulations at velocities up to ~360 km/s, and record yields at this velocity, when increasing the DT fuel layer thickness to mitigate hydrodynamic mixing of the ablator into the hot spot as a result of defects in the ablator. However, when the implosion velocity was increased, mixing due to these defects impacted performance. The ratio of measured to simulated yield for these experiments was directly correlated with the level of observed mixing. These simulations suggest that reducing the mixing, e.g., by improving the capsule defects, could result in higher performance. In addition, future experiments are planned to reduce the coast time at this scale, delay between the peak compression and the end of the laser, to increase the hot spot convergence and pressure. To reduce the coast time by several hundred ps compared to the 1100 μm inner radius implosions, HYBRID-E has also fielded 1050 μm inner radius capsules, which resulted in higher hot spot pressure and a fusion energy yield of ~170 kJ.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Benchmarking magnetised three-wave coupling for laser backscattering: analytic solutions and kinetic simulations

Understanding magnetised laser–plasma interactions is important for controlling magneto-inertial fusion experiments and developing magnetically assisted radiation and particle sources. For nanosecond pulses at non-relativistic intensities, interactions are dominated by coherent three-wave interactions, whose nonlinear coupling coefficients became known only recently when waves propagate at oblique angles with the magnetic field. In this paper, backscattering coupling coefficients predicted by warm-fluid theory are benchmarked using particle-in-cell simulations in one spatial dimension, and excellent agreements are found for a wide range of plasma temperatures, magnetic field strengths and laser propagation angles, when the interactions are mediated by electron-dominant hybrid waves. Systematic comparisons between theory and simulations are made possible by a rigorous protocol. On the theory side, the initial boundary value problem of linearised three-wave equations is solved, and the transient-time solutions allow the effects of growth and damping to be distinguished. On the simulation side, parameters are carefully chosen and calibration runs are performed to ensure that comparisons are well controlled. Fitting simulation data to analytical solutions yields numerical growth rates that match theory predictions within error bars. Although warm-fluid theory is found to be valid for a wide parameter range, genuine kinetic effects have also been observed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Aerosol and Cloud Optical Properties from the ARM Raman Lidars: The Feature Detection and Extinction (RLPROF-FEX) Value-Added Product

Aerosols and their interactions and influence on clouds are among the main sources of uncertainties in radiative direct and indirect forcing (IPCC 2013). Continuous height-resolved measurements of cloud and aerosol optical properties are needed to reduce these uncertainties. Here we describe the Raman Lidar Profiles – Feature detection and Extinction (RLPROF-FEX) Value-Added Product (VAP) derived using Raman lidar data at multiple U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility sites. RLPROF-FEX provides estimates of extinction, backscatter, and depolarization using the algorithm described by Thorsen et al. 2015 and Thorsen and Fu 2015. This document provides a description of the FEX algorithm, its input and output data, and related details about the Raman lidar (RL) system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Influence of dissolved salts and impurities on seawater on heat transfer and fluid flow through fuel channels and debris bed

This project aims to better understand this mechanism and its effect on heat transfer. Specifically, experimental research was conducted to investigate the long term coolability issues in scaled test setups. Advanced diagnostic and measurement techniques were investigated and incorporated into an annular test facility and a debris bed test facility. The diagnostic techniques investigated included: radioactively tagged salt solution, Frisch collar radiation detector array, neutron and x-ray imaging systems, and a Rayleigh backscatter-based fiber-optic temperature system. Models were checked against the in-situ experimental data and recommendations were made for modeling purposes.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Radiation Damage Mitigation in FeCrAl Alloy at Sub-Recrystallization Temperatures

Traditional defect recovery methods rely on high-temperature annealing, often exceeding 750 °C for FeCrAl. In this study, we introduce electron wind force (EWF)-assisted annealing as an alternative approach to mitigate irradiation-induced defects at significantly lower temperatures. FeCrAl samples irradiated with 5 MeV Zr 2+ ions at a dose of 10 14 cm −2 were annealed using EWF at 250 °C for 60 s. We demonstrate a remarkable transformation in the irradiated microstructure, where significant increases in kernel average misorientation (KAM) and low-angle grain boundaries (LAGBs) typically indicate heightened defect density; the use of EWF annealing reversed these effects. X-ray diffraction (XRD) confirmed these findings, showing substantial reductions in full width at half maximum (FWHM) values and a realignment of peak positions toward their original states, indicative of stress and defect recovery. To compare the effectiveness of EWF, we also conducted traditional thermal annealing at 250 °C for 7 h, which proved less effective in defect recovery as evidenced by less pronounced improvements in XRD FWHM values.

FeCrAl alloys↗
DOE OSTI2024

Using convolutional neural networks to detect edge localized modes in DIII-D from Doppler backscattering measurements

In H-mode tokamak plasmas, the plasma is sometimes ejected beyond the edge transport barrier. These events are known as edge localized modes (ELMs). ELMs cause a loss of energy and damage the vessel walls. Understanding the physics of ELMs, and by extension, how to detect and mitigate them, is an important challenge. In this paper, we focus on two diagnostic methods—deuterium-alpha (D α ) spectroscopy and Doppler backscattering (DBS). The former detects ELMs by measuring Balmer alpha emission, while the latter uses microwave radiation to probe the plasma. DBS has the advantages of having a higher temporal resolution and robustness to damage. These advantages of DBS diagnostic may be beneficial for future operational tokamaks, and thus, data processing techniques for DBS should be developed in preparation. In sight of this, we explore the training of neural networks to detect ELMs from DBS data, using D α data as the ground truth. With shots found in the DIII-D database, the model is trained to classify each time step based on the occurrence of an ELM event. The results are promising. When tested on shots similar to those used for training, the model is capable of consistently achieving a high f1-score of 0.93. Furthermore, this score is a performance metric for imbalanced datasets that ranges between 0 and 1. We evaluate the performance of our neural network on a variety of ELMs in different high confinement regimes (grassy ELM, RMP mitigated, and wide-pedestal), finding broad applicability. Beyond ELMs, our work demonstrates the wider feasibility of applying neural networks to data from DBS diagnostic.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Seasonal Variation of Dust Aerosol Vertical Distribution in Arctic Based on Polarized Micropulse Lidar Measurement

This study investigates the seasonal variation of dust aerosol vertical distribution using polarized Micropulse lidar (MPL) measurements at the Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) observatory from January 2013 to September 2017. For the first time, multi-year aerosol backscatter coefficients are retrieved at the ARM NSA site from MPL measurements and are consistent with co-located high spectral resolution lidar (HSRL) measurements. The high-quality aerosol backscatter coefficient retrievals are used to derive the particle depolarization ratio (PDR) at the wavelength of 532 nm, which is used to identify the presence of dust aerosols. The annual cycles of the vertical distributions of dust backscatter coefficient and PDR and dust aerosol optical depth (DAOD) show that aerosol loading has a maximum in late winter and early spring but a minimum in late summer and early autumn. Vertically, dust aerosol occurs in the entire troposphere in spring and winter and in the low and middle troposphere in summer and autumn. Because dust aerosols are effective ice nuclei, the seasonality of dust aerosol vertical distribution has important implications for the Arctic climate through aerosol–cloud–radiation interactions, primarily through impacting mixed-phase cloud processes.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Chiral Topological Surface States on a Finite Square Photonic Crystal Bounded by Air

Chiral, topologically protected, photonic surface states can be found at the boundary between gyrotropic photonic crystals where a changing magnetic field induces different topology across the interface. Typically, photonic crystals with either a suitable band structure on both sides of the interface to provide a band gap and evanescent decay of the surface states away from the interface, or an outer layer with engineered material properties is required. In this paper, we show the emergence of topological, unidirectional surface states at the termination of finite gyrotropic photonic crystals with a simple square lattice and C 4 rotational symmetry bounded by a vacuum, eliminating the need for an outside layer to enable chiral surface modes. Here, we start from an infinite, time-reversal-symmetry-breaking photonic crystal with a band gap associated with bands with nonzero Chern numbers, different from all-zero Chern numbers in air. We then modify the photonic crystal to move this band gap below the light line, while maintaining the Chern-number discontinuities. Band-structure calculations for a supercell approximating a photonic crystal finite in the direction normal to the surface demonstrate the existence, dispersion, and chirality of the surface mode. Extensive direct scattering calculations for a point source and spatial Fourier analysis further reveal a unidirectional free-space topological surface state, which propagates counterclockwise around the surface of a finite photonic crystal, providing a nearly foolproof way to cross-check the surface-mode band structure unaffected by backscattering from local defects. Additionally, scattering simulations allow an independent characterization of the state dispersion and unveil the robustness of the topological plasmonic mode propagation around the 90° bends of the structure, being due to only radiation leakage. In contrast to buried topological surface states, the observed surface modes at the photonic crystal–air interface have the advantage of being accessible to the outside world, allowing one to take advantage of the defect-tolerant backscattering-free surface modes to engineer emission from photonic crystal surfaces into arbitrary free-space beam shapes and directions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Validation of electron backscattering in the MCNP6.3 code

A new electron backscattering validation suite has been developed for the MCNP® code. The calculations in this suite cover both condensed history and single-event transport methods, seven different elemental materials ranging from beryllium to uranium, and incident electron energies from 200 eV to 14.1 MeV. In general, the accuracy of the MCNP code for electron backscattering calculations is energy-dependent. For incident electron energies below 2 keV, the condensed history method is not applied while the single-event method shows poor agreement with experimental measurements due to poor accuracy of the underlying atomic data. For incident energies between 2 keV and 256 keV, the single-event method typically shows better agreement with experiments, while for incident energies above 256 keV the condensed history method gives better accuracy. Additionally, agreement with experimental data is generally worse for low- Z materials such as beryllium and carbon, which may reflect deficiencies in the data or physics underlying electron transport through light elements in the MCNP code. The new electron backscattering validation suite, along with the previously introduced electron stopping power and Lockwood energy deposition validation suites, provide MCNP users with a quantified understanding of the uncertainties in electron transport calculations. Future improvements to electron transport in the MCNP code are necessary for low energies (≤ 2 keV) and for low- Z materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Identification and Quantification of Beta-Emitters in Waste Packages by Segmented Gamma-Scanning - 20114

A commonly applied method for the non-destructive characterization of the radioactive inventory of waste packages (e.g. 200-l drums) is segmented gamma-scanning. The gamma-emitting inventory is identified via their characteristic lines in the measured spectra. The corresponding net peak areas allow for quantification applying appropriate models. The identification and quantification of beta-emitters present in the waste package require further evaluation. While some of them emit gamma-rays, too, which can be used for identification and quantification, pure beta-emitters contribute to the gamma-spectra only by their Bremsstrahlung-spectra. {sup 32}P, {sup 90}Sr and {sup 90}Y are typical representatives of these isotopes. A method is developed for identification and quantification of these pure beta-emitters in radioactive waste packages. Origin of the procedure is the evaluation of the measured gamma-spectrum. After the identification of all lines present in the spectrum (characteristic gamma-lines, single- and double-escape-lines, X-ray-lines, annihilation peak etc.) this information is used together with calibration data of the detection system and further a-priori information like matrix composition, cross-section data from databases etc. to simulate the spectra without the Bremsstrahlung-components by an appropriate method. This may be Monte Carlo simulations or, like in our case, a computer code taking into account mathematical descriptions of the physical effects like photo effect, (multiple) Compton scattering, backscattering etc. and the attenuation effects of the waste matrix. In any case, the resulting spectrum must simulate the real spectrum without its Bremsstrahlung-component as good as possible. Subtracting the simulated from the measured spectrum then results in the Bremsstrahlung-components caused by the beta-emitters. Prior to evaluation, a set of Bremsstrahlung-spectra is calculated and stored for different beta-emitters in different matrices using the Bethe-Heitler equation. This data set can be extended for new conditions, if necessary, and is allocated in a data base for future application. Applying a non-linear optimization using the generalized reduced gradient method for comparing the calculated with the derived Bremsstrahlung-spectra results in the identification of the beta-emitter and a factor, which is proportional to its activity. The proportionality is determined by previous calibration measurements. The application of the method is demonstrated by measurements on a 200-l mock-up drum and a real waste package. (authors)

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2025

Thermodynamic Cloud Phase Classifications Using Machine Learning at NSA and ANX

Vertically resolved thermodynamic cloud phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) THERMOCLDPHASE Value-Added Product (VAP) uses a multi-sensor approach to classify thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave radiometer-derived liquid water path, and radiosonde temperature measurements. The measured voxels are classified as ice, snow, mixed-phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multilayer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with one year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1-score, and mean Intersection over Union (IOU). Analysis of ML confidence scores shows ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential datastreams for ML thermodynamic cloud phase predictions. The ML models’ generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. Finally, we evaluate the ML models’ response to simulated instrument outages and signal degradation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Modeling the Contribution of Precipitation Loss to a Radiation Belt Electron Dropout Observed by Van Allen Probes

A drift-diffusion model is used to simulate the low-altitude electron distribution, accounting for azimuthal drift, pitch angle diffusion, and atmospheric backscattering effects during a rapid electron dropout event on 21st August 2013, at L = 4.5. Additional external loss effects are introduced during times when the low-altitude electron distribution cannot be reproduced by diffusion alone. Here, the model utilizes low-altitude electron count rate data from five POES/MetOp satellites to quantify pitch angle diffusion rates. Low-altitude data provides critical constraint on the model because it includes the drift loss cone region where the electron distribution in longitude is highly dependent on the balance between azimuthal drift and pitch angle diffusion. Furthermore, a newly derived angular response function for the detectors onboard POES/MetOp is employed to accurately incorporate the bounce loss cone measurements, which have been previously contaminated by electrons from outside the nominal field-of-view. While constrained by low-altitude data, the model also shows reasonable agreement with high-altitude data. Pitch angle diffusion rates during the event are quantified and are faster at lower energies. Precipitation is determined to account for all of the total loss observed for 450 keV electrons, 88% for 600 keV and 38% for 900 keV. Predictions made in the MeV range are deemed unreliable as the integral energy channels E3 and P6 fail to provide the necessary constraint at relativistic energies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Nephelometer Instrument Handbook

The Integrating Nephelometer (Figure 1) is an instrument that measures aerosol light scattering. It measures aerosol optical scattering properties by detecting (with a wide angular integration – from 7 to 170°) the light scattered by the aerosol and subtracting the light scattered by the carrier gas, the instrument walls and the background noise in the detector (zeroing). Zeroing is typically performed for 5 minutes every day at midnight UTC. The scattered light is split into red (700 nm), green (550 nm), and blue (450 nm) wavelengths and captured by three photomultiplier tubes. The instrument can measure total scatter as well as backscatter only (from 90 to 170°) (Heintzenberg and Charlson 1996; Anderson et al. 1996; Anderson and Ogren 1998; TSI 3563 2015) At ARM (Atmospheric Radiation Measurement), two identical Nephelometers are usually run in series with a sample relative humidity (RH) conditioner between them. This is possible because Nephelometer sampling is non-destructive and the sample can be passed on to another instrument. The sample RH conditioner scans through multiple RH values in cycles, treating the sample. This kind of setup allows to study how aerosol particles’ light scattering properties are affected by humidification (Anderson et al. 1996). For historical reasons, the two Nephelometers in this setup are labeled “wet” and “dry”, with the “dry” Nephelometer usually being the one before the conditioner and sampling ambient air (the names are switched for the MAOS measurement site due to the high RH of the ambient air).

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Mini Micropulse Lidar measurements in vertical and scanning modes

The Atmospheric Radiation Measurement (ARM) program operates ten Sigma Space (currently Droplet Measurement Technologies/DMT) micropulse lidar systems (MPL-4B-IDS/POL-FS series), which detect the backscattered light in two orthogonal linear polarization channels allowing to distinguish between spherical (e.g. water) and non-spherical (e.g. ice) scattering particles. The ARM MPLs have been providing valuable measurements to the aerosol and cloud research community for well over a decade. DMT’s Mini-Micropulse lidar (MiniMPL) is a compact version of the standard MPL. It reduces the power-aperture product to minimize cost, size, weight, power requirements and has all the electronics integrated with optics in a single box. Although as a result, it has a lower detection range than the MPL, limiting the measurements to the troposphere, its performance is promised to match that of the standard MPL within the troposphere. The MiniMPL weighs 13 kg, and the complete system, consisting of an optical transceiver and a laptop running the data acquisition and post-processing software, fits in a storm case, allowing its transport on domestic or international flights. This can be especially valuable for short field campaigns or Intensive Operational Periods, where time is of the essence when replacing a faulty instrument. In addition, the scanning polarization lidar capability (MiniMPL-SCAN option) could be of great interest for satisfying ARM measurement needs for certain research topics related to clouds and precipitation. To evaluate its full potential in comparison with co-located remote sensing instruments including the ARM MPL-4B-IDS/POL-FS at SGP, a miniMPL was hosted at the ARM Southern Great Plains (SGP) mega-site in 2020 for approximately 3 months. During this time the miniMPL was tested in both vertical sampling and scanning modes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Sonic Detection and Ranging (SODAR) Wind Profiler Instrument Handbook

The SODAR (Sonic Detection and Ranging) wind profiler measures wind profiles and backscattered signal strength between (nominally) 15 meters (m) and 500 m. It operates by transmitting acoustic energy into the atmosphere and measuring the strength and frequency of backscattered energy. The strength of the backscattered signal is determined by the strength of temperature inhomogeneities with size on the order of 10 centimeters (cm). Assuming the scattering elements in the atmosphere are moving with the mean wind, the horizontal wind field can be derived. The U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) Climate Research Facility Mobile Facility (AMF) has a system developed by Scintec, Inc. that transmits a sequence of frequencies to enhance signal determination.

17 WIND ENERGY↗
DOE OSTI2021

Afterpulse correction for micro-pulse lidar to improve middle and upper tropospheric aerosol measurements

Micro-pulse lidar (MPL) measurements have been widely used in atmospheric research over the past few decades. However, the MPL afterpulse noise has a large impact on the MPL aerosol measurement in the middle and upper troposphere, and an effective correction method is still lacking. Here, a new afterpulse correction approach is presented by using measurements with low-level optically thick clouds to act as the lids blocking atmospheric signals beyond the clouds completely. Examples are provided to illustrate the effectiveness of this correction method. Using one-year 2014 MPL measurements at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) site, the impact of the correction on the aerosol measurements is quantified. The relative error (RE, %) of attenuated backscattering ratio (ABR) without the afterpulse correction is 30% and 190% at the height of 1.00 km and 9.00 km (AGL), and the RE is larger for weaker aerosol signals (ABR<2). The RE of linear depolarization ratio (LDR), which is significantly higher than that of ABR for a given aerosol layer, is highly non-linear above 3.00 km, with a value of 300%-900% for weak aerosols (ABR<2) above 3.00 km. Therefore, the afterpulse correction is critical for the middle and upper tropospheric aerosol observations. We demonstrated that our afterpulse correction can provide reasonable particle depolarization ratio (PDR) to properly identify dust aerosols. The newly developed method can be applied to long-term MPL measurements to support aerosol and mixed-phase/ice cloud interaction study at the NSA site.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Classifying thermodynamic cloud phase using machine learning models

Vertically resolved thermodynamic cloud-phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Thermodynamic Cloud Phase (THERMOCLDPHASE) value-added product (VAP) uses a multi-sensor approach to classify the thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave-radiometer-derived liquid water path, and radiosonde temperature measurements. The measured pixels are classified as ice, snow, mixed phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multi-layer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with 1 year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1 score, and mean intersection over union (IOU). Analysis of ML confidence scores shows that ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential data streams for ML thermodynamic cloud-phase predictions. Lidar measurements exhibit lower feature importance due to rapid signal attenuation caused by the frequent presence of persistent low-level clouds at the NSA site. The ML models' generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. The models demonstrated similar performance to that observed at the NSA site. Finally, we evaluate the ML models' response to simulated instrument outages and signal degradation and show that a CNN U-Net model trained with input channel dropouts performs better when input fields are missing.

ARM Aerial Facility↗
DOE OSTI2025

Investigating Aerosol Hygroscopicity in the Subcloud Transition Zone and at the Surface in the Southern Great Plains

Aerosols beneath a cloud base, a subcloud transition zone (SCTZ), are key to understand both the aerosol-cloud interaction and aerosol-radiation interactions. Lidars have been the primary means of observing aerosols in the SCTZ by virtue of enhanced light scattered by aerosol particles. The enhanced light maybe caused by several factors: the aerosol swelling effect due to hygroscopicity under high relative humidity, cloud 3-dimensional (3D) effect, aerosol nucleation into cloud droplets, etc. While each factor and process has been known, their relative contributions are much poorly quantified. This study explores the hygroscopicity and optical properties of aerosols in the SCTZ and at ground level in the Southern Great Plains (SGP) region. Utilizing comprehensive observational data from the U.S. Department of Energy's Atmospheric Radiation Measurement at the Oklahoma SGP site, including ground-based aerosol measurements and Raman lidar profiles from April 2021 to April 2022, this study extensively analyzes the influence of aerosol hygroscopic growth and cloud fragments on aerosol optical properties. Distinct seasonal variations in aerosol hygroscopic characteristics are revealed. At the ground level, aerosols in autumn and winter exhibit stronger hygroscopicity due to a higher proportion of inorganic content than summer. In the SCTZ, aerosols during summer show enhanced backscatter due to strong cloud fragmentation effects, with numerous cloud fragments elevating hygroscopicity beyond that observed in autumn and winter. These insights are crucial for understanding the interactions between aerosols at the surface and cloud layers, evaluating cloud condensation nuclei beneath clouds, and their implications for atmospheric radiation and climate modeling.

Hu, Rong [Beijing Normal University, Beijing (Chin↗