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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Developing the Science Basis for Understanding Polymer Encapsulant Degradation Mechanisms: DuraMAT 2.0 Final Project Report

Polymeric encapsulants are essential materials in photovoltaic modules, protecting sensitive electronics from the environment while providing mechanical integrity to the multilayered assembly. However, these polymeric materials are susceptible to degradation processes driven by the ingress of environmental species, ultraviolet radiation, thermal stresses, and mechanical loading. In this study, we employ a combined atomistic simulation and accelerated aging experimental approach to study the molecular-scale mechanisms of encapsulant degradation. Classical molecular dynamics simulations quantify the diffusion of environmental and degradation species through the polymer matrix, producing composition-specific diffusion coefficients. Reactive simulations characterize activation energy barriers and reaction rate constants for key chemical pathways. In parallel, thermal-desorption analyses coupled with mass spectrometry monitor the emergence and concentration profiles of degradation products under controlled stressor conditions. By integrating simulation and experiment, we establish quantitative correlations between polymer composition, species diffusivity, and chemical reactivity. We anticipate that these relations and quantitative values could serve as high-fidelity inputs to reaction-diffusion models, enabling physics-informed lifetime predictions and guiding the design of more durable encapsulant materials for solar energy applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Advanced Semi-Supervised Learning with Uncertainty Estimation for Phase Identification in Distribution Systems

The integration of advanced metering infrastructure (AMI) into power distribution networks generates valuable data for tasks such as phase identification; however, the limited and unreliable availability of labeled data in the form of customer phase connectivity presents challenges. To address this issue, we propose a semi-supervised learning (SSL) framework that effectively leverages labeled and unlabeled data. Our approach incorporates self-training, label spreading, and Bayesian neural networks (BNNs) to enhance phase identification with AMI data. Our method uses an ensemble of multilayer perceptron classifiers in a self-training setup, iteratively adding high-confidence pseudo-labels to improve robustness. We also apply label spread to propagate labels based on data similarity, which enhances generalization across diverse distributions. In addition, we employ a BNNs with uncertainty estimation, boosting confidence in predictions and reducing phase identification errors. In our case study, we achieved approximately 98% +/- 0.08 accuracy with uncertainty using minimal and unreliable labeled data from a real U.S. utility, Duquesne Light Company. Our SSL approach, combined with uncertainty estimation, provides an efficient solution for phase identification in AMI data, ultimately improving the reliability of smart grid applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Advanced Semi-Supervised Learning With Uncertainty Estimation for Phase Identification in Distribution Systems

The integration of advanced metering infrastructure (AMI) into power distribution networks generates valuable data for tasks such as phase identification; however, the limited and unreliable availability of labeled data in the form of customer phase connectivity presents challenges. To address this issue, we propose a semi-supervised learning (SSL) framework that effectively leverages labeled and unlabeled data. Our approach incorporates self-training, label spreading, and Bayesian neural networks (BNNs) to enhance phase identification with AMI data. Our method uses an ensemble of multilayer perceptron classifiers in a self-training setup, iteratively adding high-confidence pseudo-labels to improve robustness. We also apply label spread to propagate labels based on data similarity, which enhances generalization across diverse distributions. In addition, we employ a BNNs with uncertainty estimation, boosting confidence in predictions and reducing phase identification errors. In our case study, we achieved approximately 98% +/- 0.08 accuracy with uncertainty using minimal and unreliable labeled data from a real U.S. utility, Duquesne Light Company. Our SSL approach, combined with uncertainty estimation, provides an efficient solution for phase identification in AMI data, ultimately improving the reliability of smart grid applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Understanding spectral dependance of laser-induced damage precursors in dielectric materials (Abbreviated report_23-ERD-006)

High peak and average power laser systems are typically limited by the handling fluence of the optical components. In particular, the multilayer dielectric coatings are known to be much lower operational fluence than the more ideal bulk materials. In this project we proposed and succeeded in probing different established classes of damage prone precursors at different wavelengths to understand their fundamental laser damage response as a function of wavelength. This study helped to shed light on the fundamental physics of the non-linear precursors that govern laser damage phenomena for ns-regime pulsed laser damage. In this study, we utilized the onsite coating capabilities (VPL, IBS coating lab) to purposefully generate laser damage-prone precursors in hafnia-based coatings (both single and multi-layer coatings). Specifically, we engineered coatings with craze lines initiated by nodules, generated coatings with our xenon-based coating process to suppress nanobubble formation and generated hafnia coatings under controlled oxygen flow conditions to study hafnia sub-oxides and oxygen flow dependance. In these studies we found that craze lines are rife with precursors that are sensitive to ultra-violet light but not to infrared light; we found that the removal of nanobubbles helps with all wavelengths tested, but is most impactful for ultra-violet light; we also found that ultra-violet laser damage performance of hafnia is closely matched to oxygen flow rate, while the infrared performance may be slightly better at lower flow rates. During this LDRD we also successfully stood up a new laser damage testing capability, namely a wavelength agile damage test station to study the spectral response of known laser-induced damage precursors. This is a unique and important capability for Lawrence Livermore National Lab, allowing us to understand the spectral response of materials under high intensity irradiation and damage. This is a vital tool to understand non-linear optical response at wavelengths that we have previously been unable to test at.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Understanding spectral dependance of laser-induced damage precursors in dielectric materials (Full report_23-ERD-006)

The performance of high-energy laser systems is constrained by laser-induced damage in dielectric coatings, particularly those containing hafnium oxide (HfO 2 ). While thresholds at fundamental Nd harmonics are well studied, the spectral dependence of damage initiation—especially under dual-wavelength irradiation—remains poorly characterized. This project provides the first systematic investigation of wavelength-dependent laser damage in hafnia coatings, focusing on nanoscale precursors such as craze lines, nanobubbles, stoichiometric variations, nodules, and controlled crystallization. Coatings were fabricated via ion beam sputtering and electron-beam deposition and characterized using spectrophotometry, ellipsometry, AFM, GI-XRD, RBS, PCI absorption, and fs/ns laser damage testing. Results show that craze lines, benign under infrared light, strongly initiate damage under UV due to wavelength-selective field intensification. Substituting xenon for argon suppresses nanobubbles and improves UV thresholds by up to 32%. Oxygen modulation reveals that fully oxidized films maximize UV resistance, though at the cost of porosity and stress in multilayers. HfO 2 –SiO 2 composites resist crystallization and defects while achieving ppm-level absorption and elevated thresholds, whereas full crystallization of HfO 2 enhances LIDT by reducing defects and improving thermal transport. Collectively, these findings link photon energy, defect states, and bandgap collapse, providing a predictive framework for wavelength-dependent laser damage. The outcomes directly inform the design of durable, multi-wavelength coatings for facilities such as NIF, MEC, HAPLS, and DPAL, advancing the readiness of next-generation optics.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Feature-Based PMU Event Classification under Variable PMU Participation and Overlapping Events

Danovo Energy Solution's presented its paper named: Feature-Based PMU Event Classification under Variable PMU Participation and Overlapping Events at the 2026 Georgia Tech Fault & Disturbance Analysis Conference. The full paper can be found at OSTI ID# 3169150 Paper Abstract—Phasor Measurement Units (PMUs) stream time synchronized, high-resolution measurements from the grid, enabling data-driven techniques for event detection and classification. Accurate event classification improves grid reliability and stability. Events can be detected by varying numbers of PMUs and exhibit different durations depending on the event type. This variability challenges standard classifiers that require uniform input sizes. Moreover, multiple events may coincide, which increases classification complexity. Standard classifiers assign each instance to the class with the highest predicted probability, whereas overlapping events may exhibit comparable probabilities across multiple classes. In this study, to handle data size variability, we extract a wide range of time–frequency domain features from all available PMUs for each event into a fixed-length vector, facilitating the application of standard machine learning classifiers, including Random Forest, XGBoost, LightGBM, Support Vector Machine, and Multilayer Perceptron. To account for overlapping events, a probabilistic post-processing step is applied. For a given data instance, if multiple predicted class probabilities exceed 30% and the differences between them are less than 10%, the event is assigned to multiple classes. Experiments using real-world PMU data demonstrate that the Random Forest and XGBoost models achieve the highest accuracy, while the proposed post-processing method yields perfect classification performance on external unseen test sets.

Nematirad, Reza [Danova Energy Solutions]↗
DOE OSTI2026

Complete time-resolved X-ray shape capability for a 10 MJ implosion: Milestone Report for MRT 8818

This report documents completion of MRT 8818, which established an upgraded equatorial time-resolved X-ray imaging capability for high-yield inertial confinement fusion experiments at the National Ignition Facility (NIF) using the equatorial Dilation X-ray Imager (DIXI). The need for this work arose from the sustained increase in fusion yield at NIF, which progressively intensified the neutron and gamma-ray environment experienced by target diagnostics. Although the existing polar imaging capability, particularly the Polar Dilation X-ray Imager (PDIXI), remained viable at high yields, the equatorial capability became inadequate because of radiation-induced failure of electronic readout hardware and increasing background levels that degraded image quality and ultimately caused saturation. The work performed under MRT 8818 addressed this limitation through a combination of hardware replacement, background characterization, and targeted mitigation. The DIXI backend was converted from CCD-based electronic readout to photographic film, and the principal sources of internally generated background were investigated using prior analyses, dedicated tests, and comparison with PDIXI performance on high-yield shots. This effort led to implementation of two principal improvements: a multilayer optical coating to suppress broadband radiation-induced background generated in the fiber-optic extension cylinder, and replacement of Kodak TMAX 400 film with Agfa Copex Rapid to reduce background generated directly in the film. These upgrades were completed in 2025 and performance assessment based on DT shot data since then, comparing with Polar DIXI and scaling of these experiments to 10 MJ indicates an expected signal-to-background ratio of approximately 34 for a single pinhole image, substantially exceeding the MRT 8818 completion criterion of SNR > 5.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Machine Protection Studies of Fast Instability-Driven Beam Losses in the EIC Electron Storage Ring

Fast transverse beam instabilities represent one of the most challenging protection scenarios for the Electron-Ion Collider (EIC) electron storage ring (ESR). Coherent betatron oscillations driven by collective effects can grow rapidly and lead to substantial beam losses within only a few tens of turns, posing a threat to collimators, superconducting (SC) magnets, cryogenic systems, and detector components. A dedicated simulation framework based on distributed transverse excitations was developed to evaluate instability-driven beam losses in the ESR. A conservative instability rise time on the order of 10 turns was used to define a bounding protection scenario. Simulations show that the original two-collimator configuration is insufficient to localize losses under all operating conditions, whereas an optimized four-collimator configuration reduces loss leakage into protected regions by more than two orders of magnitude. Nevertheless, the most severe instability scenarios produce catastrophic beam impacts, with nearly half of the stored beam being intercepted by the collimators within a single turn, exceeding the preliminary estimated survivability limits of the baseline collimator design. The results demonstrate that collimation alone is insufficient to guarantee safe ESR operation and must be complemented by a fast machine-protection system. Dedicated turn-by-turn beam-position monitors and fast beam-loss monitors provide sufficient advance warning to detect instability growth and initiate a beam abort before equipment-damaging loss levels are reached. These findings establish quantitative requirements for the ESR machine-protection architecture and support the implementation of a multilayer protection strategy for both accelerator and detector systems.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Performance of the AstroPix Prototype Module for the Barrel Imaging Calorimeter at the ePIC Detector and in Space-Based Payloads

AstroPix is a high-voltage CMOS (HV-CMOS) monolithic active pixel sensor originally developed to enable precision gamma-ray imaging and spectroscopy in the medium-energy regime (~100 keV–100 MeV) based on the groundwork laid by ATLASpix and MuPix. It features a 500 μm pixel pitch, in-pixel amplification and digitization, and low power consumption (~3-4 mW/cm2), making it scalable for large-area, multilayer telescope detector planes. The detectors have a designed dynamic range of 25 keV to 700 keV. With these features, AstroPix meets the requirements of future space-based high-energy telescopes and the imaging layers of the Barrel Imaging Calorimeter (BIC) in the Electron-Proton/Ion Collider (ePIC) detector at the future Electron-Ion Collider (EIC). For the space-based payload, AstroPix is being integrated into sounding rocket and balloon payloads to demonstrate the technical readiness of the devices. For BIC, AstroPix-based imaging layers interleaved within the lead/scintillating-fiber (Pb/SciFi) sampling calorimeter provide granular shower imaging, enabling key performance features such as electron/pion or gamma/neutral pion separation. As part of the ongoing detector R&D efforts, we have been testing various AstroPix_v3 configurations: the single chip, a quad-chip assembly, a three-layer stack of quad chips, and a 9-chip module that represents the smallest prototype unit of the BIC imaging layer. This presentation will highlight recent performance test results from these AstroPix detector configurations.

Kim, Bobae (ORCID:0000000295396815)↗
DOE OSTI2024

A physics-based ensemble machine-learning approach to identifying a relationship between lightning indices and binary lightning hazard

To convert lightning indices generated by numerical weather prediction experiments into binary lightning hazard, a machine-learning tool was developed. This tool, consisting of parallel multilayer perceptron classifiers, was trained on an ensemble of planetary boundary layer schemes and microphysics parameterizations that generated four different lightning indices over 1 week. In a subsequent week, the multi-physics ensemble was applied and the machine-learning tool was used to evaluate the accuracy. Unintuitively, the machine-learning tool performed better on the testing dataset than the training dataset. Much of the error may be attributed to mischaracterizing the convection. The combination of the machine learning model and simulations could not differentiate between cloud-to-cloud lightning and cloud-to-ground lightning, despite being trained on cloud-to-ground lightning. It was found that the simulation most representative of the local operational model was the most accurate simulation tested.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Strategies for Enhancing Battery Life Under Fast Charging: Insights from NMC-Based Cell Cycling

Fast charging improves the usability of consumer electronics and electric vehicles (EVs) by reducing range anxiety and downtime but accelerates battery degradation and raises safety concerns. Optimizing operational conditions during fast-charging is critical to mitigating aging and ensuring safety. This study evaluated multilayer Gr/NMC811 cells under various conditions, including depths of discharge (DODs of 68%, 84%, and 100%), upper charge cutoff voltages (4.1–4.2 V), and post-charge rest periods (2–30 min), using a 20 min fast charging protocol for up to 500 cycles (up to 150,000 miles of EV use assuming 3.3 mi/kWh vehicle level energy efficiency). Surprisingly, higher DODs under fast charging improved battery life and performance compared to lower DODs. Reducing the upper charge cut-off voltage helped mitigate degradation. A brief 2 min rest period after charging further reduced aging effects. The primary aging modes were loss of lithium inventory and cathode active material. Although minor lithium plating was observed within 500 cycles, it did not affect performance significantly. These findings suggest that, with optimized conditions, cells can sustain hundreds of fast charge cycles—equivalent to over 100,000 miles of EV use—without significant adverse effects on performance or longevity.

25 - ENERGY STORAGE↗
DOE OSTI2024

The Evaluation of Machine Learning Techniques for Isotope Identification Contextualized by Training and Testing Spectral Similarity

Precise gamma-ray spectral analysis is crucial in high-stakes applications, such as nuclear security. Research efforts toward implementing machine learning (ML) approaches for accurate analysis are limited by the resemblance of the training data to the testing scenarios. The underlying spectral shape of synthetic data may not perfectly reflect measured configurations, and measurement campaigns may be limited by resource constraints. Consequently, ML algorithms for isotope identification must maintain accurate classification performance under domain shifts between the training and testing data. To this end, four different classifiers (Ridge, Random Forest, Extreme Gradient Boosting, and Multilayer Perceptron) were trained on the same dataset and evaluated on twelve other datasets with varying standoff distances, shielding, and background configurations. A tailored statistical approach was introduced to quantify the similarity between the training and testing configurations, which was then related to the predictive performance. Wilcoxon signed-rank tests revealed that the OVR-wrapped XGB significantly outperformed the other algorithms, with confidence levels of 99.0% or above for the 133Ba, 60Co, 137Cs, and 152Eu sources. The findings from this work are significant as they outline techniques to promote the development of robust ML-based approaches for isotope identification.

domain adaptation↗
DOE OSTI2024

Heterogeneous Morphologies and Hardness of Co-Sputtered Thin Films of Concentrated Cu-Mo-W Alloys

Heterogeneous microstructures in Cu-Mo-W alloy thin films formed by magnetron co-sputtering immiscible elements with concentrated compositions are characterized using scanning transmission electron microscopy (STEM) and nanoindentation. In this work, we modified the phase separated structure of a Cu-Mo immiscible system by adding W, which impedes surface diffusion during film growth. The heterogeneous microstructures in the Cu-Mo-W ternary system exhibited bicontinuous matrices and agglomerates composed of Mo(W)-rich phase. This is unique, as these are the slower-diffusing species, contrasting past reports of binary Cu-Mo thin films that exhibited Cu-rich agglomerates. The bicontinuous matrices comprised of Cu-rich and Mo(W)-rich phases exhibited bilayer thicknesses of less than 5 nm. The hardness of these thin films measured using nanoindentation is reported and compared to similar multilayers and nanocomposites in binary systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Optimization and Multimachine Learning Algorithms to Predict Nanometal Surface Area Transfer Parameters for Gold and Silver Nanoparticles

Interactions between gold metallic nanoparticles and molecular dyes have been well described by the nanometal surface energy transfer (NSET) mechanism. However, the expansion and testing of this model for nanoparticles of different metal composition is needed to develop a greater variety of nanosensors for medical and commercial applications. In this study, the NSET formula was slightly modified in the size-dependent dampening constant and skin depth terms to allow for modeling of different metals as well as testing the quenching effects created by variously sized gold, silver, copper, and platinum nanoparticles. Overall, the metal nanoparticles followed more closely the NSET prediction than for Förster resonance energy transfer, though scattering effects began to occur at 20 nm in the nanoparticle diameter. To further improve the NSET theoretical equation, an attempt was made to set a best-fit line of the NSET theoretical equation curve onto the Au and Ag data points. An exhaustive grid search optimizer was applied in the ranges for two variables, 0.1≤C≤2.0 and 0≤α≤4, representing the metal dampening constant and the orientation of donor to the metal surface, respectively. Three different grid searches, starting from coarse (entire range) to finer (narrower range), resulted in more than one million total calculations with values C=2.0 and α=0.0736. The results improved the calculation, but further analysis needed to be conducted in order to find any additional missing physics. With that motivation, two artificial intelligence/machine learning (AI/ML) algorithms, multilayer perception and least absolute shrinkage and selection operator regression, gave a correlation coefficient, R2, greater than 0.97, indicating that the small dataset was not overfitting and was method-independent. This analysis indicates that an investigation is warranted to focus on deeper physics informed machine learning for the NSET equations.

Demers, Steven M. E. (ORCID:0000000192213246)↗
DOE OSTI2025

Glancing Angle Deposition in Gas Sensing: Bridging Morphological Innovations and Sensor Performances

Glancing Angle Deposition (GLAD) has emerged as a versatile and powerful nanofabrication technique for developing next-generation gas sensors by enabling precise control over nanostructure geometry, porosity, and material composition. Through dynamic substrate tilting and rotation, GLAD facilitates the fabrication of highly porous, anisotropic nanostructures, such as aligned, tilted, zigzag, helical, and multilayered nanorods, with tunable surface area and diffusion pathways optimized for gas detection. This review provides a comprehensive synthesis of recent advances in GLAD-based gas sensor design, focusing on how structural engineering and material integration converge to enhance sensor performance. Key materials strategies include the construction of heterojunctions and core–shell architectures, controlled doping, and nanoparticle decoration using noble metals or metal oxides to amplify charge transfer, catalytic activity, and redox responsiveness. GLAD-fabricated nanostructures have been effectively deployed across multiple gas sensing modalities, including resistive, capacitive, piezoelectric, and optical platforms, where their high aspect ratios, tailored porosity, and defect-rich surfaces facilitate enhanced gas adsorption kinetics and efficient signal transduction. These devices exhibit high sensitivity and selectivity toward a range of analytes, including NO2, CO, H2S, and volatile organic compounds (VOCs), with detection limits often reaching the parts-per-billion level. Emerging innovations, such as photo-assisted sensing and integration with artificial intelligence for data analysis and pattern recognition, further extend the capabilities of GLAD-based systems for multifunctional, real-time, and adaptive sensing. Finally, current challenges and future research directions are discussed, emphasizing the promise of GLAD as a scalable platform for next-generation gas sensing technologies.

Chemistry↗
DOE OSTI2024

Toward Accelerated Nuclear-physics Parameter Estimation from Binary Neutron Star Mergers: Emulators for the Tolman–Oppenheimer–Volkoff Equations

Abstract Gravitational-wave observations of binary neutron-star (BNS) mergers have the potential to revolutionize our understanding of the nuclear equation of state (EOS) and the fundamental interactions that determine its properties. However, Bayesian parameter estimation frameworks do not typically sample over microscopic nuclear-physics parameters that determine the EOS. One of the major hurdles in doing so is the computational cost involved in solving the neutron-star structure equations, known as the Tolman–Oppenheimer–Volkoff (TOV) equations. In this paper, we explore approaches to emulating solutions for the TOV equations: multilayer perceptrons (MLPs), Gaussian processes, and a data-driven variant of the reduced basis method (RBM). We implement these emulators for three different parameterizations of the nuclear EOS, each with a different degree of complexity represented by the number of model parameters. We find that our MLP-based emulators are generally more accurate than the other two algorithms, whereas the RBM results in the largest speedup with respect to the full high-fidelity TOV solver. We employ these emulators for a simple parameter inference using a potentially loud BNS observation and show that the posteriors predicted by our emulators are in excellent agreement with those obtained from the full TOV solver.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

WRF-Comfort: simulating microscale variability in outdoor heat stress at the city scale with a mesoscale model

Abstract. Urban overheating and its ongoing exacerbation due to global warming and urban development lead to increased exposure to urban heat and increased thermal discomfort and heat stress. To quantify thermal stress, specific indices have been proposed that depend on air temperature, mean radiant temperature (MRT), wind speed, and relative humidity. While temperature and humidity vary on scales of hundreds of meters, MRT and wind speed are strongly affected by individual buildings and trees and vary on the meter scale. Therefore, most numerical thermal comfort studies apply microscale models to limited spatial domains (commonly representing urban neighborhoods with building blocks) with resolutions on the order of 1 m and a few hours of simulation. This prevents the analysis of the impact of city-scale adaptation and/or mitigation strategies on thermal stress and comfort. To solve this problem, we develop a methodology to estimate thermal stress indicators and their subgrid variability in mesoscale models – here applied to the multilayer urban canopy parameterization BEP-BEM within the Weather Research and Forecasting (WRF) model. The new scheme (consisting of three main steps) can readily assess intra-neighborhood-scale heat stress distributions across whole cities and for timescales of minutes to years. The first key component of the approach is the estimation of MRT in several locations within streets for different street orientations. Second, mean wind speed and its subgrid variability are downscaled as a function of the local urban morphology based on relations derived from a set of microscale LES and RANS simulations across a wide range of realistic and idealized urban morphologies. Lastly, we compute the distributions of two thermal stress indices for each grid square, combining all the subgrid values of MRT, wind speed, air temperature, and absolute humidity. From these distributions, we quantify the high and low tails of the heat stress distribution in each grid square across the city, representing the thermal diversity experienced in street canyons. In this contribution, we present the core methodology as well as simulation results for Madrid (Spain), which illustrate strong differences between heat stress indices and common heat metrics like air or surface temperature both across the city and over the diurnal cycle.

Geology↗
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↗