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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Century: Zap Energy’s 100-kW-Scale Repetitive Sheared-Flow-Stabilized Z -Pinch System with Liquid Metal Cooling

Zap Energy is developing the sheared-flow-stabilized (SFS) Z-pinch concept for commercial applications. The SFS Z pinch relies on plasma self-organization, in the sense that plasma dynamics play a critical role in confinement. Using plasma axial current for confinement and compression eliminates the need for external confinement or heating technologies. This compact magnetic confinement technology could, in turn, provide the basis for a cost-effective deuterium-tritium fusion power plant. In addition to a robust experimental program pushing plasma performance towards breakeven conditions, Zap Energy has parallel programs developing power handling systems suitable for future power plants. Technologies under development include high average-power repetitive pulsed power, high duty-cycle cathodes, and liquid metal wall systems. Century is the name of Zap Energy’s first effort to integrate these three components into an operational system capable of firing non-reacting hydrogen SFS Z-pinch plasmas into a liquid-metal-lined container at sustained repetition rates on the order of 0.1 Hz. Here, the pulsed power driver and liquid metal heat exchanger are both designed to sustain input powers of 100 kW. Construction and initial operations with an interim ~10 kW liquid metal heat exchanger are described.

Century↗
DOE OSTI2026

Proximity Magnetism in Mn(Bi,Sb) 2 Te 4 –(Bi,Sb) 2 Te 3 /MnTe Natural Heterostructures

Magnetic topological insulators and their heterostructures provide significant opportunities to couple band topology with a nontrivial spin configuration for enhanced spintronic device performance, as well as designing magnetoelectric systems and functionalities. We find that Mn interdiffusion from MnTe when interfaced with (Bi,Sb) 2 Te 3 stabilizes as self-organized Mn(Bi,Sb) 2 Te 4 septuple lamellae among alternating (Bi,Sb) 2 Te 3 quintuple layers, as observed using scanning transmission electron microscopy and depth-sensitive polarized neutron reflectometry. We further demonstrate a valuable combination of magnetic and topological orders in these naturally formed Mn(Bi,Sb) 2 Te 4 –(Bi,Sb) 2 Te 3 heterostructures, which are exchange-coupled with MnTe. Magnetotransport experiments and quantum magnetism simulations reveal that, above its own Néel temperature T N ∼ 20 K, Mn(Bi,Sb) 2 Te 4 mediates the exchange field leading to an anomalous Hall effect at the (Bi,Sb) 2 Te 3 /MnTe interface, with an enhanced interfacial T N exceeding 200 K, approaching that of the bulk MnTe. This magnetic interface, in turn, allows a robust and deterministic spin–orbit torque switching without an external magnetic field at a low critical current density of 3 × 10 5 A cm –2 . The antiferromagnetically coupled architecture of Mn(Bi,Sb) 2 Te 4 –(Bi,Sb) 2 Te 3 /MnTe, featuring magnetic and topological proximity effects across a chalcogenide backbone, is rich in fundamental interface physics and holds the potential for practical applications in spintronics.

magnetic topological insulator↗
DOE OSTI2026

Spring Dust in Colorado Plateau: Transport Pathways and Interannual Variability Derived From Two Decades of MERRA‐2 Reanalysis

Spring dust storms in the U.S. Southwest significantly impact environmental and human systems, yet their climatological patterns and driving mechanisms remain poorly understood. Using two decades of MERRA-2 reanalysis data and self-organizing map clustering, we identified four distinct dust transport pathways from surrounding and remote deserts for the top 10% of spring dust events impacting the Colorado Plateau: (a) intense southwesterly near-surface transport where eastward upward transport is blocked by topography, (b) substantial remote transport plus weaker surface emissions, (c) moderate southwesterly transport with stronger upper-level transport, and (d) rare northwesterly pattern linked to the Great Basin sources, which accounts for 38%, 14%, 37%, and 11% of total events, respectively. The frequency and intensity of these events strongly correlate with large-scale climate variability, with increased dust events during La Niña. Our results provide a robust framework for improving seasonal dust forecasts and understanding future dust dynamics under climate change.

aerosol transport↗
DOE OSTI2024

Evolution of radiative thermal instability in a confined medium

Thermally bistable fluid tends to self-organize into clouds of hot and cold material, which are internally uniform and separated by thin conduction fronts. The evolution of these clouds has been studied for isobaric systems, but when pressure is instead treated as a dynamical quantity and allowed to evolve self-consistently, fundamentally different dynamics appear. Such a treatment is necessary in some laboratory plasmas, whose volume is constrained but whose pressure can vary. Here, solutions are derived for the evolution of clouds, accounting for pressure variation and interactions between conduction fronts. Additional stable configurations and secondary instabilities are derived, which may be relevant to fusion plasmas and to the study of photoionized plasma in the laboratory.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Predicting Cislunar Orbit Lifetimes from Initial Orbital Elements

The volume of space between Earth’s geosynchronous orbit out to the Moon’s sphere of influence, including the lunar Lagrange points, is crucial for the successful planning and execution of space missions, but not fully understood dynamically. This region is a part of cislunar space. Trajectories through cislunar space are influenced by the gravitational forces of the Sun, Earth, Moon, and other Solar System planets leading to typically unpredictable and chaotic trajectory behavior. It is therefore difficult to predict the stability of an trajectory through cislunar space from a set of initial conditions or orbital elements. We simulate one million cislunar orbits to train a self-organizing map (SOM) to cluster the trajectories and orbits into families based on how long they remain stable within the cislunar space. Using the trained SOM, we are able to predict the stable lifetime of a trajectory through cislunar space from a set of initial orbital elements to within an accuracy of 10% for 8% of simulated trajectories and within 50% for 43% of the simulated trajectories. Clustering in the SOM suggests that a variety of trajectory morphologies have similar lifetimes. Once trained, the SOM can predict the stable lifetime of a given cislunar trajectory within milliseconds. The methods developed in this work enable the rapid identification of stable cislunar orbits and trajectories that could be used for future space exploration. Moreover, the developed SOM method can generate orbital and trajectory lifetime estimates from minimal observational data, such as a single two line element, making it useful for early warning systems and large-scale sensor network operations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

An electronic microemulsion phase emerging from a quantum crystal-to-liquid transition

Strongly interacting electronic systems often exhibit a complicated phase diagram that results from the competition between different quantum ground states. One feature of these phase diagrams is the emergence of microemulsion phases, where regions of different phases self-organize across multiple length scales. The experimental characterization of these microemulsions can pose considerable challenges, as the long-range Coulomb interaction microscopically mingles with the competing states. Here we observe the signatures of the microemulsion between an electronic Wigner crystal and an electron liquid in a MoSe 2 monolayer using cryogenic reflectance and magneto-optical spectroscopy. We find that the transition into this microemulsion state is marked by anomalies in exciton reflectance, spin susceptibility and umklapp scattering, establishing it as a distinct phase of electronic matter.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Self-assembled reconfigurable pump architectures via magnetic colloidal swarms

Self-assembled swarms of interactive active units, which are adaptive and dynamically reconfigurable to accommodate different functionalities, represent a promising platform for the development of next-generation robotics. Here, we utilize the emergent collective behavior of active magnetic colloids confined in quasi-two-dimensional arrays of overlapping wells to demonstrate the self-organization of a colloidal swarm into a dynamic pump architecture capable of controlled transport of passive cargo particles. This dynamic architecture provides a global unidirectional looping flow pattern along the entire length of the system. We show that the flow direction of the dynamic swarm-based pump can be externally controlled by a phase shift of a driving magnetic field energizing the swarm. The experimental observations are supported by computational modeling based on phenomenological coarse-grained particle dynamics coupled to shallow-water Navier-Stokes hydrodynamics. In conclusion, our findings demonstrate how the emergent collective behavior of a swarm can be orchestrated into a desired functionality by exploiting the interplay between activity and confinement potentials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Stakeholder-guided holistic, Adaptive Framework for enhancing community Energy Resilience (SAFER) (Final Technical Report)

The Stakeholder-guided holistic, Adaptive Framework for enhancing community Energy Resilience (SAFER) project advances resilience science and engineering by addressing challenges in rural Kansas communities where aging infrastructure, extreme weather, and socioeconomic disparities heighten vulnerability to energy disruptions. Traditional approaches often focus on technical performance while overlooking community concerns and priorities. SAFER responds by integrating community perspectives with advanced analytical frameworks to create a holistic model for measuring and improving resilience. Project objectives included developing novel resilience metrics, advancing modeling frameworks that capture interdependencies across infrastructures, and embedding community-centric indicators directly into planning processes for distributed energy resources. The key technical innovations included the creation of self-organizing map (SOM)-based indices for objective resilience quantification, hetero-functional graph theory (HFGT) models linking power, water, transportation, and community assets, and graph neural network (GNN) tools for identifying critical nodes in complex systems. Community-centric energy planning was demonstrated through optimal siting and sizing of (photovoltaic) PV and battery storage, ensuring resilience enhancements also addressed energy burden and energy insecurity. SAFER engaged community partners in Dodge City and Ford County through surveys, focus groups, and workshops, generating more than 600 responses that established baseline measures of energy burden, financial insecurity, and willingness-to-pay to avoid outages. This data, organized in terms of a community capitals framework, informed the development of weighted reliability indices that better reflect community costs than traditional utility metrics. SAFER’s GNN-based critical node identification framework identified expert-labelled critical nodes with over 99% accuracy, while also uncovering additional functionalities essential for proactive resilience planning. The project’s models demonstrated that optimal PV and storage deployment could improve resilience indices by over 11 percent, with dispatch strategies further enhancing outcomes, confirming both the technical effectiveness and economic feasibility of these approaches. Through its combined emphasis on rigorous modeling, community-focused planning, and community engagement, SAFER advances the state of resilience research while delivering direct benefits to rural communities. The project provides tools, guidelines, and resilience heatmaps that help utilities, local governments, and residents better anticipate disruptions, prioritize investments, and strengthen the capacity to withstand and recover from energy-related hazards. Furthermore, the developed HFG and GNN frameworks are designed for transferability, allowing them to be adapted for resilience planning in other communities with minimal retraining. This inductive learning capability provides a scalable pathway to extend the SAFER project’s impact. Thus, creating a foundation for a nationally applicable model of infrastructure resilience. Additionally, the HFG can also be extended to include other FEMA community lifelines.

14 SOLAR ENERGY↗
NASA NTRS1972

Computers formed by the problems rather than problems deformed by the computers.

Description of an approach to computer programming which tries to minimize the time required for retranslation from the computer language into the language of the original process. A notion of a programmable network is introduced which allows the abstract machines that are a model of the processes in the user's mind to be put into a rigorous and simple form. Each process is then modeled as a particular finite-state machine, a circulating page loose system being employed as an architecture for implementing these finite-state machines. An experiment is discussed in which the use of abstract machines as a language for modeling processes, in conjunction with the use of a self-organizing computer, decreases user effort, eliminates the need for compilation, facilitates debugging, and decreases computer time.

Schaffner, M. R.↗
DOE OSTI2025

Active-noise-induced dynamic clustering of passive colloidal particles

Active fluids generate spontaneous, often chaotic mesoscale flows. Harnessing these flows to drive soft materials embedded within an active fluid into structures with controlled length scales and lifetimes is a key challenge at the interface between the fields of active matter and nonequilibrium self-assembly. Here, we present a simple and efficient computational approach to model soft materials advected by active fluids, by simulating particles moving in a spatiotemporally correlated noise field. To illustrate our approach, we simulate the dynamical self-organization of repulsive colloids within such an active noise field. The colloids form structures whose sizes and dynamics can be tuned by the correlation time and length of the active fluid, and range from small rotating droplets to clusters with internal flows and system-spanning sizes that vastly exceed the active correlation length. Our results explain how the interplay between active fluid time and length scales and emergent driven assembly can be used to rationally design functional assemblies. More broadly, our approach can be used to efficiently simulate diverse active fluids and other systems with spatiotemporally correlated noise.

Brownian dynamics↗
DOE OSTI2025

3D pattern formation of a protein–membrane suspension

Many essential cellular processes, including cell division and the establishment of cell polarity during embryogenesis, are regulated by pattern-forming proteins. These proteins often need to bind to a substrate, such as the cell membrane, onto which they interact and form two-dimensional (2D) patterns. It is unclear how the membrane’s continuity and dimensionality impact pattern formation. Here, we address this gap using the MinDE system, a prototypical example of pattern-forming membrane proteins. We show that when the lipid substrate is fragmented into submicrometer-sized diffusive liposomes, adenosine triphosphate-driven protein–protein interactions generate three-dimensional (3D) spatially extended patterns, despite the complete loss of membrane continuity. Remarkably, these 3D patterns emerge at scales four orders of magnitude larger than the individual liposomes. By systematically varying protein concentration, liposome size, and density, we observed and characterized a variety of 3D dynamical patterns not seen on continuous 2D membranes, including traveling waves, dynamical spirals, and a coexistence phase. Simulations and linear stability analysis of a coarse-grained model revealed that the physical properties of the dispersed membrane effectively rescale both the protein–membrane binding rates and diffusion, two key parameters governing pattern formation and wavelength selection. These findings highlight the robustness of Min’s pattern-forming ability, suggesting that protein–membrane suspensions could serve as an adaptable template for studying out-of-equilibrium self-organization in 3D, beyond in vivo contexts.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Link statistics of dislocation network during strain hardening

Dislocations are line defects in crystals that multiply and self-organize into a complex network during strain hardening. The length of dislocation links, connecting neighboring nodes within this network, contains crucial information about the evolving dislocation microstructure. By analyzing data from Discrete Dislocation Dynamics (DDD) simulations in face-centered cubic (fcc) Cu, we characterize the statistical distribution of link lengths of dislocation networks during strain hardening on individual slip systems. Here, our analysis reveals that link lengths on active slip systems follow a double-exponential distribution, while those on inactive slip systems conform to a single-exponential distribution. The distinctive long tail observed in the double-exponential distribution is attributed to the stress-induced bowing out of long links on active slip systems, a feature that disappears upon removal of the applied stress. We further demonstrate that both observed link length distributions can be explained by extending a one-dimensional Poisson process to include different growth functions. Specifically, the double-exponential distribution emerges when the growth rate for links exceeding a critical length becomes super-linear, which aligns with the physical phenomenon of long links bowing out under stress. This work advances our understanding of dislocation microstructure evolution during strain hardening and elucidates the underlying physical mechanisms governing its formation.

Crystal plasticity↗
DOE OSTI2024

Underlying mechanism of structural transformation between GaSb and GaAs response to intense electronic excitation

Ion irradiation of semiconductors has emerged as a promising approach for fabricating self-organized nanosystems with high atomic precision, despite often being accompanied by undesirable phenomena. Exploring the mechanisms underlying structural transformations is crucial for assessing nanostructure array types under complex irradiation environments. By quantitatively calculating the thermodynamically driven processes and analyzing the impact of intrinsic structural parameters, distinct structural transformations in response to intense electronic excitation are systematically investigated in gallium antimonide (GaSb) and gallium arsenide (GaAs) systems. In high-energy regimes, the nanofibers layer of GaSb exhibits intriguing structural discrepancy, characterized by partial nanofibers with coherent boundaries, interspersed nanopores accompanied by antisite defects and Ga precipitates, distinguishing to a series of discontinuous latent tracks that emerged within cylindrical trajectories in GaAs. Furthermore, significant diffusion behaviors of the nanohillocks are discovered in GaAs, with higher average roughness than GaSb, driven by the gradient stress distribution influenced by the free-surface effects. The deposition energy for melting phase formation, Gibbs free energy, and Ga diffusion coefficients contribute to the distinctive structural features, evidencing relatively stable morphological configurations and higher irradiation resistance in GaAs. Consequently, special optoelectronic properties associated with structural discrepancies facilitate the design and optimization of material functionalities by irradiation technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Emerging supersolidity in photonic-crystal polariton condensates

A supersolid is a counter-intuitive phase of matter in which its constituent particles are arranged into a crystalline structure, yet they are free to flow without friction. This requires the particles to share a global macroscopic phase while being able to reduce their total energy by spontaneous, spatial self-organization. The existence of the supersolid phase of matter was speculated more than 50 years ago1, 2, 3–4. However, only recently has there been convincing experimental evidence, mainly using ultracold atomic Bose–Einstein condensates (BECs) coupled to electromagnetic fields. There, various guises of the supersolid were created using atoms coupled to high-finesse cavities5,6, with large magnetic dipole moments7, 8, 9, 10, 11, 12–13, and spin–orbit-coupled, two-component systems showing stripe phases14, 15–16. Here we provide experimental evidence of a new implementation of the supersolid phase in a driven-dissipative, non-equilibrium context based on exciton–polaritons condensed in a topologically non-trivial, bound state in the continuum (BiC) with exceptionally low losses, realized in a photonic-crystal waveguide. We measure the density modulation of the polaritonic state indicating the breaking of translational symmetry with a precision of several parts in a thousand. Direct access to the phase of the wavefunction allows us to also measure the local coherence of the supersolid. We demonstrate the potential of our synthetic photonic material to host phonon dynamics and a multimode excitation spectrum.

Trypogeorgos, Dimitrios↗
DOE OSTI2025

Surface deformation coupled with self-organized pattern on a liquid anode of 1 atm DC glow discharge

Intricate, self-organized plasma structures observed above the surface of a liquid anode of atmospheric DC glow discharge were found to give rise to coherent, organized surface deformation and mechanical wave formation at the plasma–liquid interface. This new phenomenon indicates that the liquid is closely coupled to the plasma by the anode sheath’s electrohydrodynamic (EHD) force. A scientific question then arises: Do surface perturbations, coupled with the nonuniform surface charge distribution, enhance the electric field and induce self-organization? Using the reflective background-oriented schlieren technique, the liquid surface profile under the plasma pattern was measured for the first time. The results show that surface distortions are driven by the repulsive Coulomb force of nonuniform net-negative surface charge acted by the anode sheath field. The impacts of various operating parameters on the patterns and surface waves were examined, revealing the significance of gas heating and liquid charge relaxation time in the pattern formation mechanism. Time-resolved dynamics of a pulsed DC discharge indicated that the surface deformation only appeared after the establishment of plasma patterns. Statistically, the surface wave under the plasma has high wave numbers (8000–16000 m −1 ) and small amplitudes ($<$ 10 µm), generally found in the capillary wave regime. Yet the motion of surface deformations is in tandem with the plasma pattern and exhibits a nondispersive nature of constant phase velocity (0.1–0.4 m s −1 ), suggesting the dominant role of EHD force over the surface tension in the observed surface wave. These results indicate that the deformed liquid surface is driven by the EHD force of the plasma sheath. Although they share a similar geometry, the deformation and wave dynamics of the liquid surface do not stimulate a plasma pattern. Importantly, the complex EHD coupling in the plasma–liquid system raises awareness and new challenges for plasma control engineering, and the quantitative characteristics of the nondispersive surface wave are informative for advancing relevant theory and modeling.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Using active learning to improve quasar identification for the DESI spectra processing pipeline

The Dark Energy Spectroscopic Instrument (DESI) survey uses an automatic spectral classification pipeline to classify spectra. QuasarNET is a convolutional neural network used as part of this pipeline originally trained using data from the Baryon Oscillation Spectroscopic Survey (BOSS). In this paper we implement an active learning algorithm to optimally select spectra to use for training a new version of the QuasarNET weights file using only DESI data, with the goal of improving classification accuracy. This active learning algorithm includes a novel outlier rejection step using a Self-Organizing Map to ensure we label spectra representative of the larger quasar sample observed in DESI. We perform two iterations of the active learning pipeline, assembling a final dataset of 5600 labeled spectra, a small subset of the approximately 1.3 million quasar targets in DESI's Data Release 1. When splitting the spectra into training and validation subsets we achieve similar performance to the previously trained weights file in completeness and purity calculated on the validation dataset but do so with less than one tenth of the amount of training data. The new weights also more consistently classify objects in the same way when used on unlabeled data compared to the old weights file. In the process of improving QuasarNET's classification accuracy we discovered a systemic error in QuasarNET's redshift estimation and used our findings to improve our understanding of QuasarNET's redshifts.

Machine learning↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Mesoscale Organization in Cumulus-Coupled Stratocumulus

Marine cloud systems cover a substantial portion of the world’s oceans. Most of these clouds form relatively close to the ocean surface, typically within one to two kilometers, a region referred to by meteorologists as the marine boundary layer. They are composed predominantly of liquid water, although ice particles can occur in mid- and high-latitude marine clouds during winter. In satellite imagery, these clouds appear bright against the darker ocean surface below, reflecting a large fraction of incoming sunlight back into space that would otherwise warm the ocean. Because marine boundary layer clouds cover such an extensive area of the ocean, they exert a significant influence on Earth’s overall transfer of solar energy absorbed by the surface and thermal energy emitted to space, a balance known as the planetary radiation budget. Marine boundary layer clouds are typically thin, and their formation and dissipation depend on a delicate balance between processes acting at the ocean surface below and the warm, dry air above. They are notoriously difficult to simulate accurately in weather forecast models, which often produce too few marine low clouds in midlatitudes and clouds in tropical regions that are excessively bright, meaning they reflect too much solar radiation. The marine boundary layer is frequently characterized by widespread overcast cloud cover that often transitions from a continuous, single-layer deck to more broken cloud fields toward the tropics. These transitions typically proceed through an intermediate stage in which shallow, broken clouds form beneath the overlying stratiform cloud deck. Once broken clouds develop below the overcast, they frequently self-organize into cloud clusters known as marine boundary layer convective complexes (MBLCCs), although the mechanisms governing the formation and organization of MBLCCs remain poorly understood. Accurately representing these transitions in long-range weather forecast models is essential because they influence the properties of air masses advected over the continental United States and Europe, and they become increasingly important for forecasts on seasonal and longer timescales. We employed two complementary approaches to investigate the processes controlling MBLCCs and their impact on marine cloud cover. Long-term observations from the U.S. Department of Energy’s Eastern North Atlantic (ENA) Observatory provided a unique dataset that allowed us to characterize fundamental properties of MBLCCs, including their typical size and frequency of occurrence. These observations were combined with high-resolution numerical simulations performed on supercomputers to examine the evolution of MBLCCs during cold-air outbreaks over the ENA region.

54 ENVIRONMENTAL SCIENCES↗