Unveiling Aerosol Impacts on Deep Convective Clouds: Scientific Concept, Modeling, Observational Analysis, and Future Direction
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Aerosol cloud interactions↗
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Projecting U.S. coastal storm surge risks and impacts with deep learning
Storm surge is one of the deadliest hazards posed by tropical cyclones (TCs), yet assessing its current and future risk is difficult due to the phenomenon’s rarity and physical complexity. Recent advances in artificial intelligence applications to natural hazard modeling suggest a new avenue for addressing this problem. We develop a deep learning storm surge model to efficiently estimate coastal surge risk in the United States from 900 000 synthetic TC events, accounting for projected changes in TC behavior and sea levels. The derived historical 100 year surge (the event with a 1% yearly exceedance probability) agrees well with historical observations and other modeling techniques. When coupled with an inundation model, we find that heightened TC intensities and sea levels by the end of the century result in a 50% increase in population at risk. Key findings include markedly heightened risk in Florida, and critical thresholds identified in Georgia and South Carolina.
RAFT↗
Aerosol impacts on isolated deep convection: findings from TRACER
This study focuses on quantifying the conditional relationship between aerosol and convective precipitation properties of isolated deep convective clouds (DCCs) in the Houston–Galveston region, after adjusting for confounding effects. We leverage comprehensive ground-based observations from the TRacking Aerosol Convection interactions ExpeRiment (TRACER) to estimate aerosol effects on convective echo top height (ETH), intensity, and area separately. Our results show that greater aerosol number concentrations generally have a limited impact on these convective properties, showing relationships consistent with the possibility of both invigoration and suppression effects. Under certain conditions, where ultrafine particles are abundant, aerosols exhibit a positive effect on ETH, increasing it by about 1 km. However, it is inevitable to consider measurement uncertainties and the limitations of temporal and spatial resolution in the data, as these factors can further contribute to uncertainties in our estimates. In DCCs associated with sea breezes, the estimated aerosol effects on DCCs are found to be more pronounced. However, this heightened effect could be attributed to the exclusion of key confounders such as boundary layer updrafts in the analysis.
54 ENVIRONMENTAL SCIENCES↗
Assessment of Deep Burnup HALEU Fuel Impact and Requirements
This report explores advancements in nuclear fuel technology, focusing on once-through high-assay low-enriched uranium (HALEU) fuels. It examines the potential for increased fuel residence time in reactors and the fuel cost implications. The study highlights the differences between fast and thermal reactors in terms of fuel enrichment and burnup, emphasizing the complex relationship in fast reactors in which increased core size and fuel density can lead to lower enrichment requirements. Various advances in fuel technologies for light-water reactors (LWR) and sodium-cooled fast reactors (SFR) are presented in this report to provide more comprehensive analysis.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
TAMU TRACER: Targeted Mobile Measurements to Isolate the Impacts of Aerosols and Meteorology on Deep Convection
Difficulty in using observations to isolate the impacts of aerosols from meteorology on deep convection often stems from inability to resolve the spatiotemporal variations in the environment serving as the storm’s inflow region. During the DOE TRacking Aerosol Convection interactions ExpeRiment (TRACER) in June-September 2022, a Texas A&M University (TAMU) team conducted a mobile field campaign to characterize the meteorological and aerosol variability in airmasses that serve as inflow to convection across the ubiquitous mesoscale boundaries associated with the sea- and bay-breezes in the Houston, Texas, region. These boundaries propagate inland over the fixed DOE Atmospheric Radiation Measurement (ARM) sites. However, convection occurs on either or both the continental or maritime sides or along the boundary. The maritime and continental airmasses serving as convection inflow may be quite distinct, with different meteorological and aerosol characteristics that fixed-site measurements cannot simultaneously sample. Thus, a primary objective of TAMU TRACER was to provide mobile measurements similar to those at the fixed sites, but in the opposite airmass across these moving mesoscale boundaries. TAMU TRACER collected radiosonde, lidar, aerosol, cloud condensation nuclei (CCN), and ice nucleating particle (INP) measurements on 29 enhanced operations days covering a variety of maritime, continental, outflow, and pre-frontal airmasses. This paper summarizes the TAMU TRACER deployment and measurement strategy, instruments, available datasets, and provides sample cases highlighting differences between these mobile measurements and those made at the ARM sites. We also highlight the exceptional TAMU TRACER undergraduate student participation in high impact learning activities through forecasting and field deployment opportunities.
54 ENVIRONMENTAL SCIENCES↗
Model Intercomparison of the Impacts of Varying Cloud Droplet–Nucleating Aerosols on the Life Cycle and Microphysics of Isolated Deep Convection
The microphysical impacts of aerosol particles on scattered isolated deep convective cells near Houston, Texas, on 19 June 2013, are examined using multiple cloud-resolving model (CRM) simulations initialized with vertical profiles of low and high concentrations of cloud droplet–nucleating aerosols. These simulations formed part of the Model Intercomparison Project (MIP) conducted by the Deep Convective Working Group of the Aerosol, Cloud, Precipitation and Climate (ACPC) initiative. Each CRM generated a field of convective cells representing those observed during the case study with varying degrees of accuracy. The Tracking and Object-Based Analysis of Clouds (tobac) cell-tracking algorithm was applied to each MIP CRM simulation to track relatively long-lived convective cells (20–60 min). Most of the CRMs produced similar aerosol loading impacts on the warm phase of tracked cell properties with reduced autoconversion and accretion growth of rain, increased cloud water, reduced rainfall, and reduced near-surface evaporation of rain. The sign of aerosol impacts on the warm-phase properties of the convective cells was also quite consistent over cell lifetimes with the greatest magnitude of influence in the first half of the life cycle in most CRMs. In contrast, the ice-phase response to aerosol loading was highly variable among CRMs and included increases or decreases in ice amounts at inconsistent stages of the cell life cycle and midlevel versus upper-level changes in ice. This intermodel variability in ice is indicative both of the complex indirect interactions between aerosols and ice-phase processes in deep convection and their associated parameterizations.
Aerosol-cloud interaction↗
Variability in Ice Nucleating Particles Across Greater Houston Texas
The concentration and cloud-forming potential of a region's ice nucleating particle (INP) population have uncertain impacts on deep convective clouds. Specifically, ice nucleating particles (INPs) may affect various cloud properties related to the formation, lifetime, and precipitation of deep convective clouds. As part of the U.S. Department of Energy's TRacking Aerosol and Convection interaction ExpeRiment (TRACER) campaign, researchers from Texas A&M University deployed three Davis Rotating-drum Universal-size-cut Monitoring (DRUM) samplers throughout Greater Houston, Texas from June through September 2022. Ambient particles, collected at the surface with the DRUM samplers in four aerodynamic diameter size ranges (>3, 3–1.2, 1.2–0.34, and 0.34–0.15 μm), were analyzed in offline cold-stage ice nucleation experiments. The INP population in Greater Houston is complex, varying by site and day, but can be generalized by a weak to moderately efficient mode of INPs at −24°C and an efficient mode at −15°C. Analysis reveals that supermicron particles are largely responsible for ice nucleation warmer than −20°C across the region while submicron particles dominate at temperatures colder than −20°C. Additionally, significant spatial diversity in the INP population was observed, with differences in mean nucleation temperature between sites for nearly every size cut. Although INP concentrations were typically ∼0.08 L −1 at −20°C throughout the campaign, a notable region-wide increase in INP concentration for particles freezing at temperatures warmer than −20°C occurred from mid-August to mid-September. This comprehensive characterization of Greater Houston's INP population, including spatial, temporal, and particle size variations, can help constrain ice microphysics parameterizations in weather and climate models.
Thompson, Seth A. [Texas A & M Univ., College Stat↗
Identifying Potential Geochemical and Microbial Impacts of Hydrogen Storage in a Deep Saline Aquifer
Hydrogen is valuable commodity and a promising energy carrier for variable energy production. Storage of hydrogen may occur through injection of hydrogen or a hydrogen/methane gas blend in subsurface reservoirs. However, the geochemical and biological reactions that may impact the stored hydrogen are not yet understood. Therefore, we collected samples from a deep storage aquifer located in the St. Peter Formation in southern Illinois. The reservoir material was primarily quartz with sulphur and iron deposits, while the major constituents of the fluid were chloride and sulphate. 16S rRNA gene amplicon sequencing revealed a low biomass microbial community that contained no obvious hydrogen-consuming bacteria. Next, we enriched a field sample to increase the biomass and completed a metagenomic analysis, finding a low number of genes present that are associated with hydrogen consumption. Then, we completed a series of reactor experiments under reservoir conditions with 15% H2/85% CH4 gas simulating a short-term hydrogen storage, high withdrawal scenario. We found minimal changes in the geochemistry or microbiology for the reactor experiments. This work suggests that short-term storage may be highly successful, although significant additional work needs to be completed in order to accurately evaluate the risks associated with long-term hydrogen storage scenarios. It is essential we continue to expand our understanding of the dynamics present in saline aquifers and provide new insights into how hydrogen storage may impact underground geological storage environments.
54 ENVIRONMENTAL SCIENCES↗
Impact of Penetrating Kelvin Waves on MJO Rainfall Propagation Through Deep Convection Modulation
The Madden‐Julian Oscillation (MJO) is a dominant intraseasonal oscillation in the tropics. As MJOs propagate over the Indian Ocean, some of them can be penetrated by fast‐moving equatorial Kelvin waves originating to the west of the MJOs. Through a series of sensitivity experiments, we demonstrate that these penetrating Kelvin waves can modulate the eastward propagation speed of MJO rainfall. Deep convection in the western MJO rainfall envelope initiates more frequently and produces greater lifetime rainfall when a convectively active Kelvin phase is present, while the opposite occurs during a convectively suppressed Kelvin phase. This alteration in deep convection activity can lead to notable changes in the propagation of the large‐scale MJO rainfall envelope. The modulation of deep convection lifetime rainfall by penetrating Kelvin waves primarily occurs through their impact on deep convection rainfall area, which is closely related to the variations in lower‐tropospheric moisture induced by the Kelvin waves.
Chen, Xingchao [Pennsylvania State Univ., Universi↗
Targeted Mobile Measurements to Isolate the Impacts of Aerosols and Meteorology on Deep Convection
One of the main goals of the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Tracking Aerosol Convection Interactions Experiment (TRACER) field campaign near Houston, TX was to improve understanding of meteorology and aerosol effects on deep convective storms. To help TRACER achieve its goals of disentangling aerosol-cloud interactions from meteorology, the Texas A&M University (TAMU) team deployed the new fully mobile Rapid Onsite Atmospheric Measurement Van (ROAM-V) carrying instruments to measure aerosol and atmospheric properties that was moved to multiple locations on a deployment day of isolated convection initiated by the sea breeze.
54 ENVIRONMENTAL SCIENCES↗
Investigating the Impact of the QBO on Tropical Deep Convection and the MJO in Free-Running and Nudged E3SM Simulations
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58 GEOSCIENCES↗
Impact of carbon dioxide removal technologies on deep decarbonization: EMF37 MARKAL–NETL modeling results
Here this paper examines the MARKAL-NETL modeling results for the Energy Modeling Forum Study on Deep Decarbonization & High Electrification Scenarios for North America (EMF 37) with specific focus on carbon dioxide removal (CDR) technologies and opportunities under different scenarios guidelines, policies, and technological advancements. The results demonstrate that CDR, such as, bioenergy with carbon capture and storage (BECCS), direct air capture (DAC) and afforestation are key negative emission technologies in deep decarbonization scenarios in the U.S. are accounted for about 70% of annually avoided carbon dioxide emissions (CO 2 ) by 2050, or more than 2 billion tons of CO 2 (GtCO 2 ). The potential scale of CDR and its impact on the energy system depends on energy supply and demand technologies advancement and their costs, the level of end-use sectors electrification, availability and costs of CDR. Results show that the carbon prices are substantially lower if the advanced technologies available, particularly, in carbon management scenarios.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
The SPT-deep Cluster Catalog: Sunyaev–Zel’dovich Selected Clusters from Combined SPT-3G and SPTpol Measurements over 100 Square Degrees
We present a catalog of 500 galaxy cluster candidates in the SPT-Deep field: a 100 deg$^{2}$ field that combines data from the SPT-3G and SPTpol surveys to reach noise levels of 3.0, 2.2, and 9.0 μK-arcmin at 95, 150, and 220 GHz, respectively. Candidates are selected via the thermal Sunyaev–Zel’dovich (SZ) effect with a minimum significance of ξ = 4.0, resulting in a catalog of purity ∼89%. Optical data from the Dark Energy Survey and infrared data from the Spitzer Space Telescope are used to confirm 442 cluster candidates. The clusters span 0.12 < z ≲ 1.8 and 1.0 × 10$^{14}$M$_{⊙}$/h$_{70}$ < M$_{500c}$ < 8.7 × 10$^{14}$M$_{⊙}$/h$_{70}$. The sample’s median redshift is 0.74, and the median mass is 1.7 × 10$^{14}$M$_{⊙}$/h$_{70}$; these are the lowest median mass and highest median redshift of any SZ-selected sample to date. We assess the effect of infrared emission from cluster member galaxies on cluster selection by performing a joint fit to the infrared dust and tSZ signals by combining measurements from SPT and overlapping submillimeter data from Herschel/SPIRE. We find that at high redshift (z > 1), the tSZ signal is reduced by $17.9_{−3.2}^{+3.8}$%$(3.8_{−0.7}^{+0.9}$%$)$ at 150 GHz (95 GHz) due to dust contamination. We repeat our cluster finding method on dust-nulled SPT maps and find the resulting catalog is consistent with the nominal SPT-Deep catalog, suggesting dust contamination does not significantly impact the SPT-Deep selection function; we attribute this lack of bias to the inclusion of the SPT 220 GHz band.
79 ASTRONOMY AND ASTROPHYSICS↗
Surface Trap Dynamics and Gamma‐Ray Detection Enhancement in Passivated FAPbBr 3 Perovskite Crystals
Perovskite‐based direct radiation detectors offer a compelling platform for room‐temperature gamma spectroscopy due to their high sensitivity, tunable optoelectronic properties, and compatibility with solution processing. While prior studies on formamidinium lead tribromide (FAPbBr 3 ) have shown that surface passivation mitigates deep trap state impact on the charge collection efficiency (CCE), the influence of surface‐based shallow traps on time‐resolved detector response remains largely unexplored. In this article, a digital signal processing (DSP) method is applied to analyze waveform rising edge dynamics in FAPbBr 3 single‐crystal detectors, identifying prompt and delayed charge collection components. Using the second derivative of the collected charge signal, the onset of shallow trap reemission is isolated, occurring 1.1 µs after carrier generation. Surface passivation suppressed deep trap states, shifting carrier occupancy toward shallower traps and extending the temporal window of delayed charge collection. This delayed contribution correlates with measurable changes in the temporal and amplitude distributions of detector events. By leveraging time‐domain filtering informed by these dynamics, a selective enhancement in signal quality and gamma peak‐to‐Compton ratio (PCR) is achieved, while preserving over 90% of photoelectric events. An improvement in the PCR to 1.80, from the original 1.09, is enabled by focusing on high collection efficiency events corresponding to reemitted carriers in the 1.1 to 3 µs range after the generation event. These results demonstrate a pathway for leveraging shallow trap dynamics to enhance spectral fidelity in time‐resolved perovskite detectors.
36 MATERIALS SCIENCE↗
Impacts of Hydrogen Bond Donor Structures in Phenolic Aldehyde Deep Eutectic Solvents on Pretreatment Efficiency
As a green solvent for biomass processing, deep eutectic solvents (DESs) have shown effectiveness in biomass processing. Here, in this study, phenolic aldehydes with different numbers of methoxy groups, including 4-hydroxybenzaldehyde (HBA, no methoxy), vanillin (VA, monomethoxy), and syringaldehyde (SA, dimethoxy) were employed to synthesize DESs with choline chloride (ChCl). The presence of methoxy groups in the hydrogen bond donor structure affected DES properties, as well as biomass pretreatment performance. The high thermal stability of phenolic aldehyde DESs was shown with over 225 °C onset temperature. The hydrogen bond donor with one aldehyde and one hydroxyl group at the para position without a methoxy group (ChCl-HBA) showed the highest xylan removal and delignification, reaching 59.3 and 88.0%, respectively, leading to the highest enzymatic hydrolysis yield. Sonication after pretreatment further enhanced the hydrolysis yields, achieving 83.3% glucan conversion and 50.1% xylan conversion. In the lignin-rich fraction, the recovered lignin showed a low weight–average molecular weight under 2100 g/mol with a relatively uniform molecular weight dispersity below 1.5. This study provides insights into how the chemical structure of hydrogen bond donors in DESs affects biomass processing and paves the way for designing effective lignin-derived DES in future biorefinery processes.
09 BIOMASS FUELS↗
Efficient screening of rare large pit anomalies on polished surfaces using a minimalist sampling scheme
Lawrence Livermore National Laboratory (LLNL) has made significant strides in generating clean energy through its inertial confinement fusion (ICF) experiments. These experiments rely on high-density carbon (HDC) coated shells to encapsulate the fusion fuel. The success of these experiments is heavily dependent on the surface quality of these shells, as even minor imperfections, such as deep pits, can negatively impact fusion yield. Ensuring the required smoothness involves an extensive surface-finishing process that spans approximately 20 stages, making it both time-intensive and resource-demanding. A critical challenge in this process is the need for high-resolution scans to detect rare deep pits, which can be costly and impractical if performed on every shell. This highlights the necessity of developing more efficient scanning methods to optimize time and cost without compromising accuracy. To address these challenges, we introduce a novel approach that employs the multivariate Dvoretzky–Kiefer–Wolfowitz (DKW) inequality to provide a probabilistic upper bound on the error in estimating pit distribution characteristics via a Kernel Density Estimator (KDE). This error bound enables efficient and reliable estimation of pit distribution characteristics at a specified statistical confidence level using a minimal number of surface scans. The integrated DKW-KDE approach was validated through surface-finishing experiments across two batches of HDC-coated shells, demonstrating consistent and robust performance across multiple stages of the surface-finishing experiments. The validation studies suggest that the integrated DKW-KDE approach achieves comparable accuracy in estimating the risk of deleterious large pits with six scans, thus conserving time and resources. Further evaluations show that performance remains consistent across batches and over multiple polishing stages. In conclusion, based on these findings, one can leverage the minimal-scan insights to strategically improve the bottleneck inspection process, thus enhancing the productivity and quality of shell polishing and similar challenging manufacturing processes.
Inertial confinement fusion↗
Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications
Run to run variability in parallel programs caused by floating-point non-associativity has been known to significantly affect reproducibility in iterative algorithms, due to accumulating errors. Non-reproducibility can critically affect the efficiency and effectiveness of correctness testing for stochastic programs. Recently, the sensitivity of deep learning training and inference pipelines to floating-point non-associativity has been found to sometimes be extreme. It can prevent certification for commercial applications, accurate assessment of robustness and sensitivity, and bug detection. New approaches in scientific computing applications have coupled deep learning models with high-performance computing, leading to an aggravation of debugging and testing challenges. Here we perform an investigation of the statistical properties of floating-point non-associativity within modern parallel programming models, and analyze performance and productivity impacts of replacing atomic operations with deterministic alternatives on GPUs. We examine the recently-added deterministic options in PyTorch within the context of GPU deployment for deep learning, uncovering and quantifying the impacts of input parameters triggering run to run variability and reporting on the reliability and completeness of the documentation. Finally, we evaluate the strategy of exploiting automatic determinism that could be provided by deterministic hardware, using the Groq LPUTM accelerator for inference portions of the deep learning pipeline. We demonstrate the benefits that a hardware-based strategy can provide within reproducibility and correctness efforts.
Shanmugavelu, Sanjif↗