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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A riming‐dependent parameterization of scattering by snowflakes using the self‐similar Rayleigh–Gans approximation

Abstract Riming is a key process of precipitation formation in ice‐containing clouds, but quantifying riming from observations is challenging, limiting our ability to evaluate the riming process in numerical weather models. One challenge for radar observations is that riming changes both the physical properties (mass, area cross‐section) and scattering properties of ice particles. These changes need to be implemented consistently as a function of riming in radar forward operators, which are required for retrievals and model evaluation in observation space. In this study, mass–size, cross‐section area–size, and backscattering cross‐section relations are developed as a function of the normalized rime mass for aggregates composed of various monomer types (columns, dendrites, needles, plates, and rosettes). The proposed framework allows us to simulate scattering properties of aggregated ice particles consistently as a function of riming in retrievals and radar forward operators. The parameterizations are developed from a large data set of simulated rimed aggregates of different sizes and monomer crystal types. The backscattering cross‐section parameterization (the “riming‐dependent parameterization”) is evaluated for radar frequencies of 35.6 and 94.0 GHz and is based on the Self‐Similar Rayleigh–Gans approximation (SSRGA), which is increasingly used to calculate microwave scattering of ice crystals and snowflakes. Compared with parameterizations from the literature that do not consider riming, the riming‐dependent parameterization leads to significantly smaller biases in terms of backscattering cross‐section. When using the particle masses and scattering properties of the individual particles simulated by the aggregation and riming model as a reference, the bias of our parameterization is below 1 dB when integrating over an exponential particle size distribution with sizes from 0.1–10 mm.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

First DIII-D-West hybrid scenario similarity experiments for iter-relevant long-pulse operation

For the first time, similarity experiments between DIII-D and WEST were performed in the ITER "hybrid-like" regime during dedicated campaigns in April and May 2025. The matched parameters include elongation, triangularity, ion ∇B drift direction toward the X-point, qprofile, and core normalized physics quantities in terms of normalized pressure, normalized gyroradius, electron collisionality, ratio of ion to electron temperature, T i /T e . Core transport physics is explored with different aspect ratio (R/a) values (typically 3 at DIII-D and 5 on WEST). DIII-D explored high-beta conditions (electromagnetic effect) with low torque injection (~0 ± 0.5 N•m) using high heating power (up to 6 MW NBI and 2 MW ECRH powers), while scanning the heating mix (ion vs electron), beta, T i /T e , core radiation via controlled tungsten injection using the Laser Blow-Off system. WEST extended operation toward long-duration pulses using its actively cooled tungsten divertor, achieving dominated electron heating regimes with reduced tungsten contamination. Boron impurity injection were scanned on WEST to control edge conditions and core performance. It is found that core confinement improves-manifested by higher electron temperature, total energy content, neutron rate, and ion temperatureunder conditions of low separatrix density, consistent with previous observations [Bourdelle et al., Nucl. Fusion 63 (2023) 056021]. Conditions for Hmode access and for ion heating in electron-dominated regimes in both WEST and DIII-D will be discussed and compared. The ratio of the thermal energy confinement time (τ E ) to the volume-averaged electron-ion collisional heat exchange time (τ e-i ) is a key parameter to enhance ion heating and potentially facilitate H-mode access in electron-heated regimes. These first-of-a-kind coordinated DIII-D and WEST experiments provide a unique multi-machine dataset to validate predictive models and to optimize ITER hybrid-scenario performance under diverse core and edge conditions.

DIII-D↗
DOE OSTI2023

Similarity Downselection: Finding the n Most Dissimilar Molecular Conformers for Reference-Free Metabolomics

Computational methods for creating in silico libraries of molecular descriptors (e.g., collision cross sections) are becoming increasingly prevalent due to the limited number of authentic reference materials available for traditional library building. These so-called “reference-free metabolomics” methods require sampling sets of molecular conformers in order to produce high accuracy property predictions. Due to the computational cost of the subsequent calculations for each conformer, there is a need to sample the most relevant subset and avoid repeating calculations on conformers that are nearly identical. The goal of this study is to introduce a heuristic method of finding the most dissimilar conformers from a larger population in order to help speed up reference-free calculation methods and maintain a high property prediction accuracy. Finding the set of the n items most dissimilar from each other out of a larger population becomes increasingly difficult and computationally expensive as either n or the population size grows large. Because there exists a pairwise relationship between each item and all other items in the population, finding the set of the n most dissimilar items is different than simply sorting an array of numbers. For instance, if you have a set of the most dissimilar n = 4 items, one or more of the items from n = 4 might not be in the set n = 5. An exact solution would have to search all possible combinations of size n in the population exhaustively. We present an open-source software called similarity downselection (SDS), written in Python and freely available on GitHub. SDS implements a heuristic algorithm for quickly finding the approximate set(s) of the n most dissimilar items. We benchmark SDS against a Monte Carlo method, which attempts to find the exact solution through repeated random sampling. We show that for SDS to find the set of n most dissimilar conformers, our method is not only orders of magnitude faster, but it is also more accurate than running Monte Carlo for 1,000,000 iterations, each searching for set sizes n = 3–7 out of a population of 50,000. We also benchmark SDS against the exact solution for example small populations, showing that SDS produces a solution close to the exact solution in these instances. Using theoretical approaches, we also demonstrate the constraints of the greedy algorithm and its efficacy as a ratio to the exact solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Scaling similarities and quasinormal modes of D0 black hole solutions

We study the gravity solution dual to the D0 brane quantum mechanics, or BFSS matrix model, in the ’t Hooft limit. The classical physics described by this gravity solution is invariant under a scaling transformation, which changes the action with a specific critical exponent, sometimes called the hyperscaling violating exponent. We present an argument for this critical exponent from the matrix model side, which leads to an explanation for the peculiar temperature dependence of the entropy in this theory, S ∝ T 9/5 . We also present a similar argument for all other Dp-brane geometries. We then compute the black hole quasinormal modes. This involves perturbing the finite temperature geometry. These perturbations can be easily obtained by a mathematical trick where we view the solution as the dimensional reduction of an Ad S2+9/5 × S 8 geometry.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Supplemental dietary full-fatted and defatted Desmodesmus sp. exerted similar effects on growth performance, gut health, and excreta hydrothermal liquefaction of broiler chicks

This study determined effects of full-fatted (C046, 41% crude protein) and lipid extracted (LEA, 39% crude protein) microalgae Desmodesmus sp. on growth performance, gut health, and excreta hydrothermal liquefaction of broiler chickens. Two experiments were conducted by feeding day-old Cornish male chicks: 1) a corn-soybean meal basal diet (control) 2) control +5% C046, or 3) control +5% LEA for 2 weeks. Growth performance was measured weekly in both experiments. Blood, liver, and duodenum samples of chickens were collected for biochemical analyses, and excreta samples were collected for hydrothermal liquefaction (300 °C, 60 min) at the end of Experiment 2. Compared with the control, supplemental dietary C046 and LEA enhanced average daily gain (13% to 40%, p < 0.05) in both experiments, and improved feed efficiency (15% and 11%, p < 0.01) in Experiment 2. The C046 and LEA diets enhanced the duodenum (19% to –2 fold, p < 0.05), but decreased the liver (17 to 78%, p < 0.05) mRNA abundances of interleukins-1, 6 and 8 and (or) tumor necrosis factor-α, respectively, compared with the controls. Meanwhile, the two types of biomass enhanced the protein amounts of claudin-1 in the duodenum (5–34%, p < 0.05) than the controls. Hydrothermal liquefaction of the excreta derived from the three diets showed no major differences in total bio-crude oil (average of 23%), hydrochar yield (average of 12%), or elemental compositions. However, the heating values of the resultant two bio-crude oils from the microalgae-fed chicken excreta (average of 34 MJ/kg) were 16% greater than those from the control chickens (29 MJ/kg). In conclusion, supplementing the full- and de-fatted Desmodesmus sp. microalgal biomass in the starter diets for broiler chickens produced largely similar improvements of growth performance, gut health, and excreta-derived oil quality.

09 BIOMASS FUELS↗
DOE OSTI2021

Intramedullary Unicortical Button and All-Suture Anchors Provide Similar Maximum Strength for Onlay Distal Biceps Tendon Repair

To evaluate the biomechanical profile of onlay distal biceps repair with an intramedullary unicortical button versus all-suture anchors under cyclic loading and maximal load to failure. Twenty paired fresh-frozen human cadaveric elbows were randomized to onlay distal biceps repair with either a single intramedullary button or with two 1.35-mm all-suture anchors. A 1.3-mm high tensile strength tape was used in a Krackow stitch to suture the tendons in both groups. Specimens and repair constructs were loaded for 3,000 cycles and then loaded to failure. Maximum load to failure, mode of failure, and construct elongation were recorded. Mean (± standard deviation) maximum load to failure for the unicortical intramedullary button and all-suture anchor repairs were 503.23 ± 141.77 N and 537.33 ± 262.13 N (P = .696), respectively. Mean maximum displacement after 3,000 cycles (± standard deviation) was 4.17 ± 2.05 mm in the button group and 2.06 ± 1.05 mm in the suture anchor group (P = .014). Mode of failure in the button group was suture tape rupture in 7 specimens, failure at the tendon–suture interface in 2 specimens, and button pullout in 1 specimen. Anchor pullout was the mode of failure in all suture anchor specimens. There were no tendon ruptures or radial tuberosity fractures in either group. In conclusion, this study demonstrates that onlay distal biceps repair with 2 all-suture anchors has similar maximum strength to repair with an intramedullary button and that both are viable options for fixation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Stress corrosion cracking mechanism of cold spray coating on a galvanically similar substrate

Here, the chloride-induced stress corrosion cracking (CISCC) mechanism of cold spray (CS) coating on a galvanically similar substrate is investigated. Arc welded 304L stainless steel (SS) specimens are loaded into four-point bend fixtures, cold sprayed with 304L SS, then immersed in boiling MgCl 2 . Interconnected porosity forms through crevice corrosion along CS splat boundaries, allowing corrosive species to penetrate through the CS layer. Nevertheless, the substrate is resistant to CISCC likely because of compressive stress introduced by peening during CS particle impacts. These findings underscore the importance of residual stress in the environmental degradation of CS coatings or repairs of engineering structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Active microbial biomass decreases, but microbial growth potential remains similar across soil depth profiles under deeply-vs. shallow-rooted plants

Climate-smart land management practices that replace shallow-rooted annual crop systems with deeply-rooted perennial plants can contribute to soil carbon sequestration. However, deep soil carbon accrual may be influenced by active microbial biomass and their capacity to assimilate fresh carbon at depth. Incorporating active microbial biomass, dormancy, and growth in microbially-explicit models can improve our ability to predict soil's capacity to store carbon. But, so far, the microbial parameters that are needed for such modeling are poorly constrained, especially in deep soil layers. Here, we used a lab incubation experiment and growth kinetics model to estimate how microbial parameters vary along 240 cm of soil depth in profiles under shallow- (soy) and deeply-rooted (switchgrass) plants 11 years after plant cover conversion. We also assessed resource origin and availability (total organic carbon, 14 C, extractable organic carbon, specific UV absorbance of K 2 SO 4 extractable organic C, total nitrogen, total dissolved nitrogen) along the soil profiles to examine associations between soil chemical and biological parameters. Even though root biomass was greater and rooting depth was deeper under switchgrass than soy, resource availability and microbial growth parameters were generally similar between vegetation types. Instead, depth significantly influenced soil chemical and biological parameters. For example, resource availability and total and relative active microbial biomass decreased with soil depth. Decreases in the relative active microbial biomass coincided with increased lag time (response time to external carbon inputs) along the soil profiles. Even at a depth of 210–240 cm, microbial communities were activated to grow by added resources within a day. Maximum specific growth rate decreased to a depth of 90 cm and then remained consistent in deeper layers. Our findings show that >10 years of vegetation and rooting depth changes may not be long enough to alter microbial growth parameters, and suggest that at least a portion of the microbial community in deep soils can grow rapidly in response to added resources. Our study determined microbial growth parameters that can be used in microbially-explicit models to simulate carbon dynamics in deep soil layers.

14C↗
DOE OSTI2021

A human cell type similar to murine central nervous system perivascular fibroblasts

Highlights: • Human diploid cell lines were established from tumor and non-tumor brain tissue. • Human cells share a common transcriptomic profile with murine perivascular fibroblasts. • Human cells resemble commercially available cell of presumed pericyte origin. The brain vasculature has several specific features, one of them being the blood-brain barrier (BBB), which supports and protects the brain by allowing for the passage of oxygen and nutrients, while at the same time preventing passage of pathogens and toxins. The BBB also prevents efficient delivery of drugs to the brain, e.g. for treatment of brain tumors. In the murine brain, perivascular fibroblasts were recently identified as a novel potential constituent of the BBB. Here we present the existence of human cells that could be the equivalent to the murine brain perivascular fibroblasts. Using RNA sequencing, we show a similar transcriptomic profile of cultured human brain cells and murine perivascular fibroblasts. These data open up a window for new hypotheses on cell types involved in human CNS diseases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Self-similarity of scalar isosurface area density in a temporal mixing layer

Understanding the behaviour of scalar isosurfaces in a turbulent flow is of particular interest for many problems in turbulent mixing that contain sharp interfaces between regions of the flow. Common examples include combustion, where the chemical reactions occur in thin regions within the flow, and the turbulent/non-turbulent interface in shear flows, where a thin region separates the rotational, turbulent motions from the irrotational, non-turbulent background. Recent advances in computing technology allow for in-depth analysis of these interface problems that are difficult to quantify in a laboratory setting. In this paper, the results of a direct numerical simulation of a passive scalar $\varPhi$ evolving on a turbulent, temporally developing mixing layer are described. A novel approach has been taken to calculate the surface area of individual scalar isosurfaces, $A_{iso}$ , throughout the simulation, as well as the mean isosurface area density, $\varSigma$ , as a function of the cross-stream direction and time. A notable finding is that the profiles of $\varSigma$ develop in a self-similar manner when scaled by the Taylor scale of the scalar field, $\lambda _\phi$ . Remarkably, the scaling appears to hold for a wide range of isovalues. A rough scaling argument based on the formal definition of $\varSigma$ and properties of a temporal mixing layer is presented which also exposes a dependence on $\lambda _\phi$ . Based on these results, a possible scaling for the isosurface area is presented as $A_{iso}/A_0 \sim (Re \, Sc)^{1/2}$ , where $Re$ and $Sc$ are local Reynolds and Schmidt numbers, respectively.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Plasma image classification using cosine similarity constrained convolutional neural network

Plasma jets are widely investigated both in the laboratory and in nature. Astrophysical objects such as black holes, active galactic nuclei and young stellar objects commonly emit plasma jets in various forms. With the availability of data from plasma jet experiments resembling astrophysical plasma jets, classification of such data would potentially aid in not only investigating the underlying physics of the experiments but also the study of astrophysical jets. In this work we use deep learning to process all of the laboratory plasma images from the Caltech Spheromak Experiment spanning two decades. We found that cosine similarity can aid in feature selection, classify images through comparison of feature vector direction and be used as a loss function for the training of AlexNet for plasma image classification. We also develop a simple vector direction comparison algorithm for binary and multi-class classification. Using our algorithm we demonstrate 93 % accurate binary classification to distinguish unstable columns from stable columns and 92 % accurate five-way classification of a small, labelled data set which includes three classes corresponding to varying levels of kink instability.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Multireference Equation-of-Motion Driven Similarity Renormalization Group: Theoretical Foundations and Applications to Ionized States

We present a formulation and implementation of an equation-of-motion (EOM) extension of the multireference driven similarity renormalization group (MR-DSRG) formalism for ionization potentials (IP-EOM-DSRG). The IP-EOM-DSRG formalism results in a Hermitian generalized eigenvalue problem, delivering accurate ionization potentials for strongly correlated systems. The EOM step scales as O(N 5 ) with the basis set size N, allowing for efficient calculation of spectroscopic properties, such as transition energies and intensities. The IP-EOM-DSRG formalism is combined with three truncation schemes of the parent MR-DSRG theory: an iterative nonperturbative method with up to two-body excitations [MR-LDSRG(2)] and second- and third-order perturbative approximations [DSRG-MRPT2/3]. We benchmark these variants by computing (1) the vertical valence ionization potentials of a series of small molecules at both equilibrium and stretched geometries; (2) the spectroscopic constants of several low-lying electronic states of the OH, CN, N 2 + , and CO + radicals; and (3) the binding curves of low-lying electronic states of the CN radical. A comparison with experimental data and theoretical results shows that all three IP-EOM-DSRG methods accurately reproduce the vertical ionization potentials and spectroscopic constants of these systems. Notably, the DSRG-MRPT3 and MR-LDSRG(2) versions outperform several state-of-the-art multireference methods of comparable or higher cost.

Hamiltonians↗
DOE OSTI2025

Similarity Metric for Data Optimization and Efficient Training of Reactive Machine Learning Force Fields for Hydrocarbon Radiolysis

Radiolysis is a common approach to sterilize polymers, chemically modify them for upcycling, and accelerate their decomposition for recycling purposes. Reactive molecular dynamics (MD) simulations provide a powerful tool to generate atomic-level trajectories of the reactive processes and quantify radiolytic chemical degradation pathways. For this, machine learning (ML) surrogate models for reactive force fields with quantum mechanical accuracy are now widely used, which require ML training data sets that can provide information on atomic environments for target chemical systems. However, radiolysis chemistry can be highly complex and diverse, which poses significant challenges for generating training data to parametrize ML models. In this regard, we developed a method for optimizing the training data set using a cosine similarity metric to help guide training set selection for radiolysis of polyethylene, a model hydrocarbon polymer, as well as to enhance the transferability of our reactive ML force field (MLFF) to a variety of molecular and polymeric systems. Our approach performs atom-by-atom comparisons between local atomic environments to pinpoint important data points associated with rare and localized events, such as radiolysis damage within structures. We apply this approach to train the Chebyshev Interaction Model for Efficient Simulation (ChIMES) MLFF model, which expresses the atomic interaction potentials in terms of linear combinations of many-body Chebyshev polynomials. We first show that our method can reduce our training set size by ∼70% while improving overall accuracy compared to more standard MD model fitting approaches. We then validate our optimum model against diverse hydrocarbon simulation data, including simple alkanes and systems with unsaturated carbon bonds, over a wide range of thermodynamic conditions. Finally, we use our ChIMES model to perform MD simulations of radiolytic damage with large-scale systems that help avoid system size effects. Overall, our approach yields an MD force field that retains most of the accuracy of the underlying quantum method while yielding many orders of improvement in computational efficiency. In conclusion, our efforts will have impact on future hydrocarbon polymer radiolysis studies, where the chemical details of the polymer–radiation interactions can have a strong effect on the resulting products observed in experiments.

Hydrocarbons↗
DOE OSTI2025

Scalable Implementation of Mean-Field and Correlation Methods Based on Lie-Algebraic Similarity Transformation of Spin Hamiltonians in the Jordan–Wigner Representation

Recent work has highlighted that the strong correlation inherent in spin Hamiltonians can be effectively reduced by mapping spins to Fermions via the Jordan−Wigner transformation (JW). The Hartree−Fock method is straightforward in the Fermionic domain and may provide a reasonable approximation to the ground state. Correlation with respect to the Fermionic mean field can be recovered based on Lie-algebraic similarity transformation (LAST) with two-body correlators. Specifically, a unitary LAST variant eliminates the dependence on site ordering, while a nonunitary LAST yields size-extensive correlation energies. Whereas the first recent demonstration of such methods was restricted to small spin systems, we present efficient implementations using analytical gradients for the optimization with respect to the mean-field reference and the LAST parameters, thereby enabling the treatment of larger clusters, including systems with local spins s > $\frac{1}{2}$.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

One-Step Relativistic Driven Similarity Renormalization Group Multireference Perturbation Theory

We present an efficient implementation of a one-step relativistic second-order multireference perturbation theory based on the multireference driven similarity renormalization group (MR-DSRG) using the exact two-component (X2C) Hamiltonian, which we denote X2C-DSRG-MRPT2. We show that the X2C-DSRG-MRPT2 method can accurately capture spin–orbit coupling (SOC) effects in the electronic structure of strongly correlated systems containing elements across the periodic table. We further demonstrate that the X2C-DSRG-MRPT2 method, through its variational treatment of SOC effects, can yield spin–orbit splittings with mean absolute percentage errors consistently below 7% with respect to experimental values for systems containing up to sixth row elements. With its modest computational scaling (fourth power in system size for the perturbative step) and high accuracy, X2C-DSRG-MRPT2 provides a promising avenue for the routine treatment of relativistic effects in strongly correlated molecular systems.

Hamiltonians↗
DOE OSTI2021

Mechanistic Similarities and Differences for Hydrogenation of Aromatic Heterocycles and Aliphatic Carbonyls on Sulfided Ru Nanoparticles

This study establishes the contrasting reactivity trends for the hydrogenation of aromatic hydrocarbons (AHCs = C n H m X, X = N, S, O, and C) and aliphatic carbonyls [R a C(O)R b ; R a = alkyl group, R b = CH 3 or H] on sulfided Ru clusters arising from the difference in the elementary proton and hydride attack catalytic sequence. Both reactions require sequential additions of a proton from either Ru n+ –(SH 2 ) or S 2 –(H δ+ ) species and a hydride from Ru n+ –(H δ– ) species to the unsaturated C=X bonds. For the five-membered-ring aromatic heterocycles (AHCs = pyrrole, thiophene, and furan), an initial proton addition limits the catalytic turnovers; thus, their hydrogenation reactivity increases with increasing gas-phase proton affinities of the AHCs. Pyridine as the more basic six-membered N-AHC is more susceptible to protonation; therefore, it is more reactive, and its initial proton addition is quasi-equilibrated, followed by the kinetically relevant hydride addition. Conversely, aliphatic carbonyls prefer to undergo hydrogen additions in a reverse sequence, where a Ru n+ –(H δ– ) hydride initially attacks the electron-deficient carbonyl C atom as the kinetically relevant step before a subsequent rapid S 2 –(H δ+ ) proton addition on the electron-rich O atom, as confirmed by isotopic exchange studies with butanal-D 2 and 1-butanol-D 2 mixtures and density functional theory calculations on S-deficient RuS 2 (100) surfaces. For these reasons, their hydrogenation reactivity increases with increasing gas-phase hydride affinities of the carbonyls. On metallic Ru surfaces without sulfur, hydrogen adatoms (H*) are the only reactive hydrogen species; the reactivity of their attack on aromatic heterocycles increases with increasing reactant proton affinities much more sensitively than that on sulfided Ru surfaces. This work illustrates the distinct catalytic roles of the diverse hydrogen species in hydrogenation—the interplay between proton and hydride additions has marked catalytic consequences in shaping the free energy landscape of the reactions, which in turn leads to the observed kinetic dependences, kinetic parameters, reactivity trends, and scaling relations between the measured barriers and the appropriate kinetic descriptors, that is, proton affinity of AHCs and hydride affinity of carbonyls in hydrogenation catalysis. Here, these mechanistic similarities and differences provide explanations of the observed reactivity trends and thus have profound implications for industrial hydrogenation and hydrotreating catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Similarities in Meteorological Composites Among Different Atmospheric River Detection Tools During Landfall Over Western Coastal North America

Many atmospheric river detection tools (ARDTs) have been developed over the past few decades to identify atmospheric rivers (ARs). Different ARDTs have been observed to capture a variety of frequencies, shapes, and sizes of ARs. Due to this, questions have arisen about the underlying phenomena associated with the detected ARs: do all ARDTs detect the same meteorological phenomena? In this paper, we assess eight ARDTs and investigate the underlying synoptic scale phenomena during landfalling ARs along the west coast of North America. We find that during landfalling AR events, prevalent low-pressure and high-pressure systems converge and enhance moisture influx toward the landfalling site. We identify that all eight ARDTs identify AR conditions associated with baroclinic waves, with the region of intense integrated vapor transport (IVT) located downstream of the upper level (500 hPa) trough. The magnitude of IVT is enhanced by the strength of the pressure gradients in the confluence region. Although the ARDTs assessed agree on the general phenomena, there are however subtle differences in each ARDT per the clustering analysis we performed. We conclude that the eight ARDTs identify similar underpinning synoptic scale meteorological phenomena.

54 ENVIRONMENTAL SCIENCES↗