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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Exploring the effects of climatic variables on monthly precipitation variation using a continuous wavelet-based multiscale entropy approach

Highlights: • Developed a wavelet transform entropy approach to study precipitation. • Multiscale entropy values were the basis for precipitation regionalization. • Various entropy-based bands were introduced to segregate the CWME values for each cluster. • The relationship between changes of CWME and mean monthly precipitation were investigated. • Central rain gauges of each cluster were used for further analysis. Understanding precipitation on a regional basis is an important component of water resources planning and management. The present study outlines a methodology based on continuous wavelet transform (CWT) and multiscale entropy (CWME), combined with self-organizing map (SOM) and k-means clustering techniques, to measure and analyze the complexity of precipitation. Historical monthly precipitation data from 1960 to 2010 at 31 rain gauges across Iran were preprocessed by CWT. The multi-resolution CWT approach segregated the major features of the original precipitation series by unfolding the structure of the time series which was often ambiguous. The entropy concept was then applied to components obtained from CWT to measure dispersion, uncertainty, disorder, and diversification of subcomponents. Based on different validity indices, k-means clustering captured homogenous areas more accurately, and additional analysis was performed based on the outcome of this approach. The 31 rain gauges in this study were clustered into 6 groups, each one having a unique CWME pattern across different time scales. The results of clustering showed that hydrologic similarity (multiscale variation of precipitation) was not based on geographic contiguity. According to the pattern of entropy across the scales, each cluster was assigned an entropy signature that provided an estimation of the entropy pattern of precipitation data in each cluster. Based on the pattern of mean CWME for each cluster, a characteristic signature was assigned, which provided an estimation of the CWME of a cluster across scales of 1–2, 3–8, and 9–13 months relative to other stations. The validity of the homogeneous clusters demonstrated the usefulness of the proposed approach to regionalize precipitation. Further analysis based on wavelet coherence (WTC) was performed by selecting central rain gauges in each cluster and analyzing against temperature, wind, Multivariate ENSO index (MEI), and East Atlantic (EA) and North Atlantic Oscillation (NAO), indeces. The results revealed that all climatic features except NAO influenced precipitation in Iran during the 1960–2010 period.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Effect of Phosphate on U(VI) Sorption to Montmorillonite: Ternary Complexation and Precipitation Barriers

Phosphate addition is a potential treatment method to lower the solubility of U(VI) in soil and groundwater systems by causing U(VI) phosphate precipitation as well as enhancing adsorption. Previous work has shown that iron oxide surfaces may facilitate the nucleation of U(VI) phosphate minerals and, that under weakly acidic conditions, phosphate also enhances U(VI) adsorption to such phases. Like iron oxides, clays are important reactive phases in the subsurface but little is known about the interaction of U(VI) and phosphate with these minerals. The effect of aqueous phosphate on U(VI) binding to Wyoming montmorillonite (SWy-2) in air-equilibrated systems was investigated. Equilibrium U(VI) uptake to montmorillonite was determined at pH 4, 6 and 8 at discrete initial phosphate concentrations between 0 and 100 μM. The observed behavior of U(VI) indicates a transition from adsorption to precipitation with increasing total uranium and phosphate concentrations at all pH values. At the highest phosphate concentration examined at each pH value, a barrier to U(VI) phosphate nucleation is observed. At lower concentrations, phosphate has no effect on macroscopic U(VI) adsorption. To assess the mechanisms of U(VI)-phosphate interactions on smectite surfaces, U(VI) speciation was investigated under selected conditions using laser-induced fluorescence spectroscopy (LIFS) and extended X-ray absorption fine-structure (EXAFS) spectroscopy. Samples above the precipitation threshold display EXAFS and LIFS spectral signatures consistent with the autunite family of U(VI) phosphate minerals. However, at lower U(VI) concentrations, changes in LIFS spectra upon phosphate addition suggest that U(VI)-phosphate ternary surface complexes form on the montmorillonite surface at pH 4 and 6 despite the lack of a macroscopic effect on adsorption. The speciation of solid-associated U(VI) below the precipitation threshold at pH 8 is dominated by U(VI)-carbonate surface complexes. This work reveals that ternary complexation may occur without a macroscopic signature, which is attributed to phosphate not appreciably binding to smectite in the absence of U(VI), with U(VI) surface complexes serving as the sole reactive surface sites for phosphate. This study shows that phosphate does not enhance U(VI) adsorption to smectite clay minerals, unlike oxide phases, and that a barrier to homogeneous nucleation of U(VI) phosphates was not affected by the presence of the smectite surface

Environmental Molecular Sciences Laboratory↗
DOE OSTI2014

Antibody-independent Targeted Quantification of TMPRSS2-ERG Fusion Protein Products in Prostate Cancer

Fusions between the transmembrane protease serine 2 (TMPRSS2) and ETS related gene (ERG) represent one of the most specific biomarkers that define a distinct molecular subtype of prostate cancer. The studies on TMPRSS2-ERG gene fusions have seldom been performed at the protein level, primarily due to the lack of high-quality antibodies or an antibody-independent method that is sufficiently sensitive for detecting the truncated ERG protein products resulting from TMPRSS2-ERG gene fusions and alternative splicing. Herein, we applied a recently developed PRISM (high-pressure high-resolution separations with intelligent selection and multiplexing)-SRM (selected reaction monitoring) strategy for quantifying ERG protein in prostate cancer cell lines and tumors. The highly sensitive PRISM-SRM assays led to confident detection of 6 unique ERG peptides in either the TMPRSS2-ERG positive cell lines or tissues but not in the negative controls, indicating that ERG protein expression is highly correlated with TMPRSS2-ERG gene rearrangements. Significantly, our results demonstrated for the first time that at least two groups of ERG protein isoforms were simultaneously expressed at variable levels in TMPRSS2-ERG positive samples as evidenced by concomitant detection of two mutually exclusive peptides. Three peptides shared across almost all fusion protein products were determined to be the most abundant peptides, and hence can be used as “signature” peptides for detecting ERG overexpression resulting from TMPRSS2-ERG gene fusion. These PRISM-SRM assays provide valuable tools for studying TMPRSS2-ERG gene fusion protein products, thus improving our understanding of the role of TMPRSS2-ERG gene fusion in the biology of prostate cancer.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2015

Characterization of N-doped multilayer graphene grown on 4H-SiC (0001)

Large-area graphene film doped with hetero-atoms is of great interest for a wide spectrum of nanoelectronics applications, such as field effect devices, super capacitors, fuel cells among many others. Here, we report the structural and electronic properties of nitrogen doped multilayer graphene on 4H-SiC (0001). The incorporation of nitrogen during the growth causes an increase in the D band on the Raman signature indicating that the nitrogen is creating defects. The analysis of micro-Raman mapping of G, D, 2D bands shows a predominantly trilayer graphene with a D band inherent to doping and inhomogeneous dopant distribution at the step edges. Ultraviolet photoelectron spectroscopy (UPS) indicates an n type work function (WF) of 4.1 eV. In addition, a top gate FET device was fabricated showing n-type I-V characteristic after the desorption of oxygen with high electron and holes mobilities.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Orthorhombic to monoclinic phase transition in NbNiTe 2

We report the high-temperature structural phase transition of the potential topological material NbNiTe 2 has been studied in detail. At high temperatures, the structure is centrosymmetric with orthorhombic space-group $P\frac{2}{m}\frac{2}{n}\frac{2_1}{a}(Pmna)$, and distorts at 373 K to a monoclinic structure with space-group $P11\frac{2_1}{a}(P112_1/a)$. A small signature in specific heat and commensurate small release of entropy are observed at the phase transition. The low-temperature monoclinic structure retains its inversion center and the symmetries associated with the orthorhombic $\mathcal{c}$ axis. At high temperatures, NbNiTe 2 shows phonon instabilities at Γ and X, which are removed by the structural phase transition. The electronic structure of NbNiTe 2 is only slightly affected, indicating this ferroic phase transition is primarily due to steric effects.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Electronic noise of a single skyrmion

To enable the practical use of skyrmion-based devices, it is essential to achieve a balance between energy efficiency and thermal stability while also ensuring reliable electrical detection against noise. Understanding how a skyrmion interacts with material disorder and external perturbations is thus essential. Here, we investigate the electronic noise of a single skyrmion under the influence of thermal fluctuations and spin currents in a magnetic thin film. We detect the thermally induced noise with a 1/ƒ $\gamma$ signature in the strong pinning regime but a random telegraph noise in the intermediate pinning regime. Both the thermally dominated and current-induced telegraphlike signals are detected in the weak pinning regime. Our results provide a comprehensive electronic noise picture of a single skyrmion, demonstrating the potential of noise fluctuation as a valuable tool for characterizing the pinning condition of a skyrmion. In conclusion, these insights could also aid in the development of low-noise and reliable skyrmion-based devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Disentangling the physics of the attractive Hubbard model as a fully interacting model of fermions via the accessible and symmetry-resolved entanglement entropies

The complicated ways in which electrons interact in many-body systems such as molecules and materials have long been viewed through the lens of local electron correlation and associated correlation functions. However, quantum information science has demonstrated that more global diagnostics of quantum states like the entanglement entropy can provide a complementary and clarifying lens on electronic behavior. One particularly useful measure that can be used to distinguish between quantum and classical sources of entanglement is the accessible entanglement, the entanglement available as a quantum resource for systems subject to conservation laws, such as fixed particle number, due to superselection rules. In this work, we introduce an algorithm and demonstrate how to compute accessible and symmetry-resolved entanglements for interacting fermion systems. This is accomplished by combining an incremental version of the swap algorithm with a recursive auxiliary field quantum Monte Carlo algorithm recently developed by the authors. We apply these tools to study the pairing and charge density waves exhibited in the paradigmatic attractive Hubbard model via entanglement. We find that the particle and spin symmetry-resolved entanglements and their related full probability distribution functions show very clear—and unique—signatures of the underlying electronic behavior even when those features are less pronounced in conventional correlation functions. Altogether, this work provides a systematic means of characterizing the entanglement within quantum systems that can grant a deeper understanding of the complicated electronic behavior that underlies quantum phase transitions and crossovers in many-body systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Potential for exciton condensation in a highly conductive amorphous polymer

An outstanding challenge in synthesis and theory is to develop molecular materials at ambient conditions that exhibit highly efficient energy transfer. Here, in this study, we demonstrate the potential of a recently synthesized, highly conductive amorphous material—a nickel tetrathiafulvalene-tetrathiolate (NiTTFtt) polymer—to become an exciton condensate—a Bose-Einstein condensate of particle-hole pairs, known as excitons, that supports dissipationless flow of excitation energy. While exciton condensates have recently been realized in ordered materials, we show by advanced electronic structure calculations that this highly correlated phenomenon can potentially be realized in molecularly tailored, amorphous materials. In contrast to the Bechgaard salts that support superconductivity at compressed geometries requiring high pressures, we show that the recently synthesized, amorphous NiTTFtt polymer exhibits the computational signature of exciton condensation at experimentally realizable geometries, occurring at ambient pressures. Results suggest that superfluidity in this system and related systems—including van der Waals structures, molecular metals with extended-TTF dithiolate ligands, and Bechgaard salts—may occur via a nontraditional excitonic mechanism tuneable according to system composition, geometry, size, and charge. This study prompts further experimental investigation of the rational design of molecularly scaled exciton condensates with potential applications to efficient transport in technologically relevant materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Hole doping and electronic correlations in Cr substituted BaFe$_{2}$As$_{2}$

For a significant composition range, the suppression of the spin density wave transition temperature (T SDW ) in Cr- and Mn-substituted BaFe 2 As 2 (CrBFA and MnBFA, respectively) coincides as a function of Cr/Mn content, despite the distinct electronic effects of these substitutions. Additionally, for any Cr/Mn content superconductivity (SC) is absent and this topic is particularly less explored in the case of CrBFA. Here, in this work, we employ angle-resolved photoemission spectroscopy (ARPES) and combined density functional theory plus dynamical mean field theory (DFT+DMFT) to address the evolution of the Fermi surface (FS) and electronic correlations in CrBFA. Our findings reveal that incorporating Cr leads to an effective hole doping of the states near the FS, which is well described within the virtual crystal approximation (VCA). Moreover, analysis of the ARPES spectra of the bands with main d yz -orbital character reveals a fractional scaling of the imaginary part of self-energy as a function of the binding energy, a signature property of Hund's correlations. Our DFT+DMFT calculations support these experimental findings. We conclude that CrBFA is a correlated electron system for which the changes in the FS as a function of Cr are unrelated to the suppression of T SDW . In addition, we suggest that the absence of SC is primarily due to the competition between Cr local moments and the Fe-derived itinerant spin fluctuations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2013

Search for multiply charged Heavy Stable Charged Particles in data collected with the CMS detector

Several models of new physics yield particles that are massive, long-lived, and have an electric charge, Q, greater than that of the electron, e. A search for evidence of such particles was performed using 5.0 fb -1 and 18.8 fb -1 of proton-proton collision data collected at √s = 7 TeV and √s = 8 TeV, respectively, with the Compact Muon Solenoid detector at the Large Hadron Collider. The distinctive detector signatures of these particles are that they are slow-moving and highly ionizing. Ionization energy loss and time-of- flight measurements were made using the inner tracker and the muon system, respectively. The search is sensitive to 1e ≤ |Q| ≤ 8e. Data were found to be consistent with standard model expectations and upper limits on the production cross section of these particles were computed using a Drell-Yan-like production model. Masses below 517, 687, 752, 791, 798, 778, 753, and 724 GeV are excluded for |Q| = 1e, 2e, 3e, 4e, 5e, 6e, 7e, and 8e, respectively.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are identifying hidden geothermal resources in the USA and designing profitable enhanced geothermal systems (EGS). Many non-obvious processes and parameters could characterize geothermal resources and could control the ultimate energy potential of geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize geothermal resources, but this data is sparse and multi-scale. This has hindered attempts to leverage the datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) give promise to overcome these issues. Modern ML methods and tools can (1) analyze large datasets, (2) assimilate model ensembles that include a multitude of inputs and outputs, (3) process sparse datasets, (4) perform transfer learning between sites with different data quality, (5) extract hidden geothermal signatures from field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. In this work, we implement ML-based geothermal exploration and an enhanced geothermal systems (EGS) design tool to achieve the above goals. Our exploration tool is GeoThermalCloud (GTC) EGS design tool is GeoDT-ML. GTC (github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. It enables the identification of critical measurements needed to identify geothermal resource signatures. GeoDT-ML (github.com/SmartTensors/GeoThermalCloud.jl/tree/master/) adds coupling to GeoDT (https://github.com/GeoDesignTool/GeoDT.git) for stochastic EGS design optimization and performance prediction. GeoDT-ML leverages recent advances in deep learning and high-performance computing. Contributors to this effort include LANL, PNNL, Google, Stanford, and Julia Computing.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Effect of High-Temperature Stress on Plant Physiological Traits and Mycorrhizal Symbiosis in Maize Plants

Increasing high temperature (HT) has a deleterious effect on plant growth. Earlier works reported the protective role of arbuscular mycorrhizal fungi (AMF) under stress conditions, particularly influencing the physiological parameters. However, the protective role of AMF under high-temperature stress examining physiological parameters with characteristic phospholipid fatty acids (PLFA) of soil microbial communities including AMF has not been studied. This work aims to study how high-temperature stress affects photosynthetic and below-ground traits in maize plants with and without AMF. Photosynthetic parameters like quantum yield of photosystem (PS) II, PSI, electron transport, and fractions of open reaction centers decreased in HT exposed plants, but recovered in AMF + HT plants. AMF + HT plants had significantly higher AM-signature 16:1ω5cis neutral lipid fatty acid (NLFA), spore density in soil, and root colonization with lower lipid peroxidation than non-mycorrhizal HT plants. As a result, enriched plants had more active living biomass, which improved photosynthetic efficiency when exposed to heat. This study provides an understanding of how AM-mediated plants can tolerate high temperatures while maintaining the stability of their photosynthetic apparatus. This is the first study to combine above- and below-ground traits, which could lead to a new understanding of plant and rhizosphere stress.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Massive right-handed neutrinos in $\bar{B} \to D^* τ\bar X$ decay

We explore signatures of a massive right-handed neutrino (RHN) in angular distributions of $\bar{B} \to D^* (\to D π) τ(\to πν_τ) \bar X$ decays, where $X$ is an invisible state. We assume the new physics is described by the standard model effective field theory extended with an RHN in the MeV-GeV mass range. We calculate for the first time the full differential distributions in terms of the visible final states, including the decay of the $τ$ lepton. We evaluate the sensitivity of various distributions to the new physics operators.

FOS: Physical sciences↗
NASA NTRS2016

Description of the TCERT Vetting Reports for Data Release 25

This document, the Kepler Instrument Handbook (KIH), is for Kepler and K2 observers, which includes the Kepler Science Team, Guest Observers (GOs), and astronomers doing archival research on Kepler and K2 data in NASAs Astrophysics Data Analysis Program (ADAP). The KIH provides information about the design, performance, and operational constraints of the Kepler flight hardware and software, and an overview of the pixel data sets available. The KIH is meant to be read with these companion documents:1. Kepler Data Processing Handbook (KSCI-19081) or KDPH (Jenkins et al., 2016). The KDPH describes how pixels downlinked from the spacecraft are converted by the Kepler Data Processing Pipeline (henceforth just the pipeline) into the data products delivered to the MAST archive. 2. Kepler Archive Manual (KDMC-10008) or KAM (Thompson et al., 2016). The KAM describes the format and content of the data products, and how to search for them.3. Kepler Data Characteristics Handbook (KSCI-19040) or KDCH (Christiansen et al., 2016). The KDCH describes recurring non-astrophysical features of the Kepler data due to instrument signatures, spacecraft events, or solar activity, and explains how these characteristics are handled by the pipeline.4. Kepler Data Release Notes 25 (KSCI-19065) or DRN 25 (Thompson et al., 2015). DRN 25 describes signatures and events peculiar to individual quarters, and the pipeline software changes between a data release and the one preceding it.Together, these documents supply the information necessary for obtaining and understanding Kepler results, given the real properties of the hardware and the data analysis methods used, and for an independent evaluation of the methods used if so desired.

Instrument↗
DOE OSTI2020

Automated Vehicle Detection in a Nuclear Facility Using Low-Frequency Acoustic Sensors

This article presents an analysis of the method of construction and results for a classifier intended to identify vehicles using low-frequency acoustic data collected by a dis-tributed sensor network. This data is collected as part of a venture intended to explore data analytics and multisensor fusion techniques for the monitoring of activities at a test bed nuclear facility located at Oak Ridge National Laboratory in Oak Ridge, Tennessee. We describe the associated target signature and design a classifier based on a multilayer perceptron, followed by an analysis of its results. We discuss how overall accuracy is not the only consideration in constructing this classifier, and how for this application, it is actually desirable to operate at a lower level of accuracy in exchange for a reduction in the false alarm rate, as well as how this relates to the actual deployment of the classifier in practical use.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2026

Explainable machine learning reveals that local structural motifs encode the thermodynamic state across the CuZr metallic glass-forming range

Metallic glasses derive their properties from the statistics of local atomic motifs rather than from long-range order, yet a quantitative, chemistry-specific link between motif populations and the underlying glassy state has remained elusive. In this work we combine large-scale molecular dynamics, Voronoi tessellation, deep neural networks, and SHapley Additive exPlanations (SHAP) to identify which local structural motifs define the glassy state of Cu—Zr metallic glasses. A dataset of 17,180 atomistic configurations spanning ten compositions (Cu 20 Zr 80 –Cu 80 Zr 20 ) and four quench rates (10 9 –10 12 K/s) is used to train a feed-forward neural network that regresses temperature across the 50–2000 K liquid–supercooled–glass range, achieving a mean absolute error of 19.89 K and R 2 = 0.9974, confirming that the local structural state is faithfully encoded in motif-level structure. SHAP analysis then reveals that a tightly coupled near-icosahedral family of motifs (coordination numbers (CN) 11–13, including the full icosahedron 001200 and its single-atom-perturbation sibling 10930) collectively encodes the thermodynamic state of the system across the full glass-forming range. The CN = 11–13 ordered members carry negative SHAP values at high populations, tracking the most deeply-quenched configurations, while 10930 shows the reversed signature consistent with its role as a soft-spot host whose population shrinks as the icosahedral network deepens. The analysis demonstrates that explainable machine learning can isolate the minimal motif vocabulary defining the glassy state and recovers the near-icosahedral building blocks previously identified by data-driven analyses of Cu—Zr. The approach provides a general, chemistry-specific route for characterizing the structural state of disordered materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Identification of Solid-Electrolyte Interphase Species by Joint Characterization of Li-Ion Battery Chemistry by Mass Spectrometry and Electrochemical Reaction Networks

The formation and stability of the solid-electrolyte interphase (SEI) play central roles in determining the long-term performance and safety of modern electrochemical energy storage systems. Despite decades of research, the SEI’s heterogeneous, dynamic, and multiphase nature has defied comprehensive molecular-level characterization, creating a critical knowledge gap that limits rational battery design. In this work, we introduce a computational−experimental framework that integrates high-throughput quantum chemistry calculations, data-driven electrochemical reaction networks (eCRNs), stochastic algorithms, and laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry (LDI-FTICR-MS) to unravel SEI formation in carbonatebased electrolytes without imposing predefined mechanisms. We constructed the most comprehensive eCRN to date, spanning over 10,000 species and 209 million reactions. Through stochastic network analysis, we successfully recovered 27 species that were previously reported in the literature and predicted 28 novel SEI species nearly doubling our scientific knowledge in this area. Each new species was rigorously confirmed through advanced mass spectral analysis of its distinct molecular and isotopic signatures. We kinetically refined the formation pathways for a select set of both previously reported and novel SEI products, revealing kinetically feasible elementary reaction mechanisms with activation barriers below 1 eV. This computational−experimental approach deepens our molecular-level understanding of SEI chemistry by resolving which species form and through which decomposition mechanisms they emerge. Such knowledge provides the foundation necessary to connect electrolyte composition to the resulting SEI components, a critical step toward a more informed electrolyte development in next-generation lithium-based batteries.

25 ENERGY STORAGE↗
DOE OSTI2024

Radio Frequency Sensor: Very High Frequency Radio Frequency Lightning Detection in Geostationary Orbit

Abstract The Radio Frequency Sensor (RFS), a new radio frequency lightning detector, was launched into geosynchronous orbit in December 2021, and first collected data in January 2022. RFS is a specialized software‐defined radio receiver that detects, records, and reports impulsive broadband radio‐frequency (RF) signatures from lightning in the very high frequency (VHF; 30–300 MHz) range. Its vantage point from a Western hemisphere geosynchronous orbit provides unique opportunities to study evolution of RF lightning signatures over the durations of thunderstorms over the Americas and Pacific Ocean. Its overlapping view with the Geostationary Lightning Mappers (GOES‐16 & 17) enables additional comparisons between the sources of optical emissions and associated VHF emissions that were not possible with previous sensors. We find that RFS preferentially detects bright VHF signals called transionospheric pulse pairs (trans‐ionospheric pulse pairs (TIPPs)). It is estimated that more than 85% of the RFS‐detected lightning events are TIPPs. This paper presents initial results from the first year and a half of on‐orbit operation.

54 ENVIRONMENTAL SCIENCES↗