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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Astronaut Kinematics and Injury Risk for Piloted Lunar Landings and Launches While Standing

During future lunar missions, astronauts may be required to pilot vehicles while standing, and the associated kinematic and injury response is not well understood. In this study we used human body modeling to predict unsuited astronaut kinematics and injury risk for piloted lunar launches and landings in the standing posture. Three pulses (2-5 g; 10–150 ms rise times) were applied in 10 directions (vertical; ± 10-degree offsets) for a total of 30 simulations. Across all simulations, motion envelopes were computed to quantify displacement of the astronaut’s head (max 9.0 cm forward, 7.0 cm backward, 2.1 cm upward, 7.3 cm downward, 2.4 cm lateral) and arms (max 25 cm forward, 35 cm backward, 15 cm upward, 20 cm downward, 20 cm lateral). All head, neck, lumbar, and lower extremity injury metrics were within NASA’s tolerance limits, except tibia compression forces (0–1543 N upper tibia; 0–1482 N lower tibia; tolerance—1350 N) and revised tibia index (0.04–0.58 upper tibia; 0.03–0.48 lower tibia; tolerance—0.43) for the 2.7 g/150 ms pulse. Pulse magnitude and duration contributed over 80% to the injury metric values, whereas loading direction contributed less than 3%. Overall, these simulations suggest piloting a lunar lander vehicle in the standing posture presents a low risk of injury to the astronaut, although risk of tibia injury is potentially outside NASA’s acceptance limits and warrants further investigation.

Mitesh Lalwala↗
DOE OSTI2024

Data‐Driven Predictions of Peak Warming Under Rapid Decarbonization

Abstract The severe impacts associated with recent record‐setting annual global temperatures elevate the need to accurately predict the hottest conditions that could occur even if the most ambitious decarbonization goals are achieved. We use convolutional neural networks (CNNs) to predict peak global warming from recent observed temperature maps and future cumulative CO 2 emissions. For the SSP1‐1.9 decarbonization scenario there is >99% probability that mean global warming exceeds 1.5°C, approximately even odds that it reaches 2°C, and ∼90% probability that the hottest year globally exceeds 2023 by at least 0.5°C. Further, for the SSP2‐4.5 decarbonization scenario, there is >90% probability that the hottest annual global temperature anomaly is twice the 2023 anomaly. That our framework makes highly accurate out‐of‐sample predictions of the hottest historical year provides confidence in the predicted future probabilities, suggesting substantial risks from the extreme local conditions that are likely to result from globally hot years during rapid decarbonization.

Diffenbaugh, Noah S. [Doerr School of Sustainabili↗
DOE OSTI2025

Evaluating probabilistic deep learning methods for uncertainty quantification of temperature downscaling

Deep learning (DL) has emerged as a promising tool for downscaling coarse-resolution climate data to high-resolution outputs, enabling improved regional climate predictions. A critical aspect of DL-based downscaling is the incorporation of uncertainty quantification (UQ), which enhances the interpretability and reliability of predictions—key factors for climate risk assessment and decision-making. This study develops a DL model to downscale 2 m temperature across the contiguous United States using reanalysis datasets. We systematically evaluate three epistemic UQ methods—deep ensembles (DEns), Monte Carlo dropout (MCD), and Flipout—based on their probabilistic accuracy, downscaling performance, sensitivity to geographical features, and computational efficiency. Results indicate that MCD generally outperforms Flipout and DEns in terms of calibration and downscaling accuracy. However, DEns demonstrate lower calibration errors in coastal regions, indicating its higher confidence within these areas. Flipout, in contrast, is more sensitive to elevation gradients and exhibits higher calibration errors in mountainous regions. Hence, the choice of UQ method for this task depends on the specific requirements of the application. For applications that prioritize overall calibration, downscaling accuracy, and computational efficiency, MCD is a strong candidate. These findings highlight the importance of selecting UQ methods based on application-specific requirements, such as geographical context and computational constraints. By addressing the trade-offs between UQ methods, this study provides actionable insights for improving the reliability, scalability, and utility of DL-based downscaling in climate science.

Environmental sciences↗
DOE OSTI2019

Quantifying Holocene Cliff Retreat Rates at Technical Area 54, a Low- and Mixed-Level Radioactive Waste Disposal Site, Los Alamos National Laboratory, Los Alamos, New Mexico [Slides]

Cliff failure is occurring and threatens to expose pits and shafts. Steep slope angles = FoS < 1 = high probability of failure and retreat. EQ magnitude > 5.5 could initiate failure where FoS > 1. Fracture characterization (location, orientation, etc.) is essential for predictive analysis of at-risk cliff failure locations. Additional surface exposure dating would be helpful and further constrain the effect of fracture characteristics. Continued monitoring is essential for change detection and identification of areas with higher cliff retreat rates.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Microchannel-based Membrane-less Extraction of Li from Unconventional Lithium Sources & the Separation of REE

This final report provides an overview of the Project's entire duration, covering July 1, 2021 to December 31, 2023. It primarily focuses on the achievements, technological developments, and unique challenges the team faced while working on separating and extracting Lithium from produced waters. The project's primary aim was to create an integrated, high-throughput, membrane-less, and modular microfluidic platform that could extract Lithium from unconventional sources. We have successfully met all goals and milestones envisioned in the SOPO document. The most critical primary milestones, including the Go-No-Go milestone (refer to the Gantt chart in the Appendices), were successfully accomplished. We demonstrated phase separation (>90%) and extraction (>85%) performance in the MPSE using synthetic, and representative produced water composition feed at 50 ml/min total flow through MPSE 36. We have also performed a parametric study of the MPSE operations, beyond the scope of SOPO, exploring operating conditions of current and broader interest. The extended investigation of operational parameters is concurrent with our efforts to seek further development of the MPSE technology beyond the scope of the Project. Along these lines of development, we have made efforts to be responsive to DOE calls for technological developments of other types of resources (beyond PW) for the recovery of Critical Materials and higher TRL development (beyond TRL 4). During the work on this Project, we developed and implemented three innovative technical approaches that emerged from our efforts to successfully meet the Project milestones. The innovative & original technical approaches developed and implemented in this Project are now the contributions to process engineering that could be clearly credited to the Project. First, Convergent Design Approach is a comprehensive feedforward & feedback loop of four design phases: i) design for functionality, ii) design for manufacturing, iii) design for sustainability, and iv) design for market. Next was Process Intensification. A major aim of this Project was to create an innovative phase separation & extraction microscale-based technology for Li separation – thus the words microchannel-based in the Project title. A microscale-based technology is intrinsically in the center of the Process Intensification domain as defined by its unique principles. Therefore, Process Intensification was implicitly envisioned in the Project’s SOPO. Lastly, Time Scale Analysis is a novel tool for discovering the needs and directions of Process Intensification implementations in any process technology. This Project is fully credited for developing and implementing the three novel technical approaches mentioned above. These are general contributions to process engineering that emerged from this Project. Beyond the original SOPO scope, the OSU-U.Pitt research group utilized a Convergent Design methodology, integrating first-principles mathematical modeling with experimental validation on the Minimum Development Vehicle. By creating these Digital Twins, the team rapidly assessed manufacturing iterations to support TEA analysis. This framework further enabled the development of advanced Surface Modification Techniques, where hydrophobic and oleophobic coating strategies were optimized via Digital Twin tools and validated through rigorous 100-hour longevity testing. TEA Analysis: The closing efforts of this Project were focused on the TEA analysis. TEA analysis had two primary functions: i) enabling critical assessments of design variations withing 10 the Concurrent Design Approach, thus enabling evolution of the MPSE design to reach faster- better-cheaper alternatives; and ii) to create a bridge between the accomplishments of this Project and future projects of higher TRL, beyond TRL 6 level. It is important to note that the TEA model created in the Project stirred the technological solutions for the recovery of critical materials toward a vision of a very profitable modular plant that has unique zero-waste water discharge signature. More importantly, thanks to our experimental performance data and conservative assumptions, the TEA model predicts minimal technological and investment risks. Low cost of a modular unit of a nominal capacity of [1000 tons of Li 2 CO 3 /year] positions the MPSE based technology within the reach of community investors, thus offering a paradigm shift in the development of critical technologies. The project successfully navigated two primary challenges: solvent selection and manufacturing adaptation. Restricted by the SOPO to existing literature for lithium recovery, the team identified a critical need for a "material excellence program" to develop next-generation solvents, eventually concluding with a preliminary investigation into promising Ionic Liquids (ILs). Simultaneously, COVID-19 supply chain disruptions forced a pivot from traditional manufacturing to advanced additive methods at ATAMI-OSU. By transitioning from stainless steel to 3D-printed polymer substrates, the team achieved a transformative three-order-of- magnitude reduction in manufacturing costs and compressed prototyping timelines from several months to just two days. The MPSE technology offers significant energy, environmental, and economic advantages by overcoming the traditional bottlenecks of phase-separation hardware and contactor size. Unlike conventional mixer-settlers or membrane-based systems, MPSE operates without moving parts or fouling-prone membranes, achieving robust performance even with challenging, viscous, or particulate-heavy feeds. Key performance metrics include an energy intensity reduction of 5–50x (3–40 kJ/m 3 ) compared to incumbent technologies and a dramatic reduction of processing time to under 60 seconds, which drastically reduces the physical plant footprint. These technical efficiencies translate into superior economic outcomes; for a 100 t/year Li 2 CO 3 facility, implementing MPSE is projected to nearly halve contactor CAPEX (from $\$$6.08M to $\$$3.01M) and significantly increase the project's Net Present Value (NPV), derisking new investment and enabling distributed critical-mineral processing configurations. The commercialization of MPSE technology is being spearheaded by Vigsur Dynamics Inc., which has adopted a structured, parallel approach to technical and business development since its formation in January 2026. Following extensive customer discovery and engagement with the Oregon State University accelerator, Vigsur Dynamics is working to establish a business model that transitions from pilot demonstrations to modular hardware sales, ultimately aiming for a "build-own-operate" service strategy. Current technical milestones—including 100 hours of continuous operation, superior energy efficiency, and successful 6-unit modular scale-up— provide a foundation for this transition. Backed by ongoing IP licensing and a growing network of industrial and venture advisors, the company is actively de-risking the platform to replace conventional mixer-settler systems in the critical minerals market.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Fusing time-varying mosquito data and continuous mosquito population dynamics models

Climate change is arguably one of the most pressing issues affecting the world today and requires the fusion of disparate data streams to accurately model its impacts. Mosquito populations respond to temperature and precipitation in a nonlinear way, making predicting climate impacts on mosquito-borne diseases an ongoing challenge. Data-driven approaches for accurately modeling mosquito populations are needed for predicting mosquito-borne disease risk under climate change scenarios. Many current models for disease transmission are continuous and autonomous, while mosquito data is discrete and varies both within and between seasons. This study uses an optimization framework to fit a non-autonomous logistic model with periodic net growth rate and carrying capacity parameters for 15 years of daily mosquito time-series data from the Greater Toronto Area of Canada. The resulting parameters accurately capture the inter-annual and intra-seasonal variability of mosquito populations within a single geographic region, and a variance-based sensitivity analysis highlights the influence each parameter has on the peak magnitude and timing of the mosquito season. This method can easily extend to other geographic regions and be integrated into a larger disease transmission model. This method addresses the ongoing challenges of data and model fusion by serving as a link between discrete time-series data and continuous differential equations for mosquito-borne epidemiology models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

The Use of Artificial Intelligence in Head and Neck Cancers: A Multidisciplinary Survey

Artificial intelligence (AI) approaches have been introduced in various disciplines but remain rather unused in head and neck (H&N) cancers. This survey aimed to infer the current applications of and attitudes toward AI in the multidisciplinary care of H&N cancers. From November 2020 to June 2022, a web-based questionnaire examining the relationship between AI usage and professionals’ demographics and attitudes was delivered to different professionals involved in H&N cancers through social media and mailing lists. A total of 139 professionals completed the questionnaire. Only 49.7% of the respondents reported having experience with AI. The most frequent AI users were radiologists (66.2%). Significant predictors of AI use were primary specialty (V = 0.455; p < 0.001), academic qualification and age. AI’s potential was seen in the improvement of diagnostic accuracy (72%), surgical planning (64.7%), treatment selection (57.6%), risk assessment (50.4%) and the prediction of complications (45.3%). Among participants, 42.7% had significant concerns over AI use, with the most frequent being the ‘loss of control’ (27.6%) and ‘diagnostic errors’ (57.0%). This survey reveals limited engagement with AI in multidisciplinary H&N cancer care, highlighting the need for broader implementation and further studies to explore its acceptance and benefits.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1992

Radiation dose to critical body organs for October 1989 proton event

The Geostationary Operational Environmental Satellite (GOES-7) provides high-quality environmental data about the temporal development and energy characteristics of the protons emitted during a solar particle event. The GOES-7 time history of the hourly averaged integral proton flux for various particle kinetic energies are analyzed for the solar proton event occurring October 19-29, 1989. This event is similar to the August 1972 event that has been widely studied to estimate free-space and planetary radiation-protection requirements. By analyzing the time-history data, the dose rates, which can vary over many orders of magnitude in the early phases of the flare, can be estimated as well as the cumulative dose as a function of time. When basic transport results are coupled with detailed body organ thickness distributions calculated with the Computerized Anatomical Man and Computerized Anatomical Female models, the dose rates and cumulative doses to specific organs can be predicted. With these results, the risks of cancer incidence and mortality are estimated for astronauts in free space protected by various water shield thicknesses.

Simonsen, Lisa C.↗
NASA NTRS1998

RH1020 Single Event Clock Upset Summary Report

This report summarizes the testing and analysis of "single event clock upset' in the RH1020. Also included are SEU-rate predictions and design recommendations for risk analysis and reduction. The subject of "upsets" in the RH1020 is best understood by using a model consisting of a global clock buffer and a D-type flip-flop as the basic memory unit. The RH1020 is built on the ACT 1 family architecture. As such, it has one low-skew global clock buffer with a TTL-level input threshold that is accessed via a single dedicated pin. The clock signal is driven to full CMOS levels, buffered, and sent to individual row buffers with one buffer per channel. For low-skew performance, the outputs of all of the RH1020 row buffers are shorted together via metal lines, as is done in the A1020B. All storage in the RH1020 consists of routed flip-flops, constructed with multiplexors and feedback through the routing segments. A simple latch can be constructed from a single (combinatorial or C) module; an edge-triggered flip-flop is constructed using two concatenated latches. There is no storage in the I/O modules. The front end of the clock buffering circuitry, at a common point relative to the row buffer, is a sub-circuit that was determined to be the most susceptible to heavy ions. This is due, in part, to its smaller transistors compared to the rest of the circuitry. This conclusion is also supported by SPICE simulations and an analysis of the heavy ion data, described in this report. The edge triggered D flip-flop has two single-event-upset modes. Mode one, called C-module upset, is caused by a heavy ion striking the C-module's sensitive area on the silicon and produces a soft single bit error at the output of the flip-flop. Mode two, called clock upset, is caused by a heavy ion strike on the clock buffer, generating a runt pulse interpreted as a false clock signal and consequently producing errors at the flip-flop outputs. C-module upset sensitivity in the RH1020 is essentially the same as that of its ACT 1 siblings (A1020, A1020A and A1020B), which were well tested, analyzed, and documented in the literature.

Katz, Richard B.↗
NASA NTRS2001

Earth: Earth Science and Health

A major new NASA initiative on environmental change and health has been established to promote the application of Earth science remote sensing data, information, observations, and technologies to issues of human health. NASA's Earth Sciences suite of Earth observing instruments are now providing improved observations science, data, and advanced technologies about the Earth's land, atmosphere, and oceans. These new space-based resources are being combined with other agency and university resources, data integration and fusion technologies, geographic information systems (GIS), and the spectrum of tools available from the public health community, making it possible to better understand how the environment and climate are linked to specific diseases, to improve outbreak prediction, and to minimize disease risk. This presentation is an overview of NASA's tools, capabilities, and research advances in this initiative.

Maynard, Nancy G.↗
NASA NTRS2008

Silent Aircraft Initiative Concept Risk Assessment

A risk assessment of the Silent Aircraft Initiative's SAX-40 concept design for extremely low noise has been performed. A NASA team developed a list of 27 risk items, and evaluated the level of risk for each item in terms of the likelihood that the risk would occur and the consequences of the occurrence. The following risk items were identified as high risk, meaning that the combination of likelihood and consequence put them into the top one-fourth of the risk matrix: structures and weight prediction; boundary-layer ingestion (BLI) and inlet design; variable-area exhaust and thrust vectoring; displaced-threshold and continuous descent approach (CDA) operational concepts; cost; human factors; and overall noise performance. Several advanced-technology baseline concepts were created to serve as a basis for comparison to the SAX-40 concept. These comparisons indicate that the SAX-40 would have significantly greater research, development, test, and engineering (RDT&E) and production costs than a conventional aircraft with similar technology levels. Therefore, the cost of obtaining the extremely low noise capability that has been estimated for the SAX-40 is significant. The SAX-40 concept design proved successful in focusing attention toward low noise technologies and in raising public awareness of the issue.

Nickol, Craig L.↗
NASA NTRS2009

A Methodology for Determining Statistical Performance Compliance for Airborne Doppler Radar with Forward-Looking Turbulence Detection Capability

The objective of the research developed and presented in this document was to statistically assess turbulence hazard detection performance employing airborne pulse Doppler radar systems. The FAA certification methodology for forward looking airborne turbulence radars will require estimating the probabilities of missed and false hazard indications under operational conditions. Analytical approaches must be used due to the near impossibility of obtaining sufficient statistics experimentally. This report describes an end-to-end analytical technique for estimating these probabilities for Enhanced Turbulence (E-Turb) Radar systems under noise-limited conditions, for a variety of aircraft types, as defined in FAA TSO-C134. This technique provides for one means, but not the only means, by which an applicant can demonstrate compliance to the FAA directed ATDS Working Group performance requirements. Turbulence hazard algorithms were developed that derived predictive estimates of aircraft hazards from basic radar observables. These algorithms were designed to prevent false turbulence indications while accurately predicting areas of elevated turbulence risks to aircraft, passengers, and crew; and were successfully flight tested on a NASA B757-200 and a Delta Air Lines B737-800. Application of this defined methodology for calculating the probability of missed and false hazard indications taking into account the effect of the various algorithms used, is demonstrated for representative transport aircraft and radar performance characteristics.

Bowles, Roland L.↗
NASA NTRS2008

Using Bayesian Networks for Candidate Generation in Consistency-based Diagnosis

Consistency-based diagnosis relies heavily on the assumption that discrepancies between model predictions and sensor observations can be detected accurately. When sources of uncertainty like sensor noise and model abstraction exist robust schemes have to be designed to make a binary decision on whether predictions are consistent with observations. This risks the occurrence of false alarms and missed alarms when an erroneous decision is made. Moreover when multiple sensors (with differing sensing properties) are available the degree of match between predictions and observations can be used to guide the search for fault candidates. In this paper we propose a novel approach to handle this problem using Bayesian networks. In the consistency- based diagnosis formulation, automatically generated Bayesian networks are used to encode a probabilistic measure of fit between predictions and observations. A Bayesian network inference algorithm is used to compute most probable fault candidates.

Narasimhan, Sriram↗
NASA NTRS2011

CPRIT/Johnson Space Center, September, 2011 (Cancer Prevention and Research Institute of Texas)

JSC researchers study carcinogenesis, cancer prevention and treatment along with epidemiological (primarily retrospective and longitudinal) studies, modeling, and interactions with the environment such as radiation, nutritional, and endocrine changes related to space flight along with behaviors such as smoking. Cancer research is a major focus for human space flight due to the exposure to space radiation which consists of particles of varying charges and energies, and secondary neutrons. The JSC laboratories collaborate with investigators from the U.S. as well as our European and Japanese partners. We use accelerator facilities at the Brookhaven National Laboratory, Loma Linda University and Los Alamos National Laboratory that generate high energy charged particles and neutrons to simulate cosmic radiation and solar particle events. The research using cultured cells and animals concentrates on damage and repair from the level of DNA to organ tissues, due to exposure to simulated space radiation exposure, that contribute to the induction of leukemia and solid tumors in most major tissues such as lung, colon, liver and breast. The goal of the research is to develop a mathematical model that can predict cancer morbidity and mortality risks with sufficient accuracy for a given space mission.

Davis, Jeffrey↗
NASA NTRS2013

Analytical Algorithms to Quantify the Uncertainty in Remaining Useful Life Prediction

This paper investigates the use of analytical algorithms to quantify the uncertainty in the remaining useful life (RUL) estimate of components used in aerospace applications. The prediction of RUL is affected by several sources of uncertainty and it is important to systematically quantify their combined effect by computing the uncertainty in the RUL prediction in order to aid risk assessment, risk mitigation, and decisionmaking. While sampling-based algorithms have been conventionally used for quantifying the uncertainty in RUL, analytical algorithms are computationally cheaper and sometimes, are better suited for online decision-making. While exact analytical algorithms are available only for certain special cases (for e.g., linear models with Gaussian variables), effective approximations can be made using the the first-order second moment method (FOSM), the first-order reliability method (FORM), and the inverse first-order reliability method (Inverse FORM). These methods can be used not only to calculate the entire probability distribution of RUL but also to obtain probability bounds on RUL. This paper explains these three methods in detail and illustrates them using the state-space model of a lithium-ion battery.

Sankararaman, Shankar↗
NASA NTRS2012

Assessing the Health and Performance Risks of Reduced Carbon Dioxide Exposures and Resource Utilization

There have been a cluster of anecdotal reports that ISS crews are experiencing adverse health effects from on orbit exposure to CO2 levels well below the current Spacecraft Maximum Allowable Concentration (SMAC), which is 5.3 mmHg for 180 days of exposure. Developing evidence that this standard should be reduced to protect crew health is not a simple process. Dr. John James' team looked at the reports of headaches by the crew during private medical conferences and matched these with CO2 levels around the time of these reports. They then compared these to CO2 levels when there were no reports of headache. Using benchmark dose modeling, they found that the risk of headache could be predicted in concentration ranges from 2 to 5 mmHg. However, the data are incomplete because there were insufficient data when crews were exposed to concentrations below 2 mmHg. James' team also asked whether neuro-cognitive effects could be identified with CO2 exposure levels and found that these could not be associated with CO2 levels. Finally, they addressed the question of resource use to meet various levels of CO2 control if the SMACs were lowered. They estimated that CO2 restrictions approaching 2 mmHg would require substantial increases in power use and up-mass resources. They are refining their data on CO2 and headaches, and are looking at potential interactions of intracranial pressure and CO2 levels in eliciting ocular effects.

James, John T.↗
NASA NTRS2017

Surveys of Returned ISS Hardware for MMMOD Impacts

Since February 2001, the Hypervelocity Impact Technology (HVIT) group at the Johnson Space Center (JSC) in Houston has performed 35 post-flight inspections on space exposed hardware returned from the International Space Station (ISS). Data on 1,188 observations of micrometeoroid and orbital debris (MMOD) damage have been collected from these inspections. Survey documentation typically includes impact feature location and size measurements as well as microscopic photography (25-200x). Sampling of impacts sites for projectile residue was performed for the largest features. Results of energy dispersive X-ray spectroscopic analysis to discern impactor source are included in the database when available. This paper will focus on two inspections, the Pressurized Mating Adapter 2 (PMA-2) cover returned in 2015 after 1.6 years exposure with 26 observed impact features, and two Airlock shield panels returned in 2010 after 8.75 years exposure with 58 MMOD impacts. Feature sizes from the observed data are compared to predictions using the Bumper 3 risk assessment code.

Hyde, J. L.↗
NASA NTRS2017

Meteor Shower Forecasting for Spacecraft Operations

Although sporadic meteoroids generally pose a much greater hazard to spacecraft than shower meteoroids, meteor showers can significantly increase the risk of damage over short time periods. Because showers are brief, it is sometimes possible to mitigate the risk operationally, which requires accurate predictions of shower activity. NASA's Meteoroid Environment Office (MEO) generates an annual meteor shower forecast that describes the variations in the near-Earth meteoroid flux produced by meteor showers, and presents the shower flux both in absolute terms and relative to the sporadic flux. The shower forecast incorporates model predictions of annual variations in shower activity and quotes fluxes to several limiting particle kinetic energies. In this work, we describe our forecasting methods and present recent improvements to the temporal profiles based on flux measurements from the Canadian Meteor Orbit Radar (CMOR).

Moorhead, Althea V.↗