SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
This dataset includes the outputs of the JOBS EVSE module for VGI. These impacts include state, census division region, and national metrics including gross jobs, wages, and GDP of the manufacturing, installation, and operations of interconnects per geographic unit.
Abstract Turfgrass is a crop used extensively in athletic fields and golf courses in Maryland. A soil sample collected in July 2023 from an athletic field in Baltimore County, Maryland, part of a turfgrass nematode survey, containedBelonolaimus longicaudatus. In the southeastern United States,B. longicaudatusis an economically important pathogen of warm season turfgrass. The density was four individuals/100 cm 3 of soil, and no visual symptoms were observed in the bermudagrass field. Morphological features and morphometrics of males and females were consistent withB. longicaudatusand placed the Maryland population in a subclade that was geographically represented by populations from north and west Florida, Texas, and South Carolina. Sequencing of the internal transcribed spacer region ITS1 and ITS2 and 28S large ribosomal subunit D2-23 expansion region confirmed the species' identity. Phylogenetic trees and parsimony network analysis placed the Maryland isolate in a large grouping ofB. longicaudatuspopulations including those from Alabama, Delaware, Florida, Indiana, Mississippi, South Carolina, and Texas. To our knowledge, this is the first report ofB. longicaudatusin Maryland.
The author has identified the following significant results. Accomplishments have included: (1) completion of the research design for the USGS/CARETS demonstration project; (2) preparation of photomossics and land use maps at a scale of 1:100,000 for entire area; (3) demonstration of the feasibility of extracting several categories of land use information from ERTS-1 MSS data for a portion of the CARETS region; (4) demonstration of the feasibility of detecting some significant land use changes on ERTS-1 imagery; (5) demonstration of the feasibility of attaching environmental impact significance to the remote sensor-derived land use data; (6) delivery of land use information derived from high altitude aircraft data to the Maryland state planning agency for use in its statewide land use inventory; (7) demonstration of high interest by other use groups in the test region in products and services provided by investigation; and (8) determination of the viability of setting up a computerized geographic information system as part of the CARETS investigation, to facilitate handling of sensor-derived land use data in a variety of formats to suit user requirements.
Four years of observations from the NASA Optical Transient Detector (OTD) and Global Atmospherics National Lightning Detection Network (NLDN) are combined to determine the geographic distribution of the intracloud/cloud to ground lightning ratio over the continental United States. The mean ratio over this region is 2.64-2.94, with a standard deviation of 1.1-1.3 and anomalies as low as 1.0 or less over the Rocky and Appalachian mountains and as high as 8-10 in the central-upper midwest. There is some indication that the ratio covaries with ground elevation, although the relationship is nonunique. Little evidence is found to support a latitudinal covariance, despite significant variation in the climatological mean tropopause pressure over the latitudes considered. The dynamic range of local variability is comparable to the range of values cited by previous studies for latitudinal variation from the deep tropics to midlatitudes. Local high anomalies of this ratio in the midwest are coincident with anomalies in the climatological percentage of positive CG occurrence, as well as in the occurrence of large positive CGs characteristic of MCS convective and trailing stratiform regions. This suggests that storm type, morphology and level of organization may dominate over environmental cofactors in the local determination of this ratio.
An algorithm able to identify and characterize episodes of different aerosol types above sea surfaces of the greater Mediterranean basin (GMB), including the Black Sea and the Atlantic Ocean off the coasts of Iberia and northwest Africa, is presented in this study. Based on this algorithm, five types of intense (strong and extreme) aerosol episodes in the GMB are identified and characterized using daily aerosol optical properties from satellite measurements, namely MODIS-Terra, Earth Probe (EP)-TOMS and OMIAura. These aerosol episodes are: (i) biomass-burning/urban-industrial (BU), (ii) desert dust (DD), (iii) dust/sea-salt (DSS), (iv) mixed (MX) and (v) undetermined (UN). The identification and characterization is made with our algorithm using a variety of aerosol properties, namely aerosol optical depth (AOD), Angstrom exponent (a), fine fraction (FF), effective radius (reff) and Aerosol Index (AI). During the study period (2000e2007), the most frequent aerosol episodes are DD, observed primarily in the western and central Mediterranean Sea, and off the northern African coasts, 7 times/year for strong episodes and 4 times/year for extreme ones, on average. The DD episodes yield 40% of all types of strong aerosol episodes in the study region, while they account for 71.5% of all extreme episodes. The frequency of occurrence of strong episodes exhibits specific geographical patterns, for example the BU are mostly observed along the coasts of southern Europe and off the Atlantic coasts of Portugal, the MX episodes off the Spanish Mediterranean coast and over the Adriatic and northern Aegean Sea, while the DSS ones over the western and central Mediterranean Sea. On the other hand, the extreme episodes for all but DD aerosol display more patchy spatial patterns. The strong episodes exhibit AOD at 550 nm as high as 1.6 in the southernmost parts of central and eastern Mediterranean Sea, which rise up to 5 for the extreme, mainly DD and DSS, episodes. Although more than 90% of all aerosol episodes last 1 day, there are few cases, mainly extreme DD episodes, which last up to 4 days. Independently of their type, the Mediterranean aerosol episodes occur more frequently in spring (strong and extreme episodes) and summer (strong episodes) and most rarely during winter. A significant year by year variability of Mediterranean aerosol episodes has been identified, more in terms of their frequency than intensity. An analysis of 5-day back trajectories for the most extreme episodes provides confidence on the obtained results of the algorithm, based on the revealed origin and track of air masses causing the episodes. The 25 and 6% of all strong and extreme episodes, respectively, are MX, thus highlighting the co-existence of different aerosol types in the greater Mediterranean. The intensity of both MX and DSS episodes exhibits similar patterns to those of DD strong ones, indicating that desert dust is a determinant factor for the intensity of aerosol episodes in the Mediterranean, including DSS and MX episodes.
The Snake River Plain Volcanic Province of Southern Idaho is host to an abundance of pyroclastic obsidian deposits that constitute the principal lithic raw material utilized by Native Americans of the region throughout the Precontact Period. Archaeologists have long looked to obsidian as a source of information on past patterns of raw material selection, landscape use, and mobility through geochemical analysis of obsidian artifacts and lithic raw materials. Formation of the Snake River Plain through cataclysmic “super-eruptions” of the Yellowstone hotspot, however, presents one of the most complex and poorly understood obsidian source-scapes of North America. To clarify the geographic distribution of Southern Idaho obsidian source groups, the INL CRMO has compiled a comprehensive reference library of geologic obsidian from 138 source locales distributed across the southern half of the state. In this report, we define 28 geochemically distinct obsidian source groups that occur at these deposits through X-ray Fluorescence spectrometry and contextualize the geographic distribution of each source group relative to the history of silicic magmatism on the Snake River Plain. This research facilitates ongoing investigations at the INL CRMO into patterns of obsidian procurement, conveyance, and exchange in Southern Idaho throughout the Precontact Period.
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.
Recent advancements in high performance computing platforms and computational workflow for regional-scale simulations are enabling unprecedented modeling of fault-to-structure earthquake processes. Regional simulations resolving ground motions at frequencies relevant to engineered systems are becoming computationally viable and provide a new capability to improve understanding of the geographical distribution and intensity of risk to buildings and critical infrastructure. As computational capabilities advance, it is essential to move beyond illustrative single rupture realizations for scenario earthquake events towards the development of a full suite of rupture realizations that appropriately characterize the range of risk to building systems. The work described in this article investigates the application of a suite of fault rupture realizations with the objective of assessing near-fault, site-specific seismic demand variability for building structures. A representative high-performance regional-scale computational model is utilized to execute ground motion and building response simulations based on 18 kinematic rupture realizations of an M7 strike-slip scenario earthquake. The fault rupture models for the scenario earthquake are created by systematically perturbing the hypocenter location and stochastically generating rupture parameters (slip, rise time, rake angle) to represent a breadth of ground motion intensities resulting from the spatial and temporal variabilities of an earthquake rupture process. The resulting seismic demand variability for three-story (short period) and forty-story (long period) steel moment-resisting frame buildings is characterized in terms of the median and distribution of peak inter-story drift ratio for a range of near-fault sites. The full suite of 18 fault rupture realizations and approximately 280,000 nonlinear dynamic building simulations indicate that the three-story building undergoes higher median seismic demand and significantly greater variability of demand at a given site than the forty-story building, which has important implications for the level of certainty in predicting building performance during an earthquake. The simulations performed provide deeper insight into the relationship between fault rupture parameterization and building response, which is essential information for developing a representative suite of rupture realizations for specific earthquake scenarios.
Data assimilation has been demonstrated very useful in improving both global and regional numerical weather prediction. Alaska has very coarser surface observation sites. On the other hand, it gets much more satellite overpass than lower 48 states. How to utilize satellite data to improve numerical prediction is one of hot topics among weather forecast community in Alaska. The Geographic Information Network of Alaska (GINA) at University of Alaska is conducting study on satellite data assimilation for WRF model. AIRS/CRIS sounder profile data are used to assimilate the initial condition for the customized regional WRF model (GINA-WRF model). Normalized standard deviation, RMSE, and correlation statistic analysis methods are applied to analyze one case of 48 hours forecasts and one month of 24-hour forecasts in order to evaluate the improvement of regional numerical model from Data assimilation. The final goal of the research is to provide improved real-time short-time forecast for Alaska regions.
The electrification of heavy-duty vehicles is a critical pathway toward improved energy efficiency in the freight sector. The current battery electric truck technology poses several challenges to commercial vehicle operations, such as limited driving range, sensitivity to climate conditions, and long recharging times. Estimating the energy consumption of heavy-duty electric trucks is crucial to assessing the feasibility of fleet electrification and its impact on the electric grid. This article focuses on developing a model-based simulation approach to predict and analyze the energy consumption of electric trucks by considering the impact of weather and geographical conditions on vehicle road load and auxiliary components power consumption, as well as the impact these factors have on driving range. Specifically, drayage trucks employed in logistics around maritime ports are used as a case study, with consideration of seasonal climate variations and geographical characteristics at different locations. The article includes results for three major container ports within the United States, providing region-specific insights into the energy requirements and driving range of the electric drayage trucks in these regions, which will inform decision-makers in integrating electric trucks into the existing drayage operations and plan investments for electric grid development.
The spatial distribution of electron precipitation induced by VLF signals from ground-based transmitters is determined by using a test particle computer model of the gyroresonant wave-particle interaction (Inan et al., 1982). The results are presented as contours of energy flux on a map of the region around each transmitter. It is shown that the size of the precipitation zones is a strong function of the geographic location of the transmitter, as well as its radiated power and operating frequency. In general, the precipitation zones are much wider in longitude than in latitude and are oriented along lines of constant geomagnetic latitude. Assuming backscatter and/or wave echoing, precipitation zones around the points that are magnetically conjugate to the sources are also estimated. The results presented can be used to interpret satellite- or ground-based measurements of the precipitation induced by ground-based VLF transmitters.
Several methods of deriving Arctic cloud information, primarily from satellite imagery, have been intercompared. The comparisons help in establishing what cloud information is most readily determined in polar regions from satellite data analysis. The analyses for spring-summer conditions show broad agreement, but subjective errors affecting some geographical areas and cloud types are apparent. The results suggest that visible and thermal infrared data may be insufficient for adequate cloud mapping over some Arctic surfaces.
High-spectral-resolution measurements of the O(1D) emission line, performed with the Fabry-Perot interferometer (Thule, Greenland), were used to observe the diurnal variation of the thermospheric temperature in the geomagnetic polarcap at solar minimum (January 1987). By tracing the trajectory of a parcel backward in time and space from the Thule location, it is shown that the observed diurnal temperature variation is due to the degree of solar heat input that a parcel experiences en route to the polar cap, and the route a parcel takes through the polar cusp. It is also shown that the hydrodynamic variations in the winter high-latitude regions from solar maximum to solar minimum are insufficient to mask the thermodynamical effects associated with the offset of the geographic and geomagnetic poles.
The global climatic effects of dust generated by the impact of a 10 km-diameter bolide was simulated using a one-dimensional (vertical only) globally-averaged climate model by Pollack et al. The goal of the simulation is to examine the regional climate effects, including the possibility of coastal refugia, generated by a global dust cloud in a model having realistic geographic resolution. The climate model assumes the instantaneous appearance of a global stratospheric dust cloud with initial optical depth of 10,000. The time history of optical depth decreases according to the detailed calculations of Pollack et al., reaching an optical depth of unity at day 160, and subsequently decreasing with an e-folding time of 1 year. The simulation is carried out for three years in order to examine the atmospheric effects and recovery over several seasons. The simulation does not include any effects of NOx, CO2, or wildfire smoke injections that may accompany the creation of the dust cloud. The global distribution of surface temperature changes, freezing events, precipitation and soil moisture effects and sea ice increases will be discussed.
An extended version of the Nimbus 7/TOMS ozone data set from the period January 1, 1979 to December 31, 1991 is presented. It is shown that the ozone-trend data indicate that regions of enhanced ozone depletion rates have formed at middle and high latitudes during recent years. The seasonal dependence and geographical extent of the enhanced ozone-depletion rates for the Northern and Southern hemispheres are examined. The variability of the long-term ozone trend determination is discussed via consideration of the differences among 11-, 12-, and 13-yr trend calculations. The effects of the Mount Pinatubo eruption and other volcanic eruptions on the TOMS equatorial zonal average ozone measurements, and its influence on long-term trend determinations are discussed. On the basis of a determination of the aerosol phase function using TOMS data, the effect of stratospheric aerosols on determination of ozone amounts from TOMS are shown to be less than 1 percent.
The titles in this section include: 1) Expansion in Geographic Information Services for PIGWAD; 2) Modernization of the Integrated Software for Imagers and Spectrometers; 3) Science-based Region-of-Interest Image Compression; 4) Topographic Analysis with a Stereo Matching Tool Kit; 5) Central Avra Valley Storage and Recovery Project (CAVSARP) Site, Tucson, Arizona: Floodwater and Soil Moisture Investigations with Extraterrestrial Applications; 6) ASE Floodwater Classifier Development for EO-1 HYPERION Imagery; 7) Autonomous Sciencecraft Experiment (ASE) Operations on EO-1 in 2004; 8) Autonomous Vegetation Cover Scene Classification of EO-1 Hyperion Hyperspectral Data; 9) Long-Term Continental Areal Reduction Produced by Tectonic Processes.
Soil moisture retrievals from the Soil Moisture and Ocean Salinity (SMOS) instrument are assimilated into the Noah land surface model (LSM) within the NASA Land Information System (LIS). Before assimilation, SMOS retrievals are bias-corrected to match the model climatological distribution using a Cumulative Distribution Function (CDF) matching approach. Data assimilation is done via the Ensemble Kalman Filter. The goal is to improve the representation of soil moisture within the LSM, and ultimately to improve numerical weather forecasts through better land surface initialization. We present a case study showing a large area of irrigation in the lower Mississippi River Valley, in an area with extensive rice agriculture. High soil moisture value in this region are observed by SMOS, but not captured in the forcing data. After assimilation, the model fields reflect the observed geographic patterns of soil moisture. Plans for a modeling experiment and operational use of the data are given. This work helps prepare for the assimilation of Soil Moisture Active/Passive (SMAP) retrievals in the near future.
Modern Application of Medical-Based Drone Delivery Drones have been used advantageously by militaries for nearly a century, but their uses in civilian life are still mostly cutting-edge, if not theoretical. After a decade of bold proclamations, Amazon’s “PrimeAir” drone delivery system is still in the stage of “preparing” for deliveries, while the public awaits for start ups like SkyDrop (formerly Flirtey) to follow through on impressive promises. Despite the well-publicized disappointment so far in commercial drone delivery, medical drone delivery has already proven itself practical and cheap in several countries, and it promises to expand in the coming years. Drones are uniquely suited to make valuable and urgent deliveries to remote areas, quickly transporting medical supplies where road transportation is prohibitively slow or not available at all. Drones have been used notably to deliver AEDs for out-of-hospital cardiac arrest, frequently beating first-responders to the scene; to deliver blood when there is none on hand at hospitals; to deliver vaccines to an island nation with little transportation infrastructure; and to respond flexibly to medical emergencies in a war zone. Economics make the delivery of food and other cheap goods by drone unattractive in the near-term, but the value and time-sensitivity of medical deliveries mean that drones are already saving lives in healthcare. “We believe the value of new technology is most valuable where it is clearly needed...that’s why we wanted to focus on drones delivering medicine and not delivering pizzas, ”said one executive of a drone system manufacturer. The immediate prospects for the expansion of medical drone use are many; however, they do not exist without their own drawbacks and challenges. Most obvious is the limited range of current commercially-available drones, most of which are isolated to a perimeter of roughly 18 miles. Technological know-how presents another barrier to integration of medical drones on a larger scale. Reports from the United Nations frequently cite a“skill deficit”—a prohibitively low number of qualified drone operators in low-and moderate-income countries (LMICs). Another perhaps more discreet speed bump in global drone development and usage are the various regulations on drone usage. Drone technology has developed so quickly that many states, out of an excess of caution, have nearly snuffed out the fledgling industry with regulation. There also exist significant concerns over the security of private citizens, the efficacy of medical deliveries, and the costs of drone operation. It is these last three barriers which this study will seek to overcome. Put simply, the prospect for human development in LMICs from drone-based medical delivery is far too great to disregard. As of 2020, 3.4 billion people live in rural communities, containing fewer than 5,000 people/km^2. Often lacking infrastructure, these communities are largely isolated from their more populated, urban counterparts. In drones lies the potential to reshape the geographic and developmental distinctions that divide the global population. This development must, therefore, begin first and foremost with advancement in regional well-being and life expectancy. Life expectancy makes up a key facet of human development. The United Nations relies on it as a key indicator of a state’s health. Lars Kunze of the Dortmund University Department of Economic sex plains this as a matter of physical capital accumulation. The longer people live, the more they save as opposed to spend. The more they save, the more which eventually gets invested in themselves and the community as a whole. In providing medical products via drone, it is the intention of this study to enable communities with the means and incentives for long-run savings and investment for future economic development. Through a close analysis of Vanuatu, Rwanda, Tanzania, and Ukraine—four states where drones are currently used to deliver medical supplies—this study develops a framework that LMICs in general and Mexico and particular can adopt and to use medical drones in difficult-to-reach communities for the sake of long-run human developmental initiatives.