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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Mapping the Spatial Footprint of Sea Breeze Winds in the Southeastern United States

Abstract Sea breeze winds are observed at various locations worldwide, but the spatially continuous mapping of sea breeze winds is rare. We have developed a scheme to map the areas of the southeastern United States (SEUS) coast influenced by sea breeze winds using a range of surface re‐analysis data to identify their occurrence. Changes in wind direction and dew point temperature are both used to detect a potential sea breeze signature, which is then confirmed by cumuliform clouds seen in satellite images or coastal fronts shown as cohesive lines in radar reflectivity images. Filters are employed to remove onshore winds not induced by the temperature difference between land and sea. From March to September 2019, this scheme identified 134 days with sea breeze occurrence somewhere in the SEUS, a frequency of 63 percent. The number of sea breezes increased from March to July and then decreased to September. Deep inland propagation of sea breezes during this period left footprints in a band parallel to the coastline up to about 220 km inland, after which the sea breeze winds quickly diminished. Comparisons show that the findings using the scheme are consistent with site observations, theoretical estimates, and idealized and semi‐idealized numerical model simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Making a Superbolt: Reconciling Observations of the Optically Brightest Lightning on Earth From Different Satellites

We previously documented geographic distributions of the optically brightest lightning on Earth—known as “superbolts”—using two space-based instruments: the photodiode detector (PDD) on the Fast On-orbit Recording of Transient Events (FORTE) satellite and the Geostationary Lightning Mapper (GLM) on NOAA's Geostationary Operational Environmental Satellites. In this study, we further examine the superbolts identified by the PDD and GLM to reconcile the differences between their geographic distributions. We find that both the physical extent of the parent flash and the development speed of its leaders are important for making a superbolt. The oceanic PDD superbolts tend to occur early in flashes that rapidly expand laterally into long horizontal “megaflashes.” The top GLM superbolts occur over land at later times in particularly large megaflashes. These land-based flashes grow more slowly until they extend over multiple hundreds of kilometers. The FORTE PDD missed these delayed superbolts due to limitations in its triggering. Coincident Tropical Rainfall Measuring Mission measurements show that the warm season megaflash superbolts detected by Lightning Imaging Sensor/GLM and wintertime oceanic superbolts observed by the PDD occur in otherwise similar thunderstorm environments. Both are marked by: low storm heights (<10 km), widespread precipitation near the surface, small infrared brightness temperature gradients, and low flash rates. We suggest that the vertically compact, stratiform nature of these clouds provides favorable conditions for superbolt production.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery

Understanding the interactions among agricultural processes, soil, and plants is necessary for optimizing crop yield and productivity. This study focuses on developing effective monitoring and analysis methodologies that estimate key soil and plant properties. These methodologies include data acquisition and processing approaches that use unmanned aerial vehicles (UAVs) and surface geophysical techniques. In particular, we applied these approaches to a soybean farm in Arkansas to characterize the soil–plant coupled spatial and temporal heterogeneity, as well as to identify key environmental factors that influence plant growth and yield. UAV-based multitemporal acquisition of high-resolution RGB (red–green–blue) imagery and direct measurements were used to monitor plant height and photosynthetic activity. We present an algorithm that efficiently exploits the high-resolution UAV images to estimate plant spatial abundance and plant vigor throughout the growing season. Such plant characterization is extremely important for the identification of anomalous areas, providing easily interpretable information that can be used to guide near-real-time farming decisions. Additionally, high-resolution multitemporal surface geophysical measurements of apparent soil electrical conductivity were used to estimate the spatial heterogeneity of soil texture. By integrating the multiscale multitype soil and plant datasets, we identified the spatiotemporal co-variance between soil properties and plant development and yield. Our novel approach for early season monitoring of plant spatial abundance identified areas of low productivity controlled by soil clay content, while temporal analysis of geophysical data showed the impact of soil moisture and irrigation practice (controlled by topography) on plant dynamics. Our study demonstrates the effective coupling of UAV data products with geophysical data to extract critical information for farm management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

MOSAIC-CONUS: A Multimodal, Multi-Temporally Paired Dataset for Earth Sciences

Earth embeddings—vector representations of geographic locations indexed in space and time—are emerging as a unifying interface for geospatial AI. However, their quality depends not only on model design, but on how multimodal Earth observation (EO) data are spatially indexed, temporally aligned, and cross-modally associated during pretraining. We introduce MOSAIC-CONUS (Multimodal Observations with Spatially Aligned Imagery, Urban Points of Interest, In-Situ Measurements and Text Captions), a large-scale EO dataset over the contiguous United States, organized around 250,000 stratified point indices that serve as stable spatial keys across seven modalities: active radar, passive optical imagery, lidar-derived elevation, land cover, functional context, hydrometeorological measurements, and textual summaries. Unlike existing EO datasets, MOSAIC-CONUS introduces four contributions not jointly addressed in prior work: 1. an open-source, large-scale multimodal EO corpus structured around point-indexed data designed to support Earth embedding learning; 2. explicit radar-optical pairing tables spanning twelve temporal alignment regimes, formalizing cross-sensor alignment as a controllable variable for analyzing how temporal mismatch across modalities influences learned embeddings quality; 3. a benchmark suite spanning cross-modal retrieval, annual nightlights regression, and basin-held-out streamflow prediction, positioning MOSAIC-CONUS as a benchmark-ready resource for multimodal AI systems; and 4. a language-based embedding layer through co-registered textual summaries, enabling Earth embeddings to function as a queryable interface for agentic AI systems. The dataset and pairing protocols are publicly released.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Low Yield Nuclear Monitoring Physics Experiment 1 – Integrated Data Acquisition System Design and Initial Observations

The report documents the design of the Integrated Data AcQuisition (IDAQ) system and observations recorded during the first in a series of underground chemical explosions conducted on the Nevada National Security Site (NNSS) in southern Nevada. Experiments are funded as part of Low Yield Nuclear Monitoring (LYNM) research and development within the United States National Nuclear Security Administration NA-22 nuclear non-proliferation program. The series is part of the broader Physical Experiment 1 (PE1) being conducted in and around the P-tunnel facility on the NNSS. Each explosive experiment utilizes several tons of comp-B to generate signals recorded by a broad suite of instrumentation. The IDAQ serves as the backbone for all subsurface instrumentation providing precise time synchronization, remote control, data exfiltration and backup, along with recording several sensing modalities throughout the underground complex that includes ground motion, environmental conditions, and electromagnetic signals.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2026

A Data-driven Phytotechnology Framework for Identification and Remediation of Leached-Metals-Contaminated Soil Near Coal Ash Impoundments

This project developed and evaluated advanced remote sensing and machine learning approaches to identify, monitor, and address environmental contamination associated with coal combustion residual (CCR) impoundments and landfills at coal-fired power plants. In Phase I, historical and multi-temporal Sentinel-2 satellite imagery, groundwater monitoring data, and environmental variables were integrated to detect vegetation stress potentially caused by toxic metal leaching from coal ash disposal sites. Multiple vegetation and biophysical indices were analyzed to determine their effectiveness in identifying abnormal vegetation growth patterns linked to contamination. Results from case studies conducted at coal ash–impacted power plant sites in North Carolina and Virginia demonstrated that satellite-based vegetation monitoring can serve as an effective early indicator of environmental stress associated with metals such as arsenic, cadmium, cobalt, lead, lithium, radium, and thallium.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

TRACER Radar b1 Data Processing: Corrections, Calibrations, and Processing Report

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility deployed the first ARM Mobile Facility (AMF1) to Houston, Texas for the Tracking Aerosol Convection Interactions Experiment (TRACER) field campaign. The TRACER campaign was conducted from October 1, 2021 to September 30, 2022, with an intensive operational period (IOP) from June 1 to September 30, 2022. To investigate the life cycles of convective cells in the polluted and humid urban environment of Houston, ARM deployed cloud and precipitation radars, including the C-band Scanning ARM Precipitation Radar (CSAPR2), the X/Ka-band Scanning ARM Cloud Radar (SACR), and the Ka-band ARM Zenith cloud Radar (KAZR), as shown in Figure 1. This report presents an analysis of radar data quality, hardware calibrations, and radar data corrections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Rapid Spaceborne Mapping of Wildfire Retardant Drops for Active Wildfire Management

Aerial application of fire retardant is a critical tool for managing wildland fire spread. Retardant applications are carefully planned to maximize fire line effectiveness, improve firefighter safety, protect high-value resources and assets, and limit environmental impact. However, topography, wind, visibility, and aircraft orientation can lead to differences between planned drop locations and the actual placement of the retardant. Information on the precise placement and areal extent of the dropped retardant can provide wildland fire managers with key information to (1) adaptively manage event resources, (2) assess the effectiveness of retardant slowing or stopping fire spread, (3) document location in relation to ecologically sensitive areas; and perform or validate cost-accounting for drop services. This study uses Sentinel-2 satellite data and commonly used machine learning classifiers to test an automated approach for detecting and mapping retardant application. We show that a multiclass model (retardant, burned, unburned, and cloud artifact classes) outperforms a single-class retardant model and that image differencing (post-application minus pre-application) outperforms single-image models. Compared to the random forest and support vector machine, the gradient boosting model performed the best with an overall accuracy of 0.88 and an F1 Score of 0.76 for fire retardant, though results were comparable for all three models. Our approach maps the full areal extent of the dropped retardant within minutes of image availability, rather than linear representations currently mapped by aerial GPS surveys. The development of this capability allows for the rapid assessment of retardant effectiveness and documentation of placement in relation to sensitive environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Reducing Model Uncertainty of Climate Change Impacts on High Latitude Carbon Assimilation

The Arctic Boreal Region (ABR) has a large impact on global vegetation-atmosphere interactions and is experiencing markedly greater warming than the rest of the planet, a trend that is projected to continue with anticipated future emissions of CO 2 . The ABR is a significant source of uncertainty in estimates of carbon uptake in terrestrial biosphere models (TBMs) such that reducing this uncertainty is critical for more accurately estimating global carbon cycling and understanding the response of the region to global change. Process representation and parameterization associated with gross primary productivity (GPP) drives a large amount of this model uncertainty, particularly within the next 50 years, where the response of existing vegetation to climate change will dominate estimates of GPP for the region. Furthermore, we review our current understanding and model representation of GPP in northern latitudes, focusing on vegetation composition, phenology and physiology, and consider how climate change alters these three components. We highlight challenges in the ABR for predicting GPP, but also focus on the unique opportunities for advancing knowledge and model representation, particularly through the combination of remote sensing and traditional boots-on-the-ground science.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Applications of GIS and remote sensing in public participation and stakeholder engagement for watershed management

The use of Geographic Information Systems (GIS) and remote sensing technologies for the development of water quality management programs and for post-implementation assessments has increased dramatically in the past decade. This increase in adoption has been made more accessible through the interfaces of many popular software tools used in the regulation and assessment of water quality. Customized applications of these tools will increase, as ease of access and affordability of directly monitored and remotely sensed datasets improve over time. Concurrently, there is a need for inclusive participatory engagement with stakeholders to achieve solutions to current watershed management challenges. This paper explores the potential of these GIS and remote sensing datasets, tools, models, and immersive engagement technologies from other domains, for improving public participation and stakeholder engagement throughout the watershed planning process. To do so, an initial review is presented about the use of GIS and remote sensing in watershed management and its role in impairment identification, model development, and planning and implementation. Then, ways in which GIS and remote sensing can be integrated with stakeholder engagement through (1) leveraging GIS and remote sensing datasets, and (2) stakeholder engagement approaches including outreach and education, modeler-led development, and stakeholder-led involvement and feedback, are discussed. Finally, future perspectives on the potential for transforming public participation and stakeholder engagement in the watershed management process through applications of GIS and remote sensing are presented.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Enhanced Laser-Induced Graphene Microfluidic Integrated Sensors (LIGMIS) for On-Site Biomedical and Environmental Monitoring

The convergence of microfluidic and electrochemical biosensor technologies offers significant potential for rapid, in-field diagnostics in biomedical and environmental applications. Traditional systems face challenges in cost, scalability, and operational complexity, especially in remote settings. Addressing these issues, laser-induced graphene microfluidic integrated sensors (LIGMIS) are presented as an innovative platform that integrates microfluidics and electrochemical sensors both comprised of laser-induced graphene. This study advances the LIGMIS concept by resolving issues of uneven fluid transport, increased hydrophobicity during storage, and sensor biofunctionalization challenges. Key innovations include Y-shaped reservoirs for consistent fluid flow, hydrophilic polyethyleneimine coatings to maintain wettability, and separable microfluidic and electrochemical components enabling isolated electrode nanoparticle metallization and biofunctionalization. Multiplexed electrochemical detection of the neonicotinoid imidacloprid and nitrate ions in environmental water samples yields detection limits of 707 nm and 10 -5.4 m with wide sensing ranges of 5–100 µm and 10 -5 –10 -1 m, respectively. Similarly, uric acid and calcium ions are detected in saliva, demonstrating detection limits of 217 nm and 10 -5.3 m with sensing ranges of 10–50 µm, and 10 -5 –10 -2.5 m, respectively. Overall, this biosensing demonstrates the capability of the LIGMIS platform for multiplexed detection in biologically complex solutions, with applications in environmental water quality monitoring and oral cancer screening.

environmental monitoring↗
DOE OSTI2022

The Relation of Environmental Conditions With Charge Structure in Central Argentina Thunderstorms

In this study we explored the environmental conditions hypothesized to induce a dominant charge structure in thunderstorms in the province of Cordoba, Argentina, during the RELAMPAGO-CACTI (Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations-Clouds, Aerosols, Complex Terrain Interactions) field campaigns. Hypothesized environmental conditions are thought to be related to small warm cloud residence time and warm rain growth suppression, which lead to high cloud liquid water contents in the mixed-phase zone, contributing to positive charging of graupel and anomalous charge structure storms. Data from radiosondes, a cloud condensation nuclei (CCN) ground-based instrument and reanalysis were used to characterize the proximity inflow air of storms with anomalous and normal charge structures. Consistent with the initial hypothesis, anomalous storms had small warm cloud depth caused by dry low-level humidity and low 0°C height. Anomalous storms were associated with lower CCN concentrations than normal storms, an opposite result to the initial expectation. High CAPE is not an important condition for the development of anomalous storms in Argentina, as no clear pattern could be found among the different parameters calculated for updraft proxy that would be consistent with the initial hypothesis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Simultaneous Observations of Surface Layer Profiles of Humidity, Temperature, and Wind Using Scanning Lidar Instruments

We demonstrate the combination of three Doppler lidars (DLs), a water vapor differential absorption lidar and a temperature rotational Raman lidar for the investigation of the interactions between the land-surface and the atmospheric boundary layer. This combination of scanning lidars was operated for the first time during the Land-Atmosphere Feedback Experiment at the Atmospheric Radiation Measurement program's Southern Great Plain site, Oklahoma, USA, in August 2017, and provided simultaneous surface layer profiles of horizontal wind, humidity and temperature. The horizontal wind profiles were determined using the dual-Doppler method with two DLs. The scans were performed above four towers providing atmospheric variables and fluxes. These combined lidar data allowed for the estimation of the friction velocity as well as the surface latent and sensible heat fluxes. For this purpose, profiles calculated with Monin-Obukhov similarity theory were fitted with the Levenberg-Marquardt nonlinear least squares curve-fitting method to the measured surface-layer lidar profiles. We present case studies of three 50-min periods to illustrate the new method. The measurements resulted in friction velocities of 0.31, 0.29, and 0.38 ms –1 , sensible surface heat fluxes of 312.1, 234.1, and 183.3 Wm –2 and latent surface heat fluxes of 251.8, 227.3, and 274.4 Wm –2 . These values were compared with the in-situ measurements of the towers. Considering the sampling differences and the error analyses, the results agree within 25.1%, 42.5%, and 28.8%, respectively, demonstrating that this synergy of scanning active remote sensing systems can be used to derive surface fluxes with reasonable accuracy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Multispectral UAV imagery of experimental freshwater wetlands under 5 ppt saltwater intrusion, Louisiana, 2023 and 2024

Multispectral imagery was collected using an unmanned aerial vehicle (UAV) to evaluate how freshwater vegetation responds to short-term simulated saltwater intrusion events. The purpose of this data collection was to understand how plant health changes in response to acute salinity exposure, which is increasingly relevant in coastal wetland ecosystems facing sea level rise and storm surge events, such as in coastal Louisiana. Three experimental saltwater intrusions were conducted at a salinity of approximately 5 parts per thousand (ppt) for durations of 6-days, 10-days, and 17-days. UAV flights occurred both before and after each treatment. The resulting imagery was processed using Pix4DMapper software to georeference the images and generate orthomosaics. The multispectral sensor used in this study captures reflectance in five bands: blue, green, red, red-edge, and near-infrared. The uploaded data consist of georeferenced .tif orthomosaics for each spectral band, which are compatible with GIS software for vegetation analysis. This imagery can be utilized in investigations into vegetation stress, remote sensing of freshwater wetland ecosystems, and modeling of plant response to environmental changes.

EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS↗
DOE OSTI2021

Studying drought-induced forest mortality using high spatiotemporal resolution evapotranspiration data from thermal satellite imaging

Drought can have pervasive and wide-spread impacts to forest health, as evidenced in several severe events occurring over the recent decades. Extensive forest die-off due to drought can impair the ecological functioning of forests, impacting habitat, water yield and quality from forested lands, and altering forest fire dynamics and intensity. Satellite remote sensing provides an effective means for detecting and monitoring spatial patterns of forest mortality over large areas, exploiting free and open long-term image archives available at a range in spatial and temporal resolutions. While remotely sensed surface reflectances and vegetation indices have been widely used to study optical response of forest canopies to drought events, retrievals of evapotranspiration (ET) derived from thermal satellite imagery – particularly at resolutions approaching crown scale - can provide insights into cumulative tree stresses that can incite disease and trigger mortality. In this study, we applied a multi-sensor satellite data fusion approach to estimate daily 30-m resolution ET and an associated Evaporative Stress Index (ESI) to study drought-induced mortality in a temperate forest at the Missouri Ozark AmeriFlux (MOFLUX) site, located in the central United States. The study covered the period from 2010 to 2014, including an exceptional drought year of 2012. Modeled ET agreed well with eddy flux measurements from the MOFLUX tower, with average monthly relative errors of 15%. Plot-scale ESI, describing temporal anomalies in the ratio of actual-to-reference ET, was used as an index of relative forest health to investigate relationships between forest mortality and drought severity. ESI showed good agreement with observed predawn leaf water potential, especially during the drought year. Furthermore, plot-scale ESI was also correlated with the subsequent year's tree mortality, suggesting the importance of considering the forest health condition prior to drought when studying drought-induced forest impacts. This study demonstrates the utility of multi-year ET remote sensing data at the stand or plot scale as an indicator of forest health and as a predictor of future mortality due to drought.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Fate of Internal Waves on a Shallow Shelf

Internal waves strongly influence the physical and chemical environment of coastal ecosystems worldwide. We report novel observations from a distributed temperature sensing (DTS) system that tracked the transformation of internal waves from the shelf break to the surf zone over a narrow shelf slope region in the South China Sea. The spatially continuous view of temperature fields provides a perspective of physical processes commonly available only in laboratory settings or numerical models, including internal wave reflection off a natural slope, shoreward transport of dense fluid within trapped cores, and observations of internal rundown (near-bed, offshore-directed jets of water preceding a breaking internal wave). Analysis shows that the fate of internal waves on this shelf—whether transmitted into shallow waters or reflected back offshore—is mediated by local water column density structure and background currents set by the previous shoaling internal waves, highlighting the importance of wave-wave interactions in nearshore internal wave dynamics.

54 ENVIRONMENTAL SCIENCES↗