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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 OSTI2021

NACHOS, a CubeSat-based high-resolution UV-Visible hyperspectral imager for remote sensing of trace gases: system overview and science objectives

Here, the Nano-satellite Atmospheric Chemistry Hyperspectral Observation System (NACHOS) is a high-throughput (f/2.9), high spectral resolution (~1.3 nm optical resolution, 0.6 nm sampling) Offner-design hyperspectral imager operating in the 300-500 nm spectral region. The 1.5U instrument payload (1U optical system, 0.5U electronics module) is hosted by a 1.5U LANL-designed CubeSat bus to comprise a 3U complete satellite. Spectroscopically similar to NASA’s Ozone Monitoring Instrument (OMI), which provides wide-field global mapping of ozone and other gases at coarse spatial resolution, NACHOS fills the complementary niche of targeted measurements at much higher spatial resolution. With 350 across-track spatial pixels and a 15-degree across-track field of view, NACHOS will provide spectral imaging at roughly 0.4 km per pixel from 500 km altitude. NACHOS incorporates highly streamlined gas-retrieval algorithms for rapid onboard processing, alleviating the need to routinely downlink massive hyperspectral data cubes. We will discuss the instrument design, challenges in achieving mechanical robustness to launch vibration in such a compact instrument, the onboard calibration system, and gas-retrieval data downlink strategy. We will also discuss potential science missions, including monitoring of NO 2 as an easily detected proxy for anthropogenic fossil-fuel greenhouse gases, monitoring lowlevel SO 2 degassing at pre-eruptive volcanoes, H 2 CO from wildfires, and characterization of aerosols. The long-term vision is for a many-satellite constellation that could provide both high spatial resolution and frequent revisits for selected targets of interest. As an initial technology demonstration of this vision, the NACHOS project is currently slated to launch two CubeSats in early 2022.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Cross Validation of the Network of Ground-Based Radar with GPM during the Remote Sensing of Electrification, Lightning, And Mesoscale/Microscale Processes with Adaptive Ground Observations (RELAMPAGO) Field Campaign

The cross-validation of radars in a network is important in making consistent retrievals across the domain and assuring the product quality. During the RELAMPAGO field campaign, two C-band radars, namely the Colorado State University C-band Hydrological Instrument for Volumetric Observations ( CSU-CHIVO) and the C-band Scanning ARM Precipitation Radar (CSAPR-2), were deployed near the Sierras de Cordoba in Argentina, a region known for having some of the most intense severe weather in the world. In addition to these two radars, the operational radar of the Cordoba City, the Radar Meteorologico Argentino 1 (RMA-1), adds another instrument to the RELAMPAGO network. This study presents an intercomparison between the RELAMPAGO C-band radars using the GPM spaceborne radar as a common reference ..A method to bring ground-based radars into better agreement is also proposed. Moreover, the attenuation correction for the C-band radar is studied in the context of intercomparing two radars. The attenuation coefficients are computed for the RELAMPAGO domain using the local disdrometers deployed. during the campaign. After the attenuation correction, CSU-CHIVO, CSAPR-2, and RMA-1 compare well with GPM-DPR with a high correlation and bias less than 1 dB.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Use of Remote Sensing and In-situ Observations to Develop and Evaluate Improved Representations of Convection and Clouds for the Accelerated Climate Model for Energy. Final report

Representation of unresolved cloud processes has been one of the most challenging problems in numerical modeling of the atmosphere. During the last decades, considerable efforts have been spent to better represent the collective effects of those processes in terms of resolved scales (see Arakawa 2004 for a review). Still, the progress on this problem has been unacceptably slow as pointed out by Randall et al. (2003). The large part of the difficulty is inherent in the artificial separation of atmospheric processes into the explicitly simulated resolved scales and the highly parameterized subgrid scales. Arakawa (2004) and Arakawa et al. (2011) suggested two plausible approaches to eliminate the problem of artificial separation of atmospheric processes: “parameterize everything” and “resolve everything”.

58 GEOSCIENCES↗
DOE OSTI2020

Use of Remote Sensing and In-Situ Observations to Develop and Evaluate Improved Representations of Convection and Clouds for the ACME Model

The overachieving goal of the whole CMDV-MCS project is to improve understanding of warm season continental convection and to develop treatments of convection and microphysics capable of representing mesoscale convective systems (MCSs) features in large-scale models. Our tasks for this project contributing to the overachieving goal include: (1) Improve the ice nucleation formulation for MG2 and P3 cloud microphysics schemes; (2) Improve the treatment of subgrid dynamics and thermodynamics driving the ice nucleation in E3SM; and (3) Test the performance of improved ice microphysics in E3SM with observation data. In this project, we have (1) Improved the ice nucleation parameterization for MG2 and P3 in E3SM by implementing two advanced empirical parameterizations with connection to aerosols. The two deterministic heterogeneous ice nucleation parameterizations (i.e., DeMott et al., 2015; Niemand et al., 2012) were merged with the MG2 and P3 cloud microphysics schemes in E3SM. Long-term simulations were conducted to examine the impacts of these new parameterizations on simulated cloud properties; (2) Improved the treatment of subgrid dynamics and thermodynamics driving the ice nucleation in E3SM. We evaluated the double Gaussian PDF of vertical velocity simulated by the Cloud Layers Unified By Binormals (CLUBB) and the sub-column vertical velocity sampled from the Subgrid Importance Latin Hypercube Sampler (SILHS) in E3SM. We introduced the vertical velocity variance induced by topographic gravity waves for ice nucleation and droplet activation; and (3) Tested the performance of improved ice microphysics in E3SM with observation data. We tested the new treatments of ice nucleation in the single column model (SCM) mode for the stratiform mixed-phase clouds observed during 9-10 October 2004 in the DOE ARM Mixed-Phase Arctic Cloud Experiment (M-PACE) and for the convective clouds observed on 20 May 2011 in the Midlatitude Continental Convective Clouds Experiment (MC3E). Modeled ice nucleating particles (INPs) concentrations were compared against observations collected around the globe.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Retrospective Analysis of Backwater Habitat Availability Using Remote Sensing

Low-velocity channel-margin habitats serve as important nursery habitats for the endangered Colorado pikeminnow (Ptychocheilus lucius) in the middle Green River between Jensen and Ouray, Utah. These habitats, known as backwaters, are associated with emergent sandbars, and are shaped and reformed annually by peak flows. Our recent knowledge about backwater characteristics and dynamics that is summarized in the synthesis report (Grippo et al. 2015) was based on detailed annual survey data from a relatively small sample of backwaters that were collected from 2003 to 2014 and reach-wide evaluations of backwater surface area based on manual interpretation of aerial and satellite imagery. Methods that bridge the gap between the detailed surveys from a small number of backwaters and the reach-wide assessment of their surface area would enable an assessment of the availability of backwater habitats that meet the minimum depth requirements for suitable habitat for Colorado pikeminnow. In 2015 Argonne National Laboratory (Argonne) tested three regression models—linear, multiple, and partial least square (PLS) regression models—for estimating backwater depth using National Agriculture Imagery Program (NAIP) imagery collected in July 2006 that covered the Jensen-Ouray reach of the Green River (Hamada and LaGory 2016). The results suggested that a PLS regression model showed high correlation with the reference depth (R 2 = 0.69) and had the most unbiased and consistent estimate of backwater depth. The results also indicated that the PLS model would provide reasonable estimates of the amount of habitat providing a minimum suitable depth of 30 cm for young-of-the-year Colorado pikeminnow, even though absolute depth estimates may be uncertain for backwater areas deeper than approximately 40 cm. The study also provided insights regarding the amount and the selection of calibration and validation (cal-val) data needed for improving the accuracy of depth prediction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Estimating Channel Width for the Middle Green River Using Remote Sensing

Channel geometry can affect the abundance and quality of fish habitats along rivers in the Colorado River basin. However, previous analyses of sediment transport and channel narrowing in the middle Green River have focused on very limited reaches of the river and have used techniques with varying degrees of resolution, leading to potential problems with interpretation and generalization to the overall reach. The goal of this study is to develop comprehensive baseline information for channel width estimation along the Green River by combining a high-resolution digital elevation model (DEM) derived from Light Detection and Ranging (LiDAR) data and Sentinel-2 multispectral satellite imagery. The objectives are to (1) estimate channel width using Sentinel-2 satellite imagery alone, (2) estimate channel width using the high-resolution DEM with ancillary information extracted from the multispectral imagery, and (3) examine the agreement in channel width estimates between the products derived from the two methods to gain insights for cost effective channel-width monitoring.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Identification and Analysis of Backwater Nursery Habitats in the Middle Green River, Utah, during 2014, 2017, and 2018 Using High-Resolution Optical Remotely Sensed Imagery

Low-velocity channel-margin habitats, known as backwaters, serve as important nursery habitats for the endangered Colorado pikeminnow (Ptychocheilus lucius) in the middle Green River between Jensen and Ouray, Utah. The backwater synthesis report for the 1987–2013 period (Grippo et al. 2017) reported a decreased number of backwaters per river mile (RM) and an increased mean backwater size across the middle Green River. Information about backwaters is critical for understanding habitat characteristics that are important for Colorado pikeminnow recruitment. The goal of this study was to determine backwater number and size in the middle Green River using high-resolution imagery for 2018 to add to our understanding of long-term trends of backwater habitat availability across the reach. For comparative analysis of backwater habitats between 2004 and 2018, information for 2004, 2006, 2013, 2014 and 2017 from our previous studies was also utilized (Grippo et al. 2017; Hamada et al. 2017, 2021, and in review). Across the 2004, 2006, 2013, 2014, 2017, and 2018 study years, mean daily flow at the Jensen gage during image collection ranged from 1,220 cfs (2004) to 2,940 cfs (2014), which was approximately 140% greater in 2014 than in 2004. The number of delineated backwaters ranged from 88 (2013) to 245 (2017), and total backwater area ranged from 118,938 m 2 (2006) to 209,611 m 2 (2014). Mean backwater size ranged from 822 m 2 (2017) to 1,508 m 2 (2018), but there was considerable variability in backwater size for each of the study years. Backwater areas differed significantly among the study years based on a Kruskal-Wallis Rank Sum test ( P < 0.001). Pairwise Wilcoxon tests indicated that the median backwater areas in 2017 and 2014 were significantly lower than in the other sample years ( P ≤ 0.05), even though these years had the highest total backwater area. 2018 had the greatest median backwater area, although the distribution of backwater area values was not statistically different from backwater areas measured in 2013 ( P > 0.05). These two years also had the lowest maximum backwater area values among all sample years. The distribution of backwater area values was not significantly different ( P > 0.05) among the 2006, 2004, and 2013 sample years.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Multiscale Wildfire Simulation Framework and Remote Sensing

Wildfire as one type of climate extreme events causes huge socioeconomic losses and damages. Large wildfires (i.e., generated pyrocumulonimbus (PyroCb)) can inject tremendous amounts of smoke into the stratosphere, where black carbon and organic carbon aerosols can persist months to years and influence climate by imposing a significant reduction in the radiative forcing like that associated with large volcanic eruptions or proposed via climate interventions such as geoengineering. Both observations and numerical modeling results clearly indicate an increasing trend in wildfire frequency and intensity in many regions during the recent decades with climate change. However, current understanding of wildfire remains largely uncertain owing to limitations of modeling capabilities in representing the multiscale wildfire physics and dynamics and a scarcity of observations constraining important wildfire and environmental variables. This study primarily aims to improve the wildfire simulation capabilities in the state-of-the-art climate model by filling in two major gaps: (1) model resolution is typically too coarse to resolve fine scale processes associated with fires, and (2) chemistry and aerosol processes in fire smoke are poorly represented.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Mercury Detection Utilizing an Aquatic Animal as a Remote-Sensing Platform

Mercury species in open water, especially the accumulated methylmercury ion, pose a threat to fish and environment. Therefore, it is important to develop a small sensor package that can be integrated into a biotelemetry sensor carried by an aquatic animal, enhancing the ability to detect mercury contamination in large water areas. A quartz crystal microbalance (QCM) sensor using metal-organic framework (MOF) as sensing material was developed to detect mercury and methylmercury ions in real time based on acoustic wave perturbation. Thiol groups were introduced into the MOF UiO-66 through the organic linker to prepare the UiO-66-SH which was confirmed by infrared spectroscopy results. Batch adsorption experiments were conducted for the Hg 2+ , CH 3 Hg + , and Ca 2+ ions adsorption in the UiO-66-SH. The adsorption capacities of the mercury ions were more than an order of magnitude higher than those of the competing Ca 2+ ions at the same concentration. The frequency changes of the QCM sensor with the UiO-66-SH sensing film were an order of magnitude higher than those of the controlled baseline QCM sensor without a sensing film. Additionally, the frequency change can be tailored by adjusting the thickness of the MOF film and the adsorption properties of the sensing material. The sensor frequency change correlates well with ion adsorption capacities.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Autonomous Intelligence Measurements and Sensor Systems (AIMS): Gaussian Processes in Remote Sensing: Literature Review

Power grid resilience and reliability is crucial to supporting critical infrastructure. Service interruptions can have devastating consequences to communication, emergency, and transportation services, just to name a few. In addition to infrastructure, power services are crucial to everyday life. Nearly every element of modern-day living relies on a stable and functioning power grid - heating and cooling systems to lighting, refrigeration, telecommunications, and internet access. Interruptions in the power supply can range from minor inconveniences, such as the temporary loss of internet or television services, to more significant problems, like the inability to access medical equipment or emergency services during critical situations. As climate conditions worsen, the reliability of the power grid becomes even more significant. Extreme weather events, such as hurricanes, wildfires, or severe storms, can cause serious damage to power infrastructure, leading to prolonged power outages. Rising temperatures and changing weather patterns can strain the grid, resulting in increased demand for electricity, stressing transmission and distribution systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

High-Resolution Spatio-Temporal Estimation of Net Ecosystem Exchange in Ice-Wedge Polygon Tundra Using In Situ Sensors and Remote Sensing Data

Land-atmosphere carbon exchange is known to be extremely heterogeneous in arctic ice-wedge polygonal tundra regions. In this study, a Kalman filter-based method was developed to estimate the spatio-temporal dynamics of daytime average net ecosystem exchange (NEEday) at 0.5-m resolution over a 550 m by 700 m study site. We integrated multi-scale, multi-type datasets, including normalized difference vegetation indices (NDVIs) obtained from a novel automated mobile sensor system (or tram system) and a greenness index map obtained from airborne imagery. We took advantage of the significant correlations between NDVI and NEEday identified based on flux chamber measurements. The weighted average of the estimated NEEday within the flux-tower footprint agreed with the flux tower data in term of its seasonal dynamics. We then evaluated the spatial variability of the growing season average NEEday, as a function of polygon geomorphic classes; i.e., the combination of polygon types—which are known to present different degradation stages associated with permafrost thaw—and microtopographic features (i.e., troughs, centers and rims). Our study suggests the importance of considering microtopographic features and their spatial coverage in computing spatially aggregated carbon exchange.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Identifying Urban Pluvial Frequency Flooding Hotspots Using the Topographic Control Index and Remote Sensing Radar Images for Early Warning Systems

Identifying areas that frequently experience post-rainfall ponding is essential for effective flood mitigation and planning. This study integrates Sentinel-1 radar imagery and the Topographic Control Index (TCI) to identify 378 flood-prone urban depressions in Beaumont, Texas. Out of 159 major rainfall events, only six had Sentinel-1 radar imagery acquired within six hours of peak rainfall, and these were used to generate the flood frequency map; the ground-based flood sensor data were used to verify that these selected events corresponded to actual peak rainfall and to validate radar-detected water pixels. Validation results showed 100% precision, 70.87% recall, an F1-score of 82.95%, and 71.32% overall accuracy. Approximately 84% of medium-to-high TCI depressions overlapped with Beaumont’s two-year inundation map, confirming a strong relationship between TCI and observed flooding. A total of 124 depressions retained significant water, and after excluding 25 engineered detention ponds, 99 natural depressions remained flood vulnerable. Among these, 74 depressions with medium or high TCI were identified as the highest-priority nuisance flooding hotspots. The results demonstrate that combining TCI with radar imagery provides a reliable and cost-effective approach for identifying areas prone to frequent urban ponding. This framework supports practical decision-making for drainage improvements, hotspot identification, and early-warning system development in urban flood-prone regions.

Sentinel-1 radar imagery↗