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At least 235 records · Page 13

All sources NASA NTRS DOE OSTI

NASA NTRS2015

Full-Scale Crash Tests and Analyses of Three High-Wing Single

The NASA Emergency Locator Transmitter Survivability and Reliability (ELTSAR) project was initiated in 2014 to assess the crash performance standards for the next generation of ELT systems. Three Cessna 172 aircraft have been acquired to conduct crash testing at NASA Langley Research Center's Landing and Impact Research Facility. Testing is scheduled for the summer of 2015 and will simulate three crash conditions; a flare to stall while emergency landing, and two controlled flight into terrain scenarios. Instrumentation and video coverage, both onboard and external, will also provide valuable data of airframe response. Full-scale finite element analyses will be performed using two separate commercial explicit solvers. Calibration and validation of the models will be based on the airframe response under these varying crash conditions.

Annett, Martin S.↗

DOE OSTI2026

Do we have globally representative data to understand soil processes?

Understanding and modeling soils and soil organic matter (SOM) are central to a variety of human needs, from food production to ecosystem management. Soil data have been collected for over a century, but the global spatial and process representativeness of soil data remains unclear. We assessed the representativeness of currently available soil data that could be used to understand a variety of SOM processes. We used 16 open-source soil databases and data from over 281,000 unique locations globally, categorizing the databases into three main data types necessary to understand SOM processes: soil carbon stocks and fluxes, mechanistic drivers of these stocks and fluxes, and soil carbon gain or loss potential. We found that stock and driver data have extensive global coverage. However, data on soil carbon gain or loss potential, particularly data describing change in soils over time such as time series data, are severely limited in their global coverage. We conclude that while significant strides have been made in measuring soil carbon stocks and fluxes, and their drivers, we are limited in global data related to changes in soils over time. Our recommendations for soil data generators are to ensure precise metadata reporting and prioritizing sampling in underrepresented areas like tropical, arctic, mountainous, wetland and arid regions. We also encourage designing revisit schemes that explicitly support change detection and reporting multi-modal datasets that can aid in model development. Targeted measurement of low coverage soil data types and regions is necessary for a range of applications including current and future biogeochemical predictions, and their management and policy implications.

carbon fluxes↗

NASA NTRS1999

Variability of Upper-Tropospheric Precipitable from Satellite and Model Reanalysis Datasets

Numerous datasets have been used to quantify water vapor and its variability in the upper-troposphere from satellite and model reanalysis data. These investigations have shown some usefulness in monitoring seasonal and inter-annual variations in moisture either globally, with polar orbiting satellite data or global model output analysis, or regionally, with the higher spatial and temporal resolution geostationary measurements. The datasets are not without limitations, however, due to coverage or limited temporal sampling, and may also contain bias in their representation of moisture processes. The research presented in this conference paper inter-compares the NVAP, NCEP/NCAR and DAO reanalysis models, and GOES satellite measurements of upper-tropospheric,precipitable water for the period from 1988-1994. This period captures several dramatic swings in climate events associated with ENSO events. The data are evaluated for temporal and spatial continuity, inter-compared to assess reliability and potential bias, and analyzed in light of expected trends due to changes in precipitation and synoptic-scale weather features. This work is the follow-on to previous research which evaluated total precipitable water over the same period. The relationship between total and upper-level precipitable water in the datasets will be discussed as well.

Jedlovec, Gary J.↗

NASA NTRS2001

A Practical Tutorial on Modified Condition/Decision Coverage

This tutorial provides a practical approach to assessing modified condition/decision coverage (MC/DC) for aviation software products that must comply with regulatory guidance for DO-178B level A software. The tutorial's approach to MC/DC is a 5-step process that allows a certification authority or verification analyst to evaluate MC/DC claims without the aid of a coverage tool. In addition to the MC/DC approach, the tutorial addresses factors to consider in selecting and qualifying a structural coverage analysis tool, tips for reviewing life cycle data related to MC/DC, and pitfalls common to structural coverage analysis.

Hayhurst, Kelly J.↗

NASA NTRS2022

Simulated Lunar Surface Hydration Measurements using Multispectral Lidar at 3 µm

Accurately measuring the variability of spectroscopic signatures of hydration (H2O + OH) on the illuminated lunar surface at 3 µm as a function of latitude, lunar time of day, and composition is crucial to determining the generation and destruction mechanisms of OH species and understanding the global water cycle. A prime complication in analysis of the spectroscopic feature is the accurate removal of thermal emission, which can modify or even eliminate the hydration feature depending on the data processing methods used and assumptions made. An orbital multispectral lidar, with laser illumination at key diagnostic wavelengths, would provide uniform, zero-phase geometry, complete latitude and time of day coverage from a circular polar orbit, and is agnostic to the thermal state of the surface. We have performed measurement simulations of a four-wavelength multispectral lidar using spectral mixtures of hydrated mid-ocean ridge basalt (MORB) glasses and lunar regolith endmembers to assess the lidar performance in measuring hydration signatures on the lunar surface. Our results show a feasible system with wavelengths at 1.5 µm, 2.65 µm, 2.8 µm, and 3.1 µm can measure lunar hydration with a precision of 52 ppm (1σ) or better. These results, combined with the uniform measurement capabilities of multispectral lidar make it a valuable spectroscopic technique for elucidating mechanisms of OH/H2O generation, migration, and destruction.

D. R. Cremons↗

NASA NTRS2016

Spatial Sampling of Weather Data for Regional Crop Yield Simulations

Field-scale crop models are increasingly applied at spatio-temporal scales that range from regions to the globe and from decades up to 100 years. Sufficiently detailed data to capture the prevailing spatio-temporal heterogeneity in weather, soil, and management conditions as needed by crop models are rarely available. Effective sampling may overcome the problem of missing data but has rarely been investigated. In this study the effect of sampling weather data has been evaluated for simulating yields of winter wheat in a region in Germany over a 30-year period (1982-2011) using 12 process-based crop models. A stratified sampling was applied to compare the effect of different sizes of spatially sampled weather data (10, 30, 50, 100, 500, 1000 and full coverage of 34,078 sampling points) on simulated wheat yields. Stratified sampling was further compared with random sampling. Possible interactions between sample size and crop model were evaluated. The results showed differences in simulated yields among crop models but all models reproduced well the pattern of the stratification. Importantly, the regional mean of simulated yields based on full coverage could already be reproduced by a small sample of 10 points. This was also true for reproducing the temporal variability in simulated yields but more sampling points (about 100) were required to accurately reproduce spatial yield variability. The number of sampling points can be smaller when a stratified sampling is applied as compared to a random sampling. However, differences between crop models were observed including some interaction between the effect of sampling on simulated yields and the model used. We concluded that stratified sampling can considerably reduce the number of required simulations. But, differences between crop models must be considered as the choice for a specific model can have larger effects on simulated yields than the sampling strategy. Assessing the impact of sampling soil and crop management data for regional simulations of crop yields is still needed.

upscaling↗

NASA NTRS2020

An AeroCom–AeroSat study: intercomparison of satellite AOD datasets for aerosol model evaluation

To better understand and characterize current uncertainties in the important observational constraint of climate models of aerosol optical depth (AOD), we evaluate and intercompare 14 satellite products, representing nine different retrieval algorithm families using observations from five different sensors on six different platforms. The satellite products (super-observations consisting of 1°×1° daily aggregated retrievals drawn from the years 2006, 2008 and 2010) are evaluated with AErosol RObotic NETwork (AERONET) and Maritime Aerosol Network (MAN) data. Results show that different products exhibit different regionally varying biases (both under- and overestimates) that may reach ±50 %, although a typical bias would be 15 %–25 % (depending on the product). In addition to these biases, the products exhibit random errors that can be 1.6 to 3 times as large. Most products show similar performance, although there are a few exceptions with either larger biases or larger random errors. The intercomparison of satellite products extends this analysis and provides spatial context to it. In particular, we show that aggregated satellite AOD agrees much better than the spatial coverage (often driven by cloud masks) within the 1°×1° grid cells. Up to ∼50 % of the difference between satellite AOD is attributed to cloud contamination. The diversity in AOD products shows clear spatial patterns and varies from 10 % (parts of the ocean) to 100 % (central Asia and Australia). More importantly, we show that the diversity may be used as an indication of AOD uncertainty, at least for the better performing products. This provides modellers with a global map of expected AOD uncertainty in satellite products, allows assessment of products away from AERONET sites, can provide guidance for future AERONET locations and offers suggestions for product improvements. We account for statistical and sampling noise in our analyses. Sampling noise, variations due to the evaluation of different subsets of the data, causes important changes in error metrics. The consequences of this noise term for product evaluation are discussed.

Aerosol↗

NASA NTRS2015

The Rise of GNSS Reflectometry for Earth Remote Sensing

The Global Navigation Satellite System (GNSS) reflectometry, i.e. GNSS-R, is a novel remote-sensing technique first published in that uses GNSS signals reflected from the Earth's surface to infer its surface properties such as sea surface height (SSH), ocean winds, sea-ice coverage, vegetation, wetlands and soil moisture, to name a few. This communication discusses the scientific value of GNSS-R to (a) furthering our understanding of ocean mesoscale circulation toward scales finer than those that existing nadir altimeters can resolve, and (b) mapping vegetated wetlands, an emerging application that might open up new avenues to map and monitor the planet's wetlands for methane emission assessments. Such applications are expected to be demonstrated by the availability of data from GEROS-ISS, an ESA experiment currently in phase A, and CyGNSS [3], a NASA mission currently in development. In particular, the paper details the expected error characteristics and the role of filtering played in the assimilation of these data to reduce the altimetric error (when averaging many measurements).

wetland mapping↗

NASA NTRS2015

Preliminary Findings of Inflight Icing Field Test to Support Icing Remote Sensing Technology Assessment

NASA and the National Center for Atmospheric Research have developed an icing remote sensing technology that has demonstrated skill at detecting and classifying icing hazards in a vertical column above an instrumented ground station. This technology has recently been extended to provide volumetric coverage surrounding an airport. Building on the existing vertical pointing system, the new method for providing volumetric coverage will utilize a vertical pointing cloud radar, a multifrequency microwave radiometer with azimuth and elevation pointing, and a NEXRAD radar. The new terminal area icing remote sensing system processes the data streams from these instruments to derive temperature, liquid water content, and cloud droplet size for each examined point in space. These data are then combined to ultimately provide icing hazard classification along defined approach paths into an airport.

remote sensors↗

DOE OSTI2022

A novel cosmic filament catalogue from SDSS data

Here, in this work, we present a new catalogue of cosmic filaments obtained from the latest Sloan Digital Sky Survey (SDSS) public data. In order to detect filaments, we implement a version of the Subspace-Constrained Mean-Shift algorithm that is boosted by machine learning techniques. This allows us to detect cosmic filaments as one-dimensional maxima in the galaxy density distribution. Our filament catalogue uses the cosmological sample of SDSS, including Data Release 16, and therefore inherits its sky footprint (aside from small border effects) and redshift coverage. In particular, this means that, taking advantage of the quasar sample, our filament reconstruction covers redshifts up to z = 2.2, making it one of the deepest filament reconstructions to our knowledge. We follow a tomographic approach and slice the galaxy data in 269 shells at different redshift. The reconstruction algorithm is applied to 2D spherical maps. The catalogue provides the position and uncertainty of each detection for each redshift slice. The quality of our detections, which we assess with several metrics, show improvement with respect to previous public catalogues obtained with similar methods. We also detect a highly significant correlation between our filament catalogue and galaxy cluster catalogues built from microwave observations of the Planck Satellite and the Atacama Cosmology Telescope.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2002

Vexcel Spells Excellence for Earth and Space

With assistance from Stennis Space Center, Vexcel was able to strengthen the properties of its Apex Ground Station(TM), an affordable, end-to-end system that comes complete with a tracking antenna that permits coverage within an approximate 2,000-kilometer radius of its location, a high speed direct-to-disk data acquisition system that can download information from virtually any satellite, and data processing software for virtually all synthetic aperture radar and optical satellite sensors. Vexcel is using an Apex system linked to the Terra satellite to help scientists and NASA personnel measure land and ocean surface temperatures, detect fires, monitor ocean color and currents, produce global vegetation maps and data, and assess cloud characteristics and aerosol concentrations. In addition, Vexcel is providing NASA with close-range photogrammetry software for the International Space Station. The technology, commercially available as FotoG(TM), was developed with SBIR funding and support from NASA's Jet Propulsion Laboratory. Commercially, FotoG is used for demanding projects taken on by engineering firms, nuclear power plants, oil refineries, and process facilities. A version of Vexcel's close-range photo measurement system was also used to create virtual 3-D backdrops for a high-tech science fiction film.

Source record↗

NASA NTRS2021

Midwest Food Security & Agriculture II: Leveraging NASA Earth Observations to Analyze and Display Crop Phenology Data and Weather Conditions to Support Expansion of Small Grain Crops in the Midwest

Agriculture in the Midwest is dominated by monoculture systems that strip the soil of nutrients, decrease yields, and worsen water quality. Crop diversification and cultivating small grains is economically and ecologically advantageous, but limited in practice due to a lack of data about small-grain crop performance as well as monoculture-favoring insurance coverage, economic incentives, and sociocultural traditions. Practical Farmers of Iowa (PFI) strives to create more sustainable agriculture and supports Midwestern farmers in adding small grains into their crop rotations. The DEVELOP team partnered with the USDA Agricultural Research Service and the USA National Phenology Network to assist PFI in applying NASA Earth observation data to track small-grain crop performance. This project developed a Google Earth Engine user interface that enables PFI farmers to compare satellite-assessed vegetation production and climatic conditions of their farm with other PFI farmers. The team assessed the crop performance through Normalized Difference Vegetation Index (NDVI) values derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI). Additionally, the team created a regression model to analyze the predictive properties of vegetation indices and crop yield, ultimately establishing a relationship with mean NDVI from the early growing season. PFI farmers can utilize these products to make informed decisions about their crop production as well as advocate for more expansive insurance policies to protect small-grain crops.

Sophie Barrowman↗

NASA NTRS2021

Assessment of the Advanced Very High Resolution Radiometer (AVHRR) for Snowfall Retrieval in High Latitudes Using CloudSat and Machine Learning

Precipitation retrieval is a challenging topic, especially in high latitudes (HL), and current precipitation products face ample challenges over these regions. This study investigates the potential of the Advanced Very High Resolution Radiometer (AVHRR) for snowfall retrieval in HL using CloudSat radar information and machine learning (ML). With all the known limitations, AVHRR observations should be considered for HL snowfall retrieval because 1) AVHRR data have been continuously collected for about four decades on multiple platforms with global coverage, and similar observations will likely continue in the future; 2) current passive microwave satellite precipitation products have several issues over snow and ice surfaces; and 3) good coincident observations between AVHRR and CloudSat are available for training ML algorithms. Using ML, snowfall rate was retrieved from AVHRR’s brightness temperature and cloud probability, as well as auxiliary information provided by numerical reanalysis. The results indicate that the ML-based retrieval algorithm is capable of detection and estimation of snowfall with comparable or better statistical scores than those obtained from the Atmospheric Infrared Sounder (AIRS) and two passive microwave sensors contributing to the Global Precipitation Measurement (GPM) mission constellation. The outcomes also suggest that AVHRR-based snowfall retrievals are spatially and temporally reasonable and can be considered as a quantitatively useful input to the merged precipitation products that require frequent sampling or long-term records.

Mohammad Reza↗

DOE OSTI2021

Proton Therapy for Breast Cancer: A Consensus Statement From the Particle Therapy Cooperative Group Breast Cancer Subcommittee

Radiation therapy plays an important role in the multidisciplinary management of breast cancer. Recent years have seen improvements in breast cancer survival and a greater appreciation of potential long-term morbidity associated with the dose and volume of irradiated organs. Proton therapy reduces the dose to nontarget structures while optimizing target coverage. However, there remain additional financial costs associated with proton therapy, despite reductions over time, and studies have yet to demonstrate that protons improve upon the treatment outcomes achieved with photon radiation therapy. There remains considerable heterogeneity in proton patient selection and techniques, and the rapid technological advances in the field have the potential to affect evidence evaluation, given the long latency period for breast cancer radiation therapy recurrence and late effects. In this consensus statement, we assess the data available to the radiation oncology community of proton therapy for breast cancer, provide expert consensus recommendations on indications and technique, and highlight ongoing trials’ cost-effectiveness analyses and key areas for future research.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2025

Integration and Quantitative Comparison of Up-Scaled Molecular Observation Network Data with Existing Soil Databases

MONet provides novel soil molecular data to the research community for understanding biogeochemical processes and complementing other soil datasets. This study assesses MONet's ability to replicate known soil patterns via a comparative analysis of soil respiration (Rs), pH, and clay content against benchmark datasets. Results show moderate agreement for pH and clay content, highlighting MONet's strengths in capturing soil biogeochemical variation in underrepresented regions like urban areas. Rs data are marked by the appropriate trends relative to other datasets, but direct comparison is impractical due to methodological differences in underlying data. Strategic sampling is recommended to improve MONet's coverage and eventual utility in bridging molecular observations with global datasets.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1993

Scintillation fading on a low elevation angle satellite path: Assessing the Austin experiment at 11.2 GHz

The University of Texas at Austin is now in its fifth year of observing an 11.2 GHz INTELSAT satellite beacon with an elevation angle of only 5.8 degrees. Measurements of the rainfall rate, beacon level, the radiometric sky temperature, and of meteorological parameters are ongoing. The experiment is motivated by the fact that significant increases in coverage can be obtained from a given geostationary orbit location if acceptable performance can be achieved at elevation angles below the current standard at Ku-Band of 10 degrees. The topics covered include the following: data collection, data processing, scintillation fading vs. rain fading, diurnal and seasonal variations, power spectrum, and variability of the beacon standard deviation.

Vogel, W. J.↗

NASA NTRS2020

Surface melting over the Greenland ice sheet from enhanced resolution passive microwave brightness temperatures (1979–2019)

Surface melting is a major component of the Greenland ice sheet (GrIS) surface mass balance, affecting sea level rise through direct runoff and the modulation on ice dynamics and hydrological processes, supraglacially, englacially and subglacially. Passive microwave (PMW) brightness temperature observations are of paramount importance in studying the spatial and temporal evolution of surface melting in view of their long temporal coverage (1979–to date) and high temporal resolution (daily). However, a major limitation of PMW datasets has been the relatively coarse spatial resolution, being historically of the order of tens of kilometres. Here, we use a newly released passive microwave dataset (37 GHz, horizontal polarization) made available through the NASA MeASUREs program to study the spatiotemporal evolution of surface melting over the GrIS at an enhanced spatial resolution of 3.125 Km. We assess the outputs of different detection algorithms through data collected by Automatic Weather Stations (AWS) and the outputs of the MAR regional climate model. We found that surface melting is well captured using a dynamic algorithm based on the outputs of MEMLS model, capable to detect sporadic and persistent melting. Our results indicate that, during the reference period 1979–2019 (1988–2019), surface melting over the GrIS increased in terms of both duration, up to ~4.5 (2.9) days per decade, and extension, up to 6.9 % (3.6 %) of the GrIS surface extent per decade, according to the MEMLS algorithm. Furthermore, the melting season has started up to ~4 (2.5) days earlier and ended ~7 (3.9) days later per decade. We also explored the information content of the enhanced resolution dataset with respect to the one at 25 km and MAR outputs through a semi-variogram approach. We found that the enhanced product is more sensitive to local scale processes, hence confirming the potential interest of this new enhanced product for studying surface melting over Greenland at a higher spatial resolution than the historical products and monitor its impact on sea level rise. This offers the opportunity to improve our understanding of the processes driving melting, to validate modelled melt extent at high resolution and potentially to assimilate this data in climate models.

Surface melting↗

DOE OSTI2026

Comprehensive framework for assessing and optimizing existing research networks

Conservation, monitoring, and research networks, or collections of ecological research sites unified under a common mission of data collection or a research mission, are essential infrastructure for understanding large landscapes. However, most networks developed opportunistically over decades rather than through systematic design, creating potential limitations in the ability to address conservation challenges across entire regions. We developed a framework to evaluate how well an existing research network represents the environmental conditions its members study and devised an approach to rank sites of priority for strategic expansion. Our approach measures performance through environmental representativeness, geographic coverage, and adequacy for scientific inference and thus optimizes limited monitoring resources to maximize scientific impact. We demonstrated this approach with the U.S. Department of Agriculture (USDA) Forest Service Experimental Forests and Ranges Network (EFRN), a 79‐site network across the United States that grew opportunistically over a century. At the national scale, the network effectively captured high‐biomass forests important for carbon cycle research; 82% of forest biomass was in well‐represented areas. Some areas in Texas, Florida, the Rocky Mountains, and the West Coast had no relevant EFRN sites, which limits the ability to make regional inferences. A fundamental challenge for the EFRN was that sites improving regional extent coverage sometimes provided minimal national benefits, which can create conflicts between local and global priorities. Adding the highest‐ranked candidate site provided a relevant site for 17% of currently poorly represented 1‐km pixel cells nationally, but regional and national site rankings varied considerably due to nested spatial inference. This framework provides quantitative tools for strategic infrastructure decision‐making, ensures that limited monitoring resources maximize conservation impact, and can be applied broadly to address the widespread challenge of optimizing conservation and monitoring networks worldwide.

additional site↗