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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Solar System Expansion and Strong Equivalence Principle as Seen by the NASA MESSENGER Mission

The NASA MESSENGER mission explored the innermost planet of the solar system and obtained a rich dataset of range measurements for the determination of Mercury's ephemeris. Here we use these precise data collected over seven years to estimate parameters related to General Relativity and the evolution of the Sun. These results confirm the validity of the Strong Equivalence Principle with a significantly refined uncertainty of the Nordtvedt parameter eta=(-6.6 plus or minus 7.2)x10(exp -5) By assuming a metric theory of gravitation, we retrieved the Post-Newtonian parameter beta = 1 + (-1.6 plus or minus 1.8)x10(exp -5) and the Sun's gravitational oblateness, J(sub 2 solar)=(2.246 plus or minus 0.022)x10(exp -7). Finally, we obtain an estimate of the time variation of the Sun gravitational parameter, G (raised dot)solar mass/G solar mass =(-6.13 plus or minus 1.47)x10(exp -14), which is consistent with the expected solar mass loss due to the solar wind and interior processes. This measurement allows us to constrain |G(raised dot)|/G to be less than 4 x 10(exp -14) yr(exp -1).

Antonio Genova↗
NASA NTRS2020

Comparison of Raindrop Size Distribution between NASA’s S-Band Polarimetric Radar and Two-Dimensional Video Disdrometers

The ground-based-radar-derived raindrop size distribution (DSD) parameters—mass-weighted drop diameter Dmass and normalized intercept parameter NW—are the sole resource for direct validation of the National Aeronautics and Space Administration (NASA) Global Precipitation Measurement (GPM) mission Core Observatory satellite-based retrieved DSD. Both Dmass and NW are obtained from radar-measured reflectivity ZH and differential reflectivity ZDR through empirical relationships. This study uses existing relationships that were determined for the GPM ground validation (GV) program and directly compares the NASA S-band polarimetric radar (NPOL) observables of ZH and ZDR and derivedDmass and NW with those calculated by two-dimensional video disdrometer (2DVD). The joint NPOL and 2DVD datasets were acquired during three GPM GV field campaigns conducted in eastern Iowa, southern Appalachia, and western Washington State. The comparative study quantifies the level of agreement for ZH, ZDR, Dmass, and log(NW) at an optimum distance (15–40 km) from the radar as well as at distances greater than 60 km from radar and over mountainous terrain. Interestingly, roughly 10%–15% of the NPOL ZH–ZDR pairs were well outside the envelope of 2DVD-estimated ZH–ZDR pairs. The exclusion of these pairs improved the comparisons noticeably.

Ali Tokay↗
NASA NTRS2019

Evaluation of CLARA-A2 and ISCCP-H Cloud Cover Climate Data Records over Europe with ECA&D Ground-Based Measurements

Clouds are of high importance for the climate system but they still remain one of its principal uncertainties. Remote sensing techniques applied to satellite observations have assisted tremendously in the creation of long-term and homogeneous data records; however, satellite data sets need to be validated and compared with other data records, especially ground measurements. In the present study, the spatiotemporal distribution and variability of Total Cloud Cover (TCC) from the Satellite Application Facility on Climate Monitoring (CM SAF) Cloud, Albedo And Surface Radiation dataset from AVHRR data—edition 2 (CLARA-A2) and the International Satellite Cloud Climatology Project H-series (ISCCP-H) is analyzed over Europe. The CLARA-A2 data record has been created using measurements of the Advanced Very High Resolution Radiometer (AVHRR) instrument onboard the polar orbiting NOAA and the EUMETSAT MetOp satellites, whereas the ISCCP-H data were produced by a combination of measurements from geostationary meteorological satellites and the AVHRR instrument on the polar orbiting satellites. An intercomparison of the two data records is performed over their common period, 1984 to 2012. In addition, a comparison of the two satellite data records is made against TCC observations at 22 meteorological stations in Europe, from the European Climate Assessment & Dataset (ECA&D). The results indicate generally larger ISCCP-H TCC with respect to the corresponding CLARA-A2 data, in particular in the Mediterranean. Compared to ECA&D data, both satellite datasets reveal a reasonable performance, with overall mean TCC biases of 2.1 and 5.2% for CLARA-A2 and ISCCP-H, respectively. This, along with the higher correlation coefficients between CLARA-A2 and ECA&D TCC, indicates the better performance of CLARA-A2 TCC data.

Tzallas, Vasileios↗
NASA NTRSDate not supplied

Measurements and Computations of the Turbulent Corner Flow on the NASA Juncture-Flow Model with a Symmetric Wing

The NASA Juncture Flow experiment is designed to acquire high-quality flowfield data deep in the corner of a wing-fuselage junction specifically for the purpose of computational fluid dynamics (CFD) validation and turbulence model improvement. This paper presents and discusses the results of a recent experiment with the juncture-flow model in the NASA Langley 14- by 22-Foot Subsonic Tunnel. The main objective of the test was to expand the existing juncture-flow dataset with a symmetric wing case that displays fully attached, incipient separation, and separated flow in the corner of the wing-fuselage junction, depending on the model angle of incidence. Laser Doppler velocimetry (LDV) measurements were made at three model angles of incidence (0 deg: fully attached, 1 deg: incipient separation, and 5 deg: separated flow) and for each one, mean-flow and Reynolds-stress data were obtained on the fuselage and at several streamwise locations along the corner of the wing-fuselage junction. Supporting measurements were made during the test campaign and included model and tunnel wall static pressures, tunnel wall and ceiling boundary-layer rake data, oil-flow visualizations, and laser-based measurements of the as-built model geometry and model position in the test section. Comparisons between the experimental data on the test article and Reynolds-averaged Navier-Stokes CFD results are presented and discussed.

Juncture Flow↗
NASA NTRS2023

Measurements and Computations of the Turbulent Corner Flow on the NASA Juncture-Flow Model with a Symmetric Wing

The NASA Juncture Flow experiment is designed to acquire high-quality flowfield data deep in the corner of a wing-fuselage junction specifically for the purpose of computational fluid dynamics (CFD) validation and turbulence model improvement. This paper will present and discuss the results of a recent experiment with the juncture-flow model in the NASA Langley 14- by 22-Foot Subsonic Tunnel. The main objective of that test was to expand the existing juncture-flow dataset with a symmetric wing case that displays fully attached, incipient separation, and separated flow in the corner of the wing-fuselage junction, depending on the model pitch angle. Laser Doppler velocimetry (LDV) measurements were made at three model pitch angles (0 deg: fully attached, 1 deg: incipient separation, and 5 deg: separated flow) and for each one, mean-flow and Reynolds-stress data was obtained on the fuselage and at several streamwise locations along the corner of the wing-fuselage junction. Supporting measurements were made during the test campaign and included model and tunnel wall static pressures, tunnel wall and ceiling boundary-layer rake data, oil-flow visualizations, and laser-based measurements of the as-built model geometry and model position in the test section. Comparisons between the experimental data and Reynolds averaged Navier-Stokes CFD results will also be presented and discussed.

Juncture Flow↗
NASA NTRSDate not supplied

Measurements and Computations of the Turbulent Corner Flow on the NASA Juncture-Flow Model with a Symmetric Wing

The NASA Juncture Flow experiment is designed to acquire high-quality flowfield data deep in the corner of a wing-fuselage junction specifically for the purpose of computational fluid dynamics (CFD) validation and turbulence model improvement. This paper will present and discuss the results of a recent experiment with the juncture-flow model in the NASA Langley 14- by 22-Foot Subsonic Tunnel. The main objective of that test was to expand the existing juncture-flow dataset with a symmetric wing case that displays fully attached, incipient separation, and separated flow in the corner of the wing-fuselage junction, depending on the model pitch angle. Laser Doppler velocimetry (LDV) measurements were made at three model pitch angles (0 deg: fully attached, 1 deg: incipient separation, and 5 deg: separated flow) and for each one, mean-flow and Reynolds-stress data was obtained on the fuselage and at several streamwise locations along the corner of the wing-fuselage junction. Supporting measurements were made during the test campaign and included model and tunnel wall static pressures, tunnel wall and ceiling boundary-layer rake data, oil-flow visualizations, and laser-based measurements of the as-built model geometry and model position in the test section. Comparisons between the experimental data and Reynolds averaged Navier-Stokes CFD results will also be presented and discussed.

Juncture Flow↗
NASA NTRS2019

VISAGE - A Visualization and Exploration Framework for Environmental Data

Diverse airborne and ground-based environmental observations are important technologies for disaster assessment and response, as well as for the validation of environmental satellite observations and atmospheric models which can improve forecasts. The VISAGE (Visualization for Integrated Satellite, Airborne and Ground-based data Exploration) project is working to provide three-dimensional visualization and basic analytics capabilities for such datasets in an interactive user interface. The use of cloud-native, server less technologies for analysis optimized data storage will position VISAGE for integration with other technologies into a Data Analytic Center Framework.

Conover, Helen↗
NASA NTRS2005

Creation and testing of an artificial neural network based carbonate detector for Mars rovers

We have developed an artificial neural network (ANN) based carbonate detector capable of running on current and future rover hardware. The detector can identify calcite in visible/NIR (350-2500 nm) spectra of both laboratory specimens covered by ferric dust and rocks in Mars analogue field environments. The ANN was trained using the Backpropagation algorithm with sigmoid activation neurons. For the training dataset, we chose nine carbonate and eight non-carbonate representative mineral spectra from the USGS spectral library. Using these spectra as seeds, we generated 10,000 variants with up to 2% Gaussian noise in each reflectance measurement. We cross-validated several ANN architectures, training on 9,900 spectra and testing on the remaining 100. The best performing ANN correctly detected, with perfect accuracy, the presence (or absence) of carbonate in spectral data taken on field samples from the Mojave desert and clean, pure marbles from CT. Sensitivity experiments with JSC Mars-1 simulant dust suggest the carbonate detector would perform well in aeolian Martian environments.

neural networks↗
NASA NTRS2017

Estimating Evaporative Fraction From Readily Obtainable Variables in Mangrove Forests of the Everglades, U.S.A.

A remote-sensing-based model to estimate evaporative fraction (EF) the ratio of latent heat (LE; energy equivalent of evapotranspiration -ET-) to total available energy from easily obtainable remotely-sensed and meteorological parameters is presented. This research specifically addresses the shortcomings of existing ET retrieval methods such as calibration requirements of extensive accurate in situ micro-meteorological and flux tower observations, or of a large set of coarse-resolution or model-derived input datasets. The trapezoid model is capable of generating spatially varying EF maps from standard products such as land surface temperature [T(sub s)] normalized difference vegetation index (NDVI)and daily maximum air temperature [T(sub a)]. The 2009 model results were validated at an eddy-covariance tower (Fluxnet ID: US-Skr) in the Everglades using T(sub s) and NDVI products from Landsat as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results indicate that the model accuracy is within the range of instrument uncertainty, and is dependent on the spatial resolution and selection of end-members (i.e. wet/dry edge). The most accurate results were achieved with the T(sub s) from Landsat relative to the T(sub s) from the MODIS flown on the Terra and Aqua platforms due to the fine spatial resolution of Landsat (30 m). The bias, mean absolute percentage error and root mean square percentage error were as low as 2.9% (3.0%), 9.8% (13.3%), and 12.1% (16.1%) for Landsat-based (MODIS-based) EF estimates, respectively. Overall, this methodology shows promise for bridging the gap between temporally limited ET estimates at Landsat scales and more complex and difficult to constrain global ET remote-sensing models.

trapezoid↗
NASA NTRSDate not supplied

Detecting And Characterizing Archetypes of Unintended Consequences in Engineered Systems

When designing engineered systems, the potential for unintended consequences of design policies or design decisions exists despite best intentions. Conditions that might cause the formation of unintended consequences are often known only in hindsight. However, since these conditions are associated with a single event, it is difficult to uncover the general patterns of conditions leading to unintended consequences. In this research, patterns of conditions associated with unintended consequences are learned from historical data and represented in the form of archetypes. While previous work using systems theoretic modeling has identified high-level archetypes, this work leverages a self-organizing map to learn archetypes of unintended consequences from human-tagged risk factors in a large data set of lessons learned from adverse events at NASA. The sixty-six identified archetypes contain patterns of conditions such as complexity and human-machine interaction associated with the formation of unintended consequences. To validate the archetypes, a sample of the archetypes is represented using system dynamics in order to illustrate that the identified archetypes are specialized versions of known high-level archetypes of unintended consequences. While the research is based upon a specific dataset, the archetypes apply to any engineered system and the pattern of leading indicators open a new path to manage unintended consequences and mitigate the magnitude of potentially adverse outcomes.

Hannah S Walsh↗
NASA NTRS2020

Comparison of Raindrop Size Distribution between NASA's S-Band Polarimetric Radar and Two-Dimensional Video Disdrometers

The ground-based-radar-derived raindrop size distribution (DSD) parameters—mass-weighted drop diameter D mass and normalized intercept parameter NW—are the sole resource for direct validation of the National Aeronautics and Space Administration (NASA) Global Precipitation Measurement (GPM ) mission Core Observatory satellite-based retrieved DSD. Both D mass and NW are obtained from radar-measured reflectivity Z Hand differential reflectivity ZDR through empirical relationships. This study uses existing relationships that were determined for the GPM ground validation (GV) program and directly compares the NASA S-band polarimetric radar (NPOL) observables of Z Hand Z DR and derived D mass and NW with those calculated by two-dimensional video disdrometer (2DVD). The joint NPOL and 2DVD datasets were acquired during three GPM GV field campaigns conducted in eastern Iowa, southern Appalachia, and western Washington State. The comparative study quantifies the level of agreement for ZH, ZDR, D mass, and log (NW) at an optimum distance (15–40 km) from the radar as well as at distances greater than 60 km from radar and over mountainous terrain. Interestingly, roughly 10%–15% of the NPOLZH–Z DR pairs were well outside the envelope of 2DVD-estimatedZH–Z DR pairs. The exclusion of these pairs improved the comparisons noticeably.

Ali Tokay↗
NASA NTRSDate not supplied

Using CALIPSO's New Ocean Derived Column Optical Depths

CALIPSO’s Version 4.51 Level 2 data release introduces an all-new group of science data sets containing estimates of total column two-way transmittances and effective optical depths derived from CALIOP ocean surface backscatter measurements and MERRA-2 reanalysis wind speed data. These estimates use data from the standard CALIOP lidar signal but in a passive sensor-like way, thus creating a unique link to passive instrument measurements. These new retrievals are provided for the entire mission, day and night, at 532nm, and are reported at single shot, 1km, and 5km resolutions for all profiles in which a valid lidar ocean surface return is detected. The addition of a total column optical depth constraint on subsequent retrievals of cloud and aerosol optical properties opens the door for many exciting new ways to leverage the already rich and versatile CALIPSO dataset. Following a brief review of the retrieval technique, this talk will focus on quality assurance assessments, estimated uncertainties, and data usage scenarios. We will conclude with examples highlighting some of the exciting work already being done using this new addition to CALIPSO’s already rich data record.

R Ryan↗
NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields in the last two decades, in part thanks to an increasing culture of open data sharing and reuse. Due to its capability for identifying complex relationships and patterns, AI/ML methodology is particularly well suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are many key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Even with the positive culture of Open Science and data sharing, inexperienced researchers working quickly without proper checks can produce models that perform poorly outside of the immediate training dataset. Lessons learned from biological AI/ML research indicate that Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Andrew Casaletto↗
NASA NTRS2020

Continuity of A Global, Satellite-Based Terrestrial Primary Productivity Dataset in the VIIRS Era Achieved With Model-Data Fusion

The NASA Terra and Aqua satellites have been successfully operating for over two decades and have far exceeded their original 5‐year design life. However, the era of NASA’s Earth Observing System (EOS) may be coming to a close as early as 2023. We conducted a comprehensive calibration and validation of the MODIS MOD17 product [1,2] and the potential for continuity of multi‐decadal ecosystem gross primary productivity (GPP) and annual net primary productivity (NPP), using data from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensors aboard Suomi NPP and NOAA‐20. We combined an 18‐year record of eddy covariance flux tower measurements with hundreds of field measurements of NPP from the Oak Ridge National Laboratories Multi‐Biome collection to benchmark MODIS MOD17 Collection 6.1 (C61) and to develop the first terrestrial productivity estimates from VIIRS. Plant traits from the literature and the global TRY database [3,4] provide strong priors for identifying model parameters in a Bayesian model‐data fusion. As MODIS‐like observations are still needed for global environmental applications, the new VIIRS VNP17 product has the potential to extend these continuous estimates of global, terrestrial primary productivity beyond 2030.

K. Arthur Endsley↗
NASA NTRS2024

Validation and Application of Satellite-Derived Sea Surface Temperature Gradients in the Bering Strait and Bering Sea

The Arctic is one of the most important regions in the world’s oceans for understanding the impacts of a changing climate. Yet, it is also difficult to measure because of extreme weather and ice conditions. In this work, we directly compare four datasets from the Group for High-Resolution Sea Surface Temperature (GHRSST) with a NASA Saildrone deployment along the Alaskan Coast and the Bering Sea and Bering Strait. The four datasets used are the Remote Sensing Systems Microwave Infrared Optimally Interpolated (MWIR) product, the Canadian Meteorological Center (CMC) product, the Daily Optimally Interpolated Product (DOISST), and the Operational Sea Surface Temperature and Ice Analysis (OSTIA) product. Spatial sea surface temperature (SST) gradients were derived for both the Saildrone deployment and GHRSST products, with the GHRSST products collocated with the Saildrone deployment. Overall, statistics indicate that the OSTIA product had a correlation of 0.79 and a root mean square difference of 0.11 °C/km when compared with Saildrone. CMC had the highest correlation of 0.81. Scatter plots indicate that OSTIA had the slope closest to one, thus best reproducing the magnitudes of the Saildrone gradients. Differences increased at latitudes > 65°N where sea ice would have a greater impact. A trend analysis was then performed on the gradient fields. Overall, positive trends in gradients occurred in areas along the coastal regions. A negative trend occurred at approximately 60°N. A major finding of this study is that future work needs to revolve around the impact of changing ice conditions on SST gradients. Another major finding is that a northward shift in the southern ice edge occurred after 2010 with a maxima at approximately 2019. This indicates that the shift of the southern ice edge is not gradual but has dramatically increased over the last decade. Future work needs to revolve around examining the possible causes for this northward shift.

Arctic↗
NASA NTRS2019

Orbital Debris Radar Measurements from the Haystack Ultra-Wideband Satellite Imaging Radar (HUSIR): 2014-2017

For many years, the NASA Orbital Debris Program Office (ODPO) has partnered with the U.S. Department of Defense and the Massachusetts Institute of Technology Lincoln Laboratory (MIT/LL) to collect data on the orbital debris environment using the Haystack radar. These measurements are used to characterize the small debris environment in low Earth orbit (LEO), down to a noise-limited size of approximately 5 mm—depending on altitude. The Haystack radar operated by MIT Lincoln Lab underwent upgrades starting in May 2010, with operations resuming in 2014 as the Haystack Ultra-wideband Satellite Imaging Radar (HUSIR). Hence, the data collected beginning in 2014 represents the first dataset available from this upgraded sensor. HUSIR is the primary source of data used by the ODPO to statistically sample orbital debris in the 5-mm to 10-cm size regime in LEO and is a key source of data to build and validate the NASA Orbital Debris Engineering Model. In this paper, we will present recent results from measurements performed during the US Government fiscal years 2014 – 2017. Using the NASA Size Estimation Model, a method based on laboratory radar measurements of debris, we will compare the size distributions of selected orbital debris populations over this 4-year period and flux measurements of orbital debris greater than 1 cm.

Murray, James↗
NASA NTRS2004

Detailed Modeling and Analysis of the CPFM Dataset

A quantitative understanding of photolysis rate coefficients (or "j-values") is essential to determining the photochemical reaction rates that define ozone loss and other crucial processes in the atmosphere. j-Values can be calculated with radiative transfer models, derived from actinic flux observations, or inferred from trace gas measurements. The principal objective of this study is to cross-validate j-values from the Composition and Photodissociative Flux Measurement (CPFM) instrument during the Photochemistry of Ozone Loss in the Arctic Region In Summer (POLARIS) and SAGE I11 Ozone Loss and Validation Experiment (SOLVE) field campaigns with model calculations and other measurements and to use this detailed analysis to improve our ability to determine j-values. Another objective is to analyze the spectral flux from the CPFM (not just the j-values) and, using a multi-wavelength/multi-species spectral fitting technique, determine atmospheric composition.

Swartz, William H.↗
NASA NTRS2017

A 30+ Year AVHRR Land Surface Reflectance Climate Data Record and Its Application to Wheat Yield Monitoring

The Advanced Very High Resolution Radiometer (AVHRR) sensor provides a unique global remote sensing dataset that ranges from the 1980's to the present. Over the years, several efforts have been made on the calibration of the different instruments to establish a consistent land surface reflectance time-series and to augment the AVHRR data record with data from other sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS). In this paper, we present a summary of all the corrections applied to the AVHRR Surface Reflectance and NDVI Version 4 Product, developed in the framework of the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) program. These corrections result from assessment of the geo-location, improvement of the cloud masking and calibration monitoring. Additionally, we evaluate the performance of the surface reflectance over the AERONET sites by a cross-comparison with MODIS, which is an already validated product, and evaluation of a downstream Leaf Area Index (LAI) product. We demonstrate the utility of this long time-series by estimating the winter wheat yield over the USA. The methods developed by [1] and [2] are applied to both the MODIS and AVHRR data. Comparison of the results from both sensors during the MODIS-era shows the consistency of the dataset with similar errors of 10%. When applying the methods to AVHRR historical data from the 1980's, the results have errors equivalent to those derived from MODIS.

AVHRR↗