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At least 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Maximum predictive power and the superposition principle

In quantum physics the direct observables are probabilities of events. We ask how observed probabilities must be combined to achieve what we call maximum predictive power. According to this concept the accuracy of a prediction must only depend on the number of runs whose data serve as input for the prediction. We transform each probability to an associated variable whose uncertainty interval depends only on the amount of data and strictly decreases with it. We find that for a probability which is a function of two other probabilities maximum predictive power is achieved when linearly summing their associated variables and transforming back to a probability. This recovers the quantum mechanical superposition principle.

Summhammer, Johann↗
NASA NTRSDate not supplied

FluxSat: Long-term Earth Science Data Record (ESDR) for Terrestrial Gross Primary Production (GPP) based on satellite data calibrated with eddy covariance data

Gross primary production (GPP), the amount of carbon dioxide (CO 2 ) assimilated by plants through photosynthesis, is one of the most variable and uncertain components of the global carbon cycle. Global GPP has been estimated with a number of process-based models, data-driven, and hybrid approaches. Dynamic global vegetation models (DGVMs), driven by observed environmental changes, are used for global carbon budget assessments and long-term (climate) prediction. Benchmarking these and other models globally with data-driven GPP estimates is critical for understanding the land sink and ensuring accurate forecasts of the carbon cycle. In addition, global data-driven GPP estimates are crucial for studies of interannual variability, including trends that are linked to mechanisms with large uncertainties, such as the indirect CO 2 fertilization effect related to greening. In response to a community need for a GPP data set that well captures spatio-temporal variability, we developed FluxSat, a data-driven approach that optimizes the use of satellite reflectance data from the NASA MODerate-resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites, calibrated using ground-based eddy covariance (EC) data. We are enhancing (spatially, higher resolution) and extending FluxSat (in time, with additional sensors) to create a high quality long term GPP Earth System Data Record (ESDR) for use in model benchmarking, carbon cycle modeling, and studies of trends and interannual variability. Our team’s objectives are to: 1. Update and document the current MODIS FluxSat GPP (daily, 0.05o and 0.5o resolutions) products with latest available MODIS and EC data sets; 2. Extend FluxSat GPP record forward in time with the Visible Infrared Imaging Radiometer Suite (VIIRS) on operational weather satellites going forward; 3. Extend FluxSat GPP record backward in time using the Advanced Very High Resolution Radiometer (AVHRR) on weather satellites dating back to 1981; 4. Provide higher spatial resolution MODIS and VIIRS GPP (0.0083o). 5. Thoroughly evaluate all FluxSat products with independent data; and 6. Create a homogenized long-term GPP record spanning 40+ years. We will discuss plans for this long-term data set that is supported through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program.

gross Primary Production↗
NASA NTRS1997

Bayesian Blocks: A New Method to Analyze Photon Counting Data

A Bayesian analysis of photon-counting data leads to a new time-domain algorithm for detecting localized structures (bursts), revealing pulse shapes, and generally characterizing intensity variations. The raw counting data -- time-tag events (TTE), time-to-spill (TTS) data, or binned counts -- is converted to a maximum likelihood segmentation of the observation into time intervals during which the photon arrival rate is perceptibly constant -- i.e. has a fixed intensity without statistically significant variations. The resulting structures, Bayesian Blocks, can be thought of as bins with arbitrary spacing determined by the data. The method itself sets no lower limit to the time scale on which variability can be detected. We have applied the method to RXTE data on Cyg X-1, yielding information on this source's short-time-scale variability.

Scargle, Jeffrey D.↗
NASA NTRS1977

Variability and transport of ozone at the tropopause from the first year of GASP data

The relationships of ozone near the tropopause with potential vorticity temperature, and distance from the tropopause are examined. Data are also used to estimate the vertical and horizontal fluxes of ozone near the tropopause. The present estimates of the total flux of ozone into the troposphere verify the model results. Although the distribution of flux between mean motions and diffusion is different and thus suggests that models with coarse horizontal resolution must continue to parameterize much vertical transport by diffusion coefficients. Monthly estimates of the horizontal transient eddy flux of ozone are generally smaller than seasonal or yearly results based on ozonesonde data. This is perhaps because the present estimates are made over monthly periods to reduce the influence of correlation between the annual variations in ozone and meridional wind. The available data support the hypothesis that transient eddy fluxes of ozone have large longitudinal variations.

Nastrom, G. D.↗
NASA NTRS1987

Interannual variability of Mars' south polar cap

Published observational data on the seasonal recession of the south polar cap on Mars (covering the period 1903-1977) are compiled in tables and graphs and analyzed statistically. The basic data set (photographic observations obtained at Lowell Observatory) of Fischbacher et al. (1960) and James and Lumme (1982) and the reduction procedures described by Baum and Martin (1973) are employed, and Viking data from 1977 are used for comparison; the early onset (relative to the mean) of the 1956 recession is characterized in detail. A list of photographically documented large dust storms is provided, and it is suggested that in years with early spring storms, recession may be slower than in years without such storms.

James, Philip B.↗
NASA NTRS2013

Anomalous Variability in Antarctic Sea Ice Extents During the 1960s With the Use of Nimbus Data

The Nimbus I, II, and III satellites provide a new opportunity for climate studies in the 1960s. The rescue of the visible and infrared imager data resulted in the utilization of the early Nimbus data to determine sea ice extent. A qualitative analysis of the early NASA Nimbus missions has revealed Antarctic sea ice extents that are significant larger and smaller than the historic 1979-2012 passive microwave record. The September 1964 ice mean area is 19.7x10(exp 6) sq. km +/- 0.3x10(exp 6) sq. km. This is more the 250,000 sq. km greater than the 19.44x10(exp 6) sq. km seen in the new 2012 historic maximum. However, in August 1966 the maximum sea ice extent fell to 15.9x10(exp 6) sq. km +/- 0.3x10(exp 6) sq. km. This is more than 1.5x10(exp 6) sq. km below the passive microwave record of 17.5x10(exp 6) sq. km set in September of 1986. This variation between 1964 and 1966 represents a change of maximum sea ice of over 3x10(exp 6) sq. km in just two years. These inter-annual variations while large, are small when compared to the Antarctic seasonal cycle.

Antarctic↗
NASA NTRS2014

Anomalous Variability in Antarctic Sea Ice Extents During the 1960s With the Use of Nimbus Data

The Nimbus I, II, and III satellites provide a new opportunity for climate studies in the 1960s. The rescue of the visible and infrared imager data resulted in the utilization of the early Nimbus data to determine sea ice extent. A qualitative analysis of the early NASA Nimbus missions has revealed Antarctic sea ice extents that are signicant larger and smaller than the historic 1979-2012 passive microwave record. The September 1964 ice mean area is 19.7x10 km +/- 0.3x10 km. This is more the 250,000 km greater than the 19.44x10 km seen in the new 2012 historic maximum. However, in August 1966 the maximum sea ice extent fell to 15.9x10 km +/- 0.3x10 km. This is more than 1.5x10 km below the passive microwave record of 17.5x10 km set in September of 1986. This variation between 1964 and 1966 represents a change of maximum sea ice of over 3x10 km in just two years. These inter-annual variations while large, are small when compared to the Antarctic seasonal cycle.

Sea Ice↗
NASA NTRS1984

Variable stator radial turbine

A radial turbine stage with a variable area nozzle was investigated. A high work capacity turbine design with a known high performance base was modified to accept a fixed vane stagger angle moveable sidewall nozzle. The nozzle area was varied by moving the forward and rearward sidewalls. Diffusing and accelerating rotor inlet ramps were evaluated in combinations with hub and shroud rotor exit rings. Performance of contoured sidewalls and the location of the sidewall split line with respect to the rotor inlet was compared to the baseline. Performance and rotor exit survey data are presented for 31 different geometries. Detail survey data at the nozzle exit are given in contour plot format for five configurations. A data base is provided for a variable geometry concept that is a viable alternative to the more common pivoted vane variable geometry radial turbine.

Rogo, C.↗
NASA NTRS1981

Optical spectral variability of the N galaxies 3C 382 and 3C 390.3

Multichannel spectrophotometry data acquired from 1969 to 1980 are used to study the absolute energy distribution and emission line variability of the N galaxies 3C 382 and 3C 390.3. It is shown that: (1) the continuum spectra can be decomposed into a nuclear nonthermal component of constant spectral shape and a constant elliptical galaxy component; (2) apparent visual magnitudes within a 10 arcsec aperture are derived for underlying galaxies, 3C 382 being 15.27 and 3C 390.3, 16.04; (3) forbidden lines and narrow Balmer lines do not change with the central continuum, but (4) the intensity variations of the broad Balmer lines are correlated nonlinearly with changes in the luminosity of the central continuum source. This correlation can be explained by a time-lag effect if there are two components of broad-line emission, one varying on a time scale of a few months and the other over a few years.

Yee, H. K. C.↗
NASA NTRS2023

A GeoNEX-Based High-Spatiotemporal-Resolution Product of Land Surface Downward Shortwave Radiation and Photosynthetically Active Radiation

Surface downward shortwave radiation (DSR) and photosynthetically active radiation (PAR) play critical roles in the Earth’s surface processes. As the main inputs of various ecological, hydrological, carbon, and solar photovoltaic models, increasing requirements for high-spatiotemporal-resolution DSR and PAR es- timation with high accuracy have been observed in recent years. However, few existing products satisfy all of these requirements. This study employed a well-established physical-based lookup table (LUT) approach to the GeoNEX gridded top-of-atmosphere bidirectional reflectance factor data acquired by the Advanced Hi- mawari Imager (AHI) and Advanced Baseline Imager (ABI) sensors. It produced a data product of DSR and PAR over both AHI and ABI coverage at an hourly temporal step with a 1 km spatial resolution. GeoNEX DSR data were validated over 63 stations, and GeoNEX PAR data were validated over 27 stations. The vali- dation showed that the new GeoNEX DSR and PAR products have accuracy higher than other existing prod- ucts, with root mean square error (RMSE) of hourly GeoNEX DSR achieving 74.3Wm−2 (18.0%), daily DSR estimation achieving 18.0 W m−2 (9.2 %), hourly GeoNEX PAR achieving 34.9 W m−2 (19.6 %), and daily PAR achieving 9.5 W m−2 (10.5 %). The study also demonstrated the application of the high-spatiotemporal- resolution GeoNEX DSR product in investigating the spatial heterogeneity and temporal variability of surface solar radiation. The data product can be freely accessed through the NASA Advanced Supercomputing Division GeoNEX data portal: https://data.nas.nasa.gov/geonex/geonexdata/GOES16/GEONEX-L2/DSR-PAR/ (last ac- cess: 12 March 2023) and https://data.nas.nasa.gov/geonex/geonexdata/HIMAWARI8/GEONEX-L2/DSR-PAR/ (last access: 12 March 2023) (https://doi.org/10.5281/zenodo.7023863; Wang and Li, 2022).

Surface downward shortwave radiation (DSR)↗
NASA NTRS1977

A simple thermal model of the earth's surface for geologic mapping by remote sensing

Thermal inertia of the earth's surface can be used in geologic mapping as a complement to surface reflectance data as provided by Landsat. Thermal inertia cannot be determined directly but must be inferred from radiation temperature measurements (by thermal IR sensors) made at various times in the diurnal cycle, combined with a model of the surface heating processes. A model is developed which differs from those created previously for this purpose, because it includes sensible and latent heating. Tests of this model using field data indicate that it accurately determines the surface heating. When the model is used with field measurements of meteorological variables and is combined with remotely sensed temperature data, a thermal inertia image can be produced.

Kahle, A. B.↗
NASA NTRS1999

Multiwavelength Observations of Mkn 501 During the 1997 High State

During the observation period 1977, the close Blazar Mkn 501 showed extremely strong emission and high variability. We examine multiwavelength aspects of this event using Radio optical, soft and hard X-ray and TeV data. We concentrate on the medium-timescale variability of the broadband spectra averaged over 1-week intervals. We confirm the previously found correlation between soft and hard X-ray emission and the at TeV energies while the source shows only minor variability at radio and optical wavelengths. The non-linear correlation between hard X-ray and TeV fluxes is consistent with a simple analytic estimate based on an SSC model in which Klein-Nishina effects are important for the highest-energy electrons in the jet and flux; variations are caused by variations of the electron density and/or the spectral index of the electron injection spectrum. The time-averaged spectra are fitted with an SSC dominated leptonic jet model using the full Klein-Nishina cross section and following the self-consistent evolution of relativistic particles along the jet accounting for gamma gamma absorption and pair production within the source as well as due to the intergalactic infrared red background radiation. The contribution from external inverse-Compton scattering is tightly constrained by the low maximum EGRET flux and found co be negligible.

Petry, D.↗
NASA NTRS2016

Advanced Noise Control Fan: A 20-Year Retrospective

The ANCF test bed is used for evaluating fan noise reduction concepts, developing noise measurement technologies, and providing a database for Aero-acoustic code development. Rig Capabilities: 4 foot 16 bladed rotor @ 2500 rpm, Auxiliary air delivery system (3 lbm/sec @ 6/12 psi), Variable configuration (rotor pitch angle, stator count/position, duct length), synthetic acoustic noise generation (tone/broadband). Measurement Capabilities: 112 channels dynamic data system, Unique rotating rake mode measuremen, Farfield (variable radius), Duct wall microphones, Stator vane microphones, Two component CTA w/ traversing, ESP for static pressures.

turbo fan↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗