Search NASA⌕ Search

SEARCH · Search NASA

Results for “Multivariate statistics”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Toward The Development of Hailstorm Climatologies Derived From Reanalyses and Infared/Passive Microwave Satellite Imagers

Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) and Meteosat series, provide both historical and near-real-time observations of cloud top patterns that are commonly associated with severe convection. Environmental conditions favorable for severe weather are thought to be represented well by reanalyses. Predicting exactly where convection and costly storm hazards like hail will occur using models or satellite imagery alone, however, is extremely challenging. The multivariate combination of satellite-observed cloud patterns with reanalysis environmental parameters, linked to United States Next Generation Weather Radar- (NEXRAD-) estimated Maximum Expected Size of Hail (MESH) using a deep neural network (DNN), enables estimation of potentially severe hail likelihood for any observed storm cell. These estimates are specifically designed to make hail likelihood distinctions based on satellite-indicated points of deep convection within environments favorable for storm development. We seek an approach that can be used to estimate climatological hailstorm frequency and risk throughout the historical satellite data record. This presentation demonstrates that statistical distributions of convective parameters from satellite and reanalysis show separation between non-severe/severe hailstorm classes for predictors including overshooting cloud top temperature and area characteristics, convective available potential energy, vertical wind shear, 500 hPa temperature, mid-level lapse rate, precipitable water, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN to produce a hail likelihood metric with a critical success index of 0.504 and Heidke skill score of 0.403, which is exceptional among recent analogous hail studies. Furthermore, applications of the DNN to select case studies demonstrate good qualitative agreement between hail likelihood and MESH. These hail classifications are aggregated across an 11-year GOES-12/13 image database to derive a hail frequency and severity climatology, which denotes the Central Plains, the Midwest, and northwestern Mexico as being the most hail-prone regions within the domain studied. Opportunities for training and applying DNN-based hailstorm predictions to recently developed GOES-8/10/12/13/16 and Meteosat Second Generation convective storm detection and characterization climatologies over South America and South Africa, respectively, will also be presented.

Kristopher Bedka↗
NASA NTRS2014

Using Concurrent Cardiovascular Information to Augment Survival Time Data from Orthostatic Tilt Tests

Orthostatic Intolerance (OI) is the propensity to develop symptoms of fainting during upright standing. OI is associated with changes in heart rate, blood pressure and other measures of cardiac function. Problem: NASA astronauts have shown increased susceptibility to OI on return from space missions. Current methods for counteracting OI in astronauts include fluid loading and the use of compression garments. Multivariate trajectory spread is greater as OI increases. Pairwise comparisons at the same time within subjects allows incorporation of pass/fail outcomes. Path length, convex hull area, and covariance matrix determinant do well as statistics to summarize this spread Missing data problems Time series analysis need many more time points per OTT session treatment of trend? how incorporate survival information?

Feiveson, Alan H.↗
NASA NTRS1977

Analysis of vector wind change with respect to time for Cape Kennedy, Florida: Wind aloft profile change vs. time, phase 1

Wind vector change with respect to time at Cape Kennedy, Florida, is examined according to the theory of multivariate normality. The joint distribution of the four variables represented by the components of the wind vector at an initial time and after a specified elapsed time is hypothesized to be quadravariate normal; the fourteen statistics of this distribution, calculated from fifteen years of twice daily Rawinsonde data are presented by monthly reference periods for each month from 0 to 27 km. The hypotheses that the wind component changes with respect to time is univariate normal, the joint distribution of wind component changes is bivariate normal, and the modulus of vector wind change is Rayleigh, has been tested by comparison with observed distributions. Statistics of the conditional bivariate normal distributions of vector wind at a future time given the vector wind at an initial time are derived. Wind changes over time periods from one to five hours, calculated from Jimsphere data, are presented.

Adelfang, S. I.↗
NASA NTRS2008

Generation of Boundary Manikin Anthropometry

The purpose of this study was to develop 3D digital boundary manikins that are representative of the anthropometry of a unique population. These digital manikins can be used by designers to verify and validate that the components of the spacesuit design satisfy the requirements specified in the Human Systems Integration Requirements (HSIR) document. Currently, the HSIR requires the suit to accommodate the 1st percentile American female to the 99th percentile American male. The manikin anthropometry was derived using two methods: Principal Component Analysis (PCA) and Whole Body Posture Based Analysis (WBPBA). PCA is a statistical method for reducing a multidimensional data set by using eigenvectors and eigenvalues. The goal is to create a reduced data set that encapsulates the majority of the variation in the population. WBPBA is a multivariate analytical approach that was developed by the Anthropometry and Biomechanics Facility (ABF) to identify the extremes of the population for a given body posture. WBPBA is a simulation-based method that finds extremes in a population based on anthropometry and posture whereas PCA is based solely on anthropometry. Both methods yield a list of subjects and their anthropometry from the target population; PCA resulted in 20 female and 22 male subjects anthropometry and WBPBA resulted in 7 subjects' anthropometry representing the extreme subjects in the target population. The subjects anthropometry is then used to 'morph' a baseline digital scan of a person with the same body type to create a 3D digital model that can be used as a tool for designers, the details of which will be discussed in subsequent papers.

Young, Karen S.↗
NASA NTRSDate not supplied

Spacesuit Sizing by Multivariate Simulation: Case Study for Liquid Cooling and Ventilation Garment

A technique was developed to assess the fit of spacesuit garment liquid cooling and ventilation garment (LCVG). A fit scoring scheme was built from the spacesuit sizing requirements and guidelines, and the accommodated proportion of the wearer population was estimated from statistically synthesized pseudo population data. The method evaluated different scenarios of LCVG sizing systems including 1) baseline 7-size system, 2) 7-sizes with target body dimensions iteratively adjusted for maximum fit, and 3) 9-sizes with iterative adjustments. Overall, the baseline sizing system showed 81% accommodation rate, while the hypothetical new systems in-tegrating iterative adjustments for 7- and 9-sizes predictively indicated that accommodation could improve up to 92 and 97%. The proposed method provided a structured metric to quantify fit performance of a garment sizing system, which can be further applied to different spacesuit component, PPE and garment designs.

Han Kim↗
NASA NTRS2010

The Response of African Land Surface Phenology to Large Scale Climate Oscillations

Variations in agricultural production due to rainfall and temperature fluctuations are a primary cause of food insecurity on the African continent. Analysis of changes in phenology can provide quantitative information on the effect of climate variability on growing seasons in agricultural regions. Using a robust statistical methodology, we describe the relationship between phenology metrics derived from the 26 year AVHRR NDVI record and the North Atlantic Oscillation index (NAO), the Indian Ocean Dipole (IOD), the Pacific Decadal Oscillation (PDO), and the Multivariate ENSO Index (MEI). We map the most significant positive and negative correlation for the four climate indices in Eastern, Western and Southern Africa between two phenological metrics and the climate indices. Our objective is to provide evidence of whether climate variability captured in the four indices has had a significant impact on the vegetative productivity of Africa during the past quarter century. We found that the start of season and cumulative NDVI were significantly affected by large scale variations in climate. The particular climate index and the timing showing highest correlation depended heavily on the region examined. In Western Africa the cumulative NDVI correlates with PDO in September-November. In Eastern Africa the start of the June-October season strongly correlates with PDO in March-May, while the PDO in December-February correlates with the start of the February-June season. The cumulative NDVI over this last season relates to the MEI of March-May. For Southern Africa, high correlations exist between SOS and NAO of September-November, and cumulative NDVI and MEI of March-May. The research shows that climate indices can be used to anticipate late start and variable vigor in the growing season of sensitive agricultural regions in Africa.

Brown, Molly E.↗
NASA NTRS2020

The role of blocking circulation and emerging open water feedbacks on Greenland cold‐season air temperature variability over the last century

Substantial marine, terrestrial, and atmospheric changes have occurred over the Greenland region during the last century. Several studies have documented record‐levels of Greenland Ice Sheet (GrIS) summer melt extent during the 2000s and 2010s, but relatively little work has been carried out to assess regional climatic changes in other seasons. Here, we focus on the less studied cold‐season (i.e., autumn and winter) climate, tracing the long‐term (1873–2013) variability of Greenland's air temperatures through analyses of coastal observations and model‐derived outlet glacier series and their linkages with North Atlantic sea ice, sea surface temperature (SST), and atmospheric circulation indices. Through a statistical framework, large amounts of west and south Greenland temperature variance (up to r^2~50%) can be explained by the seasonally‐contemporaneous combination of the Greenland Blocking Index (GBI) and the North Atlantic Oscillation (NAO; hereafter the combination of GBI and NAO is termed GBI). Lagged and concomitant regional sea‐ice concentration (SIC) and the Atlantic Multidecadal Oscillation (AMO) seasonal indices account for small amounts of residual air temperature variance (r^2<~10%) relative to the GBI. The correlations between GBI and cold‐season temperatures are predominantly positive and statistically‐significant through time, while regional SIC conditions emerge as a significant covariate from the mid‐20th century through the conclusion of the study period. The inclusion of the cold‐season Pacific Decadal Oscillation (PDO) in multivariate analyses bolsters the air temperature variance explained by the North Atlantic regional predictors, suggesting the remote, background climate state is important to long‐term Greenland temperature variability. These findings imply that large‐scale tropospheric circulation has a strong control on surface temperature over Greenland through dynamic and thermodynamic impacts and stress the importance of understanding the evolving two‐way linkages between the North Atlantic marine and atmospheric environment in order to more accurately predict Greenland seasonal climate variability and change through the 21st century.

air temperatures↗
NASA NTRS1975

The use of ERTS-1 multispectral imagery for crop identification in a semi-arid climate

Crop identification using multispectral satellite imagery and multivariate pattern recognition was used to identify wheat accurately in Greeley County, Kansas. A classification accuracy of 97 percent was found for wheat and the wheat estimate in hectares was within 5 percent of the USDA's Statistical Reporting Service estimate for 1973. The multispectral response of cotton and sorghum in Texas was not unique enough to distinguish between them nor to separate them from other cultivated crops.

Stockton, J. G.↗
NASA NTRS1978

Analysis of vector wind change with respect to time for Cape Kennedy, Florida

Multivariate analysis was used to determine the joint distribution of the four variables represented by the components of the wind vector at an initial time and after a specified elapsed time is hypothesized to be quadravariate normal; the fourteen statistics of this distribution, calculated from 15 years of twice-daily rawinsonde data are presented by monthly reference periods for each month from 0 to 27 km. The hypotheses that the wind component changes with respect to time is univariate normal, that the joint distribution of wind component change with respect to time is univariate normal, that the joint distribution of wind component changes is bivariate normal, and that the modulus of vector wind change is Rayleigh are tested by comparison with observed distributions. Statistics of the conditional bivariate normal distributions of vector wind at a future time given the vector wind at an initial time are derived. Wind changes over time periods from 1 to 5 hours, calculated from Jimsphere data, are presented. Extension of the theoretical prediction (based on rawinsonde data) of wind component change standard deviation to time periods of 1 to 5 hours falls (with a few exceptions) within the 95 percentile confidence band of the population estimate obtained from the Jimsphere sample data. The joint distributions of wind change components, conditional wind components, and 1 km vector wind shear change components are illustrated by probability ellipses at the 95 percentile level.

Adelfang, S. I.↗
NASA NTRS2015

Astronaut Preflight Cardiovascular Variables Associated with Vascular Compliance are Highly Correlated with Post-Flight Eye Outcome Measures in the Visual Impairment Intracranial Pressure (VIIP) Syndrome Following Long Duration Spaceflight

The detection of the first VIIP case occurred in 2005, and adequate eye outcome measures were available for 31 (67.4%) of the 46 long duration US crewmembers who had flown on the ISS since its first crewed mission in 2000. Therefore, this analysis is limited to a subgroup (22 males and 9 females). A "cardiovascular profile" for each astronaut was compiled by examining twelve individual parameters; eleven of these were preflight variables: systolic blood pressure, pulse pressure, body mass index, percentage body fat, LDL, HDL, triglycerides, use of anti‐lipid medication, fasting serum glucose, and maximal oxygen uptake in ml/kg. Each of these variables was averaged across three preflight annual physical exams. Astronaut age prior to the long duration mission, and inflight salt intake was also included in the analysis. The group of cardiovascular variables for each crew member was compared with seven VIIP eye outcome variables collected during the immediate post‐flight period: anterior-posterior axial length of the globe measured by ultrasound and optical biometry; optic nerve sheath diameter, optic nerve diameter, and optic nerve to sheath ratio‐ each measured by ultrasound and magnetic resonance imaging (MRI), intraocular pressure (IOP), change in manifest refraction, mean retinal nerve fiber layer (RNFL) on optical coherence tomography (OCT), and RNFL of the inferior and superior retinal quadrants. Since most of the VIIP eye outcome measures were added sequentially beginning in 2005, as knowledge of the syndrome improved, data were unavailable for 22.0% of the outcome measurements. To address the missing data, we employed multivariate multiple imputation techniques with predictive mean matching methods to accumulate 200 separate imputed datasets for analysis. We were able to impute data for the 22.0% of missing VIIP eye outcomes. We then applied Rubin's rules for collapsing the statistical results across our 200 multiply imputed data sets to assess the canonical correlation between the eye outcomes and the twelve astronaut cardiovascular variables available for all 31 subjects. Results: A highly significant canonical correlation was observed among the canonical solutions (p<.00001), with an average best canonical correlation of.97. The results suggest a strong association between astronauts' measures of cardiovascular health and the seven eye outcomes of the VIIP syndrome used in this analysis. Furthermore, the "joint test" revealed a significant difference in cardiovascular profile between male and female astronauts (Prob > F = 0.00001). Overall, female astronauts demonstrated a significantly healthier cardiovascular status. Individually, the female astronauts had significantly healthier profiles on seven of twelve cardiovascular variables than the men (p values ranging from <0.0001 to <0.05). Male astronauts did not demonstrate significantly healthier values on any of the twelve cardiovascular variables measured

Otto, Christian↗
NASA NTRS1998

Effect of altered sensory conditions on multivariate descriptors of human postural sway

Multivariate descriptors of sway were used to test whether altered sensory conditions result not only in changes in amount of sway but also in postural coordination. Eigenvalues and directions of eigenvectors of the covariance of shnk and hip angles were used as a set of multivariate descriptors. These quantities were measured in 14 healthy adult subjects performing the Sensory Organization test, which disrupts visual and somatosensory information used for spatial orientation. Multivariate analysis of variance and discriminant analysis showed that resulting sway changes were at least bivariate in character, with visual and somatosensory conditions producing distinct changes in postural coordination. The most significant changes were found when somatosensory information was disrupted by sway-referencing of the support surface (P = 3.2 x 10(-10)). The resulting covariance measurements showed that subjects not only swayed more but also used increased hip motion analogous to the hip strategy. Disruption of vision, by either closing the eyes or sway-referencing the visual surround, also resulted in altered sway (P = 1.7 x 10(-10)), with proportionately more motion of the center of mass than with platform sway-referencing. As shown by discriminant analysis, an optimal univariate measure could explain at most 90% of the behavior due to altered sensory conditions. The remaining 10%, while smaller, are highly significant changes in posture control that depend on sensory conditions. The results imply that normal postural coordination of the trunk and legs requires both somatosensory and visual information and that each sensory modality makes a unique contribution to posture control. Descending postural commands are multivariate in nature, and the motion at each joint is affected uniquely by input from multiple sensors.

NASA Discipline Neuroscience↗
NASA NTRS1994

Arm structure in normal spiral galaxies, 1: Multivariate data for 492 galaxies

Multivariate data have been collected as part of an effort to develop a new classification system for spiral galaxies, one which is not necessarily based on subjective morphological properties. A sample of 492 moderately bright northern Sa and Sc spirals was chosen for future statistical analysis. New observations were made at 20 and 21 cm; the latter data are described in detail here. Infrared Astronomy Satellite (IRAS) fluxes were obtained from archival data. Finally, new estimates of arm pattern radomness and of local environmental harshness were compiled for most sample objects.

Magri, Christopher↗
NASA NTRS1999

Uncertainty Analysis of Instrument Calibration and Application

Experimental aerodynamic researchers require estimated precision and bias uncertainties of measured physical quantities, typically at 95 percent confidence levels. Uncertainties of final computed aerodynamic parameters are obtained by propagation of individual measurement uncertainties through the defining functional expressions. In this paper, rigorous mathematical techniques are extended to determine precision and bias uncertainties of any instrument-sensor system. Through this analysis, instrument uncertainties determined through calibration are now expressed as functions of the corresponding measurement for linear and nonlinear univariate and multivariate processes. Treatment of correlated measurement precision error is developed. During laboratory calibration, calibration standard uncertainties are assumed to be an order of magnitude less than those of the instrument being calibrated. Often calibration standards do not satisfy this assumption. This paper applies rigorous statistical methods for inclusion of calibration standard uncertainty and covariance due to the order of their application. The effects of mathematical modeling error on calibration bias uncertainty are quantified. The effects of experimental design on uncertainty are analyzed. The importance of replication is emphasized, techniques for estimation of both bias and precision uncertainties using replication are developed. Statistical tests for stationarity of calibration parameters over time are obtained.

Tripp, John S.↗
NASA NTRS2014

Tropospheric Ozone Increases over the Southern Africa Region: Bellwether for Rapid Growth in Southern Hemisphere Pollution?

Increases in free-tropospheric (FT) ozone based on ozonesonde records from the early 1990s through 2008 over two subtropical stations, Irene (near Pretoria, South Africa) and Réunion (21 deg. S, 55 deg. E; approx. 2800 km NE of Irene in the Indian Ocean), have been reported. Over Irene a large increase in the urban-influenced boundary layer (BL, 1.5-4 km) was also observed during the 18-year period, equivalent to 30%decade−1. Here we show that the Irene BL trend is at least partly due to a gradual change in the sonde launch times from early morning to the midday period. The FT ozone profiles over Irene in 1990-2007 are re-examined, filling in a 1995-1999 gap with ozone profiles taken during the Measurements of Ozone by Airbus In-service Aircraft (MOZAIC) project over nearby Johannesburg. A multivariate regression model that accounts for the annual ozone cycle, El Niño-Southern Oscillation (ENSO) and possible tropopause changes was applied to monthly averaged Irene data from 4 to 11 km and to 1992-2011 Réunion sonde data from 4 to 15 km. Statistically significant trends appear predominantly in the middle and upper troposphere (UT; 4-11 km over Irene, 4-15 km over Réunion) in winter (June-August), with increases 1 ppbv yr(exp. −1) over Irene and approx. 2 ppbv yr(exp. −1) over Réunion. These changes are equivalent to approx. 25 and 35-45%decade( exp. −1), respectively. Both stations also display smaller positive trends in summer, with a 45%decade(exp. −1) ozone increase near the tropopause over Réunion in December. To explain the ozone increases, we investigated a time series of dynamical markers, e.g., potential vorticity (PV) at 330-350 K. PV affects UT ozone over Irene in November-December but displays little relationship with ozone over Réunion. A more likely reason for wintertime FT ozone increases over Irene and Réunion appears to be long-range transport of growing pollution in the Southern Hemisphere. The ozone increases are consistent with trajectory origins of air parcels sampled by the sondes and with recent NOx emissions trends estimated for Africa, South America and Madagascar. For Réunion trajectories also point to sources from the eastern Indian Ocean and Asia.

Trends↗
NASA NTRS1990

Atmospheric water vapour over oceans from SSM/I measurements

A statistical retrieval technique is developed to derive the atmospheric water vapor column content from the Special Sensor Microwave/Imager (SSM/I) measurements. The radiometer signals are simulated by means of radiative-transfer calculations for a large set of atmospheric/oceanic situations. These simulated responses are subsequently summarized by multivariate analyses, giving water-vapor coefficients and error estimates. Radiative-transfer calculations show that the SSM/I microwave imager can detect atmospheric water vapor structures with an accuracy from 0.145 to 0.17 g/sq cm. The accuracy of the method is confirmed by globally distributed match-ups with radiosonde measurements.

Schluessel, Peter↗
NASA NTRS1989

Sources of error in thematic classification of remotely sensed imagery

From a statistician's point of view, the input datasets are rarely examined to determine their underlying frequency distribution; it is just assumed that the data are normal enough, and that the deviations from normality are unimportant. It is not clear how deviations from a hypothetical multivariate normal might affect the power of the classification process, and there is ample evidence in the literature that, at a minimum, the spectral channels are correlated. From a practioner's point of view, in a supervised classification the number of training fields for developing a statistical description of a given class is usually arbitrary. It is unclear how small changes in the details of the training field selection process affect the quality of the derived thematic information. The start of an examination of this latter problem is discussed.

Star, Jeffrey L.↗
NASA NTRS2014

NMME Monthly / Seasonal Forecasts for NASA SERVIR Applications Science

This work details use of the North American Multi-Model Ensemble (NMME) experimental forecasts as drivers for Decision Support Systems (DSSs) in the NASA / USAID initiative, SERVIR (a Spanish acronym meaning "to serve"). SERVIR integrates satellite observations, ground-based data and forecast models to monitor and forecast environmental changes and to improve response to natural disasters. Through the use of DSSs whose "front ends" are physically based models, the SERVIR activity provides a natural testbed to determine the extent to which NMME monthly to seasonal projections enable scientists, educators, project managers and policy implementers in developing countries to better use probabilistic outlooks of seasonal hydrologic anomalies in assessing agricultural / food security impacts, water availability, and risk to societal infrastructure. The multi-model NMME framework provides a "best practices" approach to probabilistic forecasting. The NMME forecasts are generated at resolution more coarse than that required to support DSS models; downscaling in both space and time is necessary. The methodology adopted here applied model output statistics where we use NMME ensemble monthly projections of sea-surface temperature (SST) and precipitation from 30 years of hindcasts with observations of precipitation and temperature for target regions. Since raw model forecasts are well-known to have structural biases, a cross-validated multivariate regression methodology (CCA) is used to link the model projected states as predictors to the predictands of the target region. The target regions include a number of basins in East and South Africa as well as the Ganges / Baramaputra / Meghna basin complex. The MOS approach used address spatial downscaling. Temporal disaggregation of monthly seasonal forecasts is achieved through use of a tercile bootstrapping approach. We interpret the results of these studies, the levels of skill by several metrics, and key uncertainties.

Robertson, Franklin R.↗