A mutual information criterion with applications to canonical correlation analysis and graphical models
Explore the source record for details and available documents.
Timothy DelSole↗
SEARCH · Search NASA
Results for “canonical correlation analysis”
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.
Analysis of atmosphere-sea ice interactions in the Arctic basin using ESMR microwave data
Data from the Electrically Scanning Microwave Radiometer (ESMR) on Nimbus-5 are used to determine the spatial and temporal patterns of change in microwave brightness signatures of Arctic sea ice during a full annual cycle (1973/74). Interactions of ice conditions with the atmosphere are examined using grid point data for surface air temperature and atmospheric pressure. Principal components analysis is used to examine the major elements present in the microwave and atmospheric data. Component scores from these analyses are then used in a canonical correlation analysis to determine interassociations present between the ice and atmosphere in the Beaufort Sea and the European sectors on a synoptic time scale. The synoptic weather conditions associated with the pattern of snow melt on the ice in spring 1974 are described, and a clarification of possible alternative interpretations of features identified as polynyi occurring at 80-85 deg N during the late summer 1974 is presented.
Crane, R. G.↗
Statistical Process Control and Analysis on the Water Content Measurements in NASA Glenn’s Icing Research Tunnel
The Icing Research Tunnel at NASA Glenn follows the recommended practice for calibration outlined in SAE’s ARP5905. The calibration team has followed the schedule of a full calibration every five years with a check calibration done every six months following. The liquid water content of the IRT has maintained stability within the stated specifications of variation within +/- 10% of the curve fit equation generated from calibration data. Using past measurements and data trends, IRT characterization engineers wanted to develop methods for the ability to know when data were not within variation. Trends can be observed in the liquid water content measurement process by constructing statistical process control charts. This paper describes data processing procedures for the Multi-Element Sensor in the IRT, including collision efficiency corrections, canonical correlation analysis, process for rejection of data, and construction of control charts. Data are presented to display the control capability to meet defined liquid water content specifications of the IRT with the Multi-Element Sensor mounted in the center of the test section.
Statistical Process Control↗
Statistical Process Control and Analysis on the Water Content Measurements in NASA Glenn’s Icing Research Tunnel
The Icing Research Tunnel at NASA Glenn follows the recommended practice for calibration outlined in SAE’s ARP5905. The calibration team has followed the schedule of a full calibration every five years with a check calibration done every six months following. The liquid water content of the IRT has maintained stability within the stated specifications of variation within +/- 10% of the curve fit equation generated from calibration data. Using past measurements and data trends, IRT characterization engineers wanted to develop methods for the ability to know when data were not within variation. Trends can be observed in the liquid water content measurement process by constructing statistical process control charts. This paper describes data processing procedures for the Multi-Element Sensor in the IRT, including collision efficiency corrections, canonical correlation analysis, process for rejection of data, and construction of control charts. Data are presented to display the control capability to meet defined liquid water content specifications of the IRT with the Multi-Element Sensor mounted in the center of the test section.
Statistical Process Control↗
Error Estimation of An Ensemble Statistical Seasonal Precipitation Prediction Model
This NASA Technical Memorandum describes an optimal ensemble canonical correlation forecasting model for seasonal precipitation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. Since new CCA scheme is derived for continuous fields of predictor and predictand, an area-factor is automatically included. Thus our model is an improvement of the spectral CCA scheme of Barnett and Preisendorfer. The improvements include (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States (US) precipitation field. The predictor is the sea surface temperature (SST). The US Climate Prediction Center's reconstructed SST is used as the predictor's historical data. The US National Center for Environmental Prediction's optimally interpolated precipitation (1951-2000) is used as the predictand's historical data. Our forecast experiments show that the new ensemble canonical correlation scheme renders a reasonable forecasting skill. For example, when using September-October-November SST to predict the next season December-January-February precipitation, the spatial pattern correlation between the observed and predicted are positive in 46 years among the 50 years of experiments. The positive correlations are close to or greater than 0.4 in 29 years, which indicates excellent performance of the forecasting model. The forecasting skill can be further enhanced when several predictors are used.
Shen, Samuel S. P.↗
Troposphere-Stratosphere Dynamic Coupling Under Strong and Weak Polar Vortex Conditions
The relationship between Northern Hemisphere (NH) tropospheric and stratospheric wave-like anomalies of spherical zonal wave number (ZWN) 1 is studied by applying Canonical Correlation Analysis (CCA). A lag-correlation technique is used with 10-day lowpass filtered daily time series of 50- and 500-hPa geopotential heights. Generally stratospheric circulation is determined by ultralong tropospheric planetary waves. During winter seasons characterized either by any anomalously strong or weak polar winter vortex different propagation characteristics for waves of ZWN 1 are observed. The non-linear perspective of the results have implications for medium range weather forecast and climate sensitivity experiments.
Perlwitz, Judith↗
A Canonical Ensemble Correlation Prediction Model for Seasonal Precipitation Anomaly
This report describes an optimal ensemble forecasting model for seasonal precipitation and its error estimation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. This new CCA model includes the following features: (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States precipitation field. The predictor is the sea surface temperature.
Shen, Samuel S. P.↗
Kernel PLS-SVC for Linear and Nonlinear Discrimination
A new methodology for discrimination is proposed. This is based on kernel orthonormalized partial least squares (PLS) dimensionality reduction of the original data space followed by support vector machines for classification. Close connection of orthonormalized PLS and Fisher's approach to linear discrimination or equivalently with canonical correlation analysis is described. This gives preference to use orthonormalized PLS over principal component analysis. Good behavior of the proposed method is demonstrated on 13 different benchmark data sets and on the real world problem of the classification finger movement periods versus non-movement periods based on electroencephalogram.
Rosipal, Roman↗
Ocean-Atmosphere Coupling in the Mediterranean Sea from TOPEX/POSEIDON, ERS1 and AVHRR Data
This study examines the ocean-atmosphere coupling patterns in time and space through a canonical correlation analysis of the fields.
Satellite ERS1 seasonal AVHRR Ocean Project↗
Statistical Process Control and Capability Study on the Water Content Measurements in NASA Glenn's Icing Research Tunnel
The Icing Research Tunnel (IRT) at NASA Glenn Research Center follows the recommended practice for icing tunnel calibration outlined in SAE's ARP5905 document. The calibration team has followed the schedule of a full calibration every five years with a check calibration done every six months following. The liquid water content of the IRT has maintained stability within in the specifications presented to customers that the variation is within +/- 10% of the calibrated, target measurement. With recent measurements and instrumentation errors, a more thorough assessment of error source was desired. By constructing statistical process control charts, the ability to determine how the instrument varies in the short term, mid term, and long term was gained. The control charts offer a view of instrument error, facility error, or installation changes. It was discovered that there was a shift from target to mean baseline thus leading to the study of the overall capability indices of the liquid water content measuring instrument to perform within specifications defined in the IRT. This presentation describes data processing procedures for the Multi-Element Sensor in the IRT, including collision efficiency corrections, canonical correlation analysis, Chauvenet's Criterion for rejection of data, distribution check of data, and mean, median and mode for construction of control charts. Further data is presented to describe the repeatability of the IRT with the Multi-Element Sensor and the ability to maintain a stable process for the defined calibration schedule.
Timko, Emily N.↗
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↗
An improved multiple linear regression and data analysis computer program package
NEWRAP, an improved version of a previous multiple linear regression program called RAPIER, CREDUC, and CRSPLT, allows for a complete regression analysis including cross plots of the independent and dependent variables, correlation coefficients, regression coefficients, analysis of variance tables, t-statistics and their probability levels, rejection of independent variables, plots of residuals against the independent and dependent variables, and a canonical reduction of quadratic response functions useful in optimum seeking experimentation. A major improvement over RAPIER is that all regression calculations are done in double precision arithmetic.
Sidik, S. M.↗
Use of simplified flow separation criteria for slotted flap preliminary design
From experimental correlations of airfoil and flap pressure distributions, it is observed that flow separation is likely to occur when the canonical pressure recovery coefficient (C sub pr) exceeds a critical value. A procedure is described for obtaining the C sub pr parameter from modified inviscid analysis. The procedure has been applied to preliminary design studies of a new slotted flap to determine the influence of shape and location. Experiments are planned to evaluate the flap designed by this procedure.
Wentz, W. H., Jr.↗
Springtime SEA Air Is Consistently Both Smoky and Humid, but This Relationship (and Its Radiative Effects) Varies Spatially and Seasonally
The atmosphere over the southeast Atlantic Ocean (SEA) sees a consistent springtime biomass burning (BB) smoke from widespread agricultural fires on the African continent. This smoke layer is initially lofted high in a continental mixed layer (~5-6km) and is then transported westward in the free troposphere, where it overlies and ultimately mixes into the SEA stratocumulus-topped oceanic boundary layer. Coincident with this smoke is an elevated humidity signal which is present from the time a given airmass is over the continental source region. ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) was a NASA Earth Venture Suborbital mission with the goal of measuring aerosol, cloud, and atmospheric properties over this region during three deployments in September 2016, August 2017, and October 2018. A previous study discussed the spatial characteristics of the water vapor-BB plume observations in the September 2016 flight data and their degree of agreement with several atmospheric reanalyses and models. In the present work, we first discuss atmospheric chemistry, structure, and aerosol results from all three ORACLES deployments to place these results in a broader seasonal context. Despite the differing locations and season of each deployment, we show that there continues to be good agreement between the airborne ORACLES dataset and large-scale reanalyses, specifically the ECMWF ERA5 and CAMS reanalyses. Other reanalyses (specifically NASA's MERRA-2) preserved the observed correlation between meteorology and biomass burning conditions, but, as was seen for 2016, was frequently displaced spatially relative to the observations. The CAMS reanalysis performs better with water vapor than with CO. Results comparing CAMS column AOD to the field measurements show slight overestimates, consistent with previous analysis, but the good relationship observed between AOD and inlet-based aerosol extinction demonstrates this metric's utility to broader studies. The good ERA5/CAMS/ORACLES agreement allows us to next examine the multi-year seasonal patterns and trends beyond the three ORACLES deployment periods. Looking at seven years of reanalysis data for the BB season, we find distinct variations between each month/deployment in terms of vertical smoke distribution and correlation to atmospheric specific humidity, due to changing conditions through the BB season. Using k-means clustering of these climatological reanalysis, we identify six canonical atmospheric profile types of varying total atmospheric humidity and vertical structure and describe their changing incidence spatially and throughout the season, and six analogous profile types for carbon monoxide, allowing us to characterize the atmospheric structure of both vapor and BB over time throughout the SEA region. This classification will allow for a more complete analysis of the broader radiative and dynamical effects of humid aerosols overlying stratocumulus clouds.
Kristina Marie Myers Pistone↗
Application of ecological, geological and oceanographic ERTS-1 imagery to Delaware's coastal resources management
The author has identified the following significant results. Data from twelve successful ERTS-1 passes over Delaware Bay have been analyzed with special emphasis on coastal vegetation, land use, current circulation, water turbidity and pollution dispersion. Secchi depth, suspended sediment concentration and transmissivity as measured from helicopters and boats were correlated with ERTS-1 image radiance. Multispectral signatures of acid disposal plumes, sediment plumes and slick were investigated. Ten vegetative cover and water discrimination classes were selected for mapping: (1) forest-land; (2) Phragmites communis; (3) Spartina patens and Distichlis spicata; (4) Spartina alterniflora; (5) cropland; (6) plowed cropland; (7) sand and bare sandy soil; (8) bare mud; (9) deep water; and (10) sediment-laden and shallow water. Canonical analysis predicted good classification accuracies for most categories. The actual classification accuracies were very close to the predicted values with 8 of 10 categories classified with greater than 90% accuracy indicating that representative training sets had been selected.
Klemas, V.↗
Disentangling the Hard X-ray Background ROSAT HRI
The goal of our investigation was to improve our understanding of the properties of the discrete X-ray sources that produce the X-ray background (XRB). Many surveys have shown that the XRB at energies of 0.5-3.0 keV is dominated by emission from extragalactic point sources and that a significant fraction of the XRB at higher energies also is produced by discrete sources. In spite of the fact that the bulk of the 0.5-10 keV XRB was demonstrated to arise from extragalactic point sources, the spectral shape of the background presented a difficulty, referred to as the "spectral paradox". Spectra for classes of individual sources generally have been found to be incompatible with the observed energy index, alpha = 0.4 of the XRB over the 2-10 keV energy range. For the 0.3-3.0 keV Einstein band, Macca'caro et al. (1988) derived a mean energy spectral index of a approx. 0.95 for 599 extragalactic sources and for a subset of X-ray selected AGN, found alpha = 1.03(sup +0-05, sub -0.06) . Wilkes and Elvis showed that radio "loudness" was strongly correlated with the source spectrum, such that radio-load quasars exhibited flatter spectra (alpha approx. 0.5), while radio-quiet quasars had steeper spectra (alpha approx. 1). Studies of moderately faint sources in the 0.1-2.0 keV ROSAT band also found rather steep spectra (alpha = 0.96 +/- 0.11 for sources with an average flux of 1.5 x 10(exp -14) ergs/sq cm sec). At higher energies and much higher fluxes, energy spectra of individual AGN suggested a "canonical" alpha = 0.7 energy spectrum. Thus, the best evidence suggested that known classes of AGN could not readily explain the observed X-ray background spectrum. In our ROSAT PSPC analysis, we studied not only the traditional log N - log S, but also the spectral properties of the sources. We computed hardness ratios for individual sources and performed spectral fits to the summed source spectra, averaged in flux bins from 10(exp -15) to 10(exp -12) ergs/sq cm sec. We found that the hardness ratios and source spectra consistently show a hardening in the source spectra as the source fluxes decrease, confirming the suggestion by Hasinger et al. (1993).
Murray, Stephen S.↗
PSE-Based Aerodynamic Flow Transition Prediction Using Automated Unstructured CFD Integration
Accurate, robust, and efficient prediction of transition in viscous flows is a significant challenge in computational fluid dynamics. We present a coupled, high-fidelity, iterative approach that leverages the FUN3D flow solver and the LASTRAC stability code to predict transition in low-disturbance environments, initiated by the linear growth of boundary-layer instability modes. Our method integrates the ability of FUN3D to compute mixed laminar-transitional-turbulent mean flows via transition-sensitized Reynolds-Averaged Navier-Stokes equations with the ability of LASTRAC to perform linear stability analysis, all within an automated framework that requires no intermediate user involvement. Unlike conventional frameworks that rely on classical stability theory or reduced-order metamodels, our approach employs the parabolized stability equations to provide more accurate and reliable estimates of disturbance growth for multiple instability mechanisms, including Tollmien-Schlichting, Kelvin-Helmholtz, and crossflow modes. By accounting for the effects of mean-flow nonparallelism as well as the surface curvature, this approach lays the foundation for improved N-factor correlations for transition onset prediction in a broad class of flows. We apply this method to three distinct flow configurations: 1) flow over a zero-pressure-gradient flat plate, 2) the NLF-0416 airfoil with both natural and separation-induced transitions, and 3) a 6:1 prolate spheroid, where transition is primarily driven by crossflow instability. For the two-dimensional cases, a formulated intermittency distribution is used to model the transition zone in between the laminar and fully turbulent flows. The results include comparisons with experimental measurements, similar numerical approaches, and transport-equations-based models, demonstrating good agreement in surface pressure coefficients, transition onset locations, and skin-friction coefficients for all three configurations. Besides contributing a couple of new insights into boundary-layer transition in these canonical cases, this study provides a powerful tool for transition modeling in both research and design applications in aerodynamics.
Transition Prediction↗
A statistical analysis of the dependency of closure assumptions in cumulus parameterization on the horizontal resolution
Simulated data from the UCLA cumulus ensemble model are used to investigate the quasi-universal validity of closure assumptions used in existing cumulus parameterizations. A closure assumption is quasi-universally valid if it is sensitive neither to convective cloud regimes nor to horizontal resolutions of large-scale/mesoscale models. The dependency of three types of closure assumptions, as classified by Arakawa and Chen, on the horizontal resolution is addressed in this study. Type I is the constraint on the coupling of the time tendencies of large-scale temperature and water vapor mixing ratio. Type II is the constraint on the coupling of cumulus heating and cumulus drying. Type III is a direct constraint on the intensity of a cumulus ensemble. The macroscopic behavior of simulated cumulus convection is first compared with the observed behavior in view of Type I and Type II closure assumptions using 'quick-look' and canonical correlation analyses. It is found that they are statistically similar to each other. The three types of closure assumptions are further examined with simulated data averaged over selected subdomain sizes ranging from 64 to 512 km. It is found that the dependency of Type I and Type II closure assumptions on the horizontal resolution is very weak and that Type III closure assumption is somewhat dependent upon the horizontal resolution. The influences of convective and mesoscale processes on the closure assumptions are also addressed by comparing the structures of canonical components with the corresponding vertical profiles in the convective and stratiform regions of cumulus ensembles analyzed directly from simulated data. The implication of these results for cumulus parameterization is discussed.
Xu, Kuan-Man↗