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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

The global structure of the annual and semiannual sea surface height variability from Geosat altimeter data

Two years of data from the Geosat Exact Repeat Mission are employed to study the annual and semiannual variability of sea-surface height on a global basis. The data are treated with atmospheric corrections and interpolations, and the orbit error of about 40 cm RMS is purged. The spatial variability of the Geosat data is constructed for particular frequencies, and the estimate of the M2 tidal error indicates that the error is aliased to 1.15 cycles/yr and has an unusual spatial pattern. Sine and cosine coefficients are derived for the annual and semiannual frequencies by means of a least squares fit yielding the amplitude and phase of the changes in sea-surface height. A 180-deg phase difference is noted between the Northern and Southern Hemispheres for the annual variability, and large-scale westward propagating waves are identified. The Geosat data also indicate the phase relationships between major current systems and the systems of variations at work in the Intertropical Convergence Zone.

Jacobs, Gregg A.↗
NASA NTRS1971

Preprocessing techniques to reduce atmospheric and sensor variability in multispectral scanner data.

Multispectral scanner data are potentially useful in a variety of remote sensing applications. Large-area surveys of earth resources carried out by automated recognition processing of these data are particularly important. However, the practical realization of such surveys is limited by a variability in the scanner signals that results in improper recognition of the data. This paper discusses ways by which some of this variability can be removed from the data by preprocessing with resultant improvements in recognition results.

Crane, R. B.↗
NASA NTRS1971

Adaptive variable-length coding for efficient compression of spacecraft television data.

An adaptive variable length coding system is presented. Although developed primarily for the proposed Grand Tour missions, many features of this system clearly indicate a much wider applicability. Using sample to sample prediction, the coding system produces output rates within 0.25 bit/picture element (pixel) of the one-dimensional difference entropy for entropy values ranging from 0 to 8 bit/pixel. This is accomplished without the necessity of storing any code words. Performance improvements of 0.5 bit/pixel can be simply achieved by utilizing previous line correlation. A Basic Compressor, using concatenated codes, adapts to rapid changes in source statistics by automatically selecting one of three codes to use for each block of 21 pixels. The system adapts to less frequent, but more dramatic, changes in source statistics by adjusting the mode in which the Basic Compressor operates on a line-to-line basis. Furthermore, the compression system is independent of the quantization requirements of the pulse-code modulation system.

Rice, R. F.↗
NASA NTRS1985

Method for evaluating wind turbine wake effects on wind farm performance

A method of testing the performance of a cluster of wind turbine units an data analysis equations are presented which together form a simple and direct procedure for determining the reduction in energy output caused by the wake of an upwind turbine. This method appears to solve the problems presented by data scatter and wind variability. Test data from the three-unit Mod-2 wind turbine cluster at Goldendale, Washington, are analyzed to illustrate the application of the proposed method. In this sample case the reduction in energy was found to be about 10 percent when the Mod-2 units were separated a distance equal to seven diameters and winds were below rated.

Neustadter, H. E.↗
NASA NTRS1996

Precipitable Water Variability Using SSM/I and GOES VAS Pathfinder Data Sets

Determining moisture variability for all weather scenes is critical to understanding the earth's hydrologic cycle and global climate changes. Remote sensing from geostationary satellites provides the necessary temporal and spatial resolutions necessary for global change studies. Due to antenna size constraints imposed with the use of microwave radiometers, geostationary satellites have carried instruments passively measuring radiation at infrared wavelengths or shorter. The shortfall of using infrared instruments in moisture studies lies in its inability to sense terrestrial radiation through clouds. Microwave emissions, on the other hand, are mostly unaffected by cloudy atmospheres. Land surface emissivity at microwave frequencies exhibit both high temporal and spatial variability thus confining moisture retrievals at microwave frequencies to over marine atmospheres (a near uniform cold background). This study intercompares the total column integrated water content Precipitable Water, (PW) as derived from both the Special Sensor Microwave Imager (SSM/I) and the Geostationary Operational Environmental Satellite (GOES) VISSR Atmospheric Sounder (VAS) pathfinder data sets. PW is a bulk parameter often used to quantify moisture variability and is important to understanding the earth's hydrologic cycle and climate system. This research has been spawned in an effort to combine two different algorithms which together can lead to a more comprehensive quantification of global water vapor. The approach taken here is to intercompare two independent PW retrieval algorithms and to validate the resultant retrievals against an existing data set, namely the European Center for Medium range Weather Forecasts (ECMWF) model analysis data.

Lerner, Jeffrey A.↗
NASA NTRS1995

Linear and nonlinear trending and prediction for AVHRR time series data

The variability of AVHRR calibration coefficient in time was analyzed using algorithms of linear and non-linear time series analysis. Specifically we have used the spline trend modeling, autoregressive process analysis, incremental neural network learning algorithm and redundancy functional testing. The analysis performed on available AVHRR data sets revealed that (1) the calibration data have nonlinear dependencies, (2) the calibration data depend strongly on the target temperature, (3) both calibration coefficients and the temperature time series can be modeled, in the first approximation, as autonomous dynamical systems, (4) the high frequency residuals of the analyzed data sets can be best modeled as an autoregressive process of the 10th degree. We have dealt with a nonlinear identification problem and the problem of noise filtering (data smoothing). The system identification and filtering are significant problems for AVHRR data sets. The algorithms outlined in this study can be used for the future EOS missions. Prediction and smoothing algorithms for time series of calibration data provide a functional characterization of the data. Those algorithms can be particularly useful when calibration data are incomplete or sparse.

Smid, J.↗
NASA NTRS1998

Predicting Ecologically Important Vegetation Variables from Remotely Sensed Optical/Radar Data Using Neural Networks

A number of satellite sensor systems will collect large data sets of the Earth's surface during NASA's Earth Observing System (EOS) era. Efforts are being made to develop efficient algorithms that can incorporate a wide variety of spectral data and ancillary data in order to extract vegetation variables required for global and regional studies of ecosystem processes, biosphere-atmosphere interactions, and carbon dynamics. These variables are, for the most part, continuous (e.g. biomass, leaf area index, fraction of vegetation cover, vegetation height, vegetation age, spectral albedo, absorbed photosynthetic active radiation, photosynthetic efficiency, etc.) and estimates may be made using remotely sensed data (e.g. nadir and directional optical wavelengths, multifrequency radar backscatter) and any other readily available ancillary data (e.g., topography, sun angle, ground data, etc.). Using these types of data, neural networks can: 1) provide accurate initial models for extracting vegetation variables when an adequate amount of data is available; 2) provide a performance standard for evaluating existing physically-based models; 3) invert multivariate, physically based models; 4) in a variable selection process, identify those independent variables which best infer the vegetation variable(s) of interest; and 5) incorporate new data sources that would be difficult or impossible to use with conventional techniques. In addition, neural networks employ a more powerful and adaptive nonlinear equation form as compared to traditional linear, index transformations, and simple nonlinear analyses. These neural networks attributes are discussed in the context of the authors' investigations of extracting vegetation variables of ecological interest.

Kimes, Daniel S.↗
NASA NTRS1981

Total ozone trend significance from space time variability of daily Dobson data

Estimates of standard errors of total ozone time and area means, as derived from ozone's natural temporal and spatial variability and autocorrelation in middle latitudes determined from daily Dobson data are presented. Assessing the significance of apparent total ozone trends is equivalent to assessing the standard error of the means. Standard errors of time averages depend on the temporal variability and correlation of the averaged parameter. Trend detectability is discussed, both for the present network and for satellite measurements.

Wilcox, R. W.↗
NASA NTRS1993

Observation of 3-6 day meridional wind oscillations over the tropical Pacific, 1973-1992: Vertical structure and interannual variability

Rawinsonde data from tropical Pacific stations were examined for westward-propagating 3-6-day meridional wind oscillations in the troposphere and lower stratosphere, 1973-1992. Four types were identified from cross-spectrum and principal component analysis. (1) The dominant oscillation, near 250 mb, had a period slightly greater than 5 days, zonal wavenumber 4-6, and modified Rossby-gravity structure near the date line. (2) In the western Pacific lower troposphere there was broadband activity with short zonal scale, coupled to upper-tropospheric waves in NH summer. (3) In the central Pacific, during NH autumn, there was a well-defined approximately 4 1/2-day oscillation with maximum amplitude in the lower troposphere and baroclinic phase tilt above. The vertical structure suggested coupling to deep tropical convection; this interpretation was supported by correlation of meridional wind with antisymmetric outgoing longwave radiation. (4) In the stratosphere, Rossby-gravity waves had periods less than or equal to 4 days and zonal wavenumber 3-4. Unlike tropospheric waves, these disturbances were coherent in a shallow layer, largest in west phase of quasi-biennial oscillation (QBO) and annual cycle (NH winter-spring).

Dunkerton, Timothy J.↗
NASA NTRS2011

Iterative Strain-Gage Balance Calibration Data Analysis for Extended Independent Variable Sets

A new method was developed that makes it possible to use an extended set of independent calibration variables for an iterative analysis of wind tunnel strain gage balance calibration data. The new method permits the application of the iterative analysis method whenever the total number of balance loads and other independent calibration variables is greater than the total number of measured strain gage outputs. Iteration equations used by the iterative analysis method have the limitation that the number of independent and dependent variables must match. The new method circumvents this limitation. It simply adds a missing dependent variable to the original data set by using an additional independent variable also as an additional dependent variable. Then, the desired solution of the regression analysis problem can be obtained that fits each gage output as a function of both the original and additional independent calibration variables. The final regression coefficients can be converted to data reduction matrix coefficients because the missing dependent variables were added to the data set without changing the regression analysis result for each gage output. Therefore, the new method still supports the application of the two load iteration equation choices that the iterative method traditionally uses for the prediction of balance loads during a wind tunnel test. An example is discussed in the paper that illustrates the application of the new method to a realistic simulation of temperature dependent calibration data set of a six component balance.

Ulbrich, Norbert Manfred↗
NASA NTRS1975

Pressure contact sounding data for NASA's Atmospheric Variability Experiment (AVE 2)

The basic rawinsonde data are described at each pressure contact from the surface to sounding termination for the 54 stations participating in the AVE 2 pilot experiment. Soundings were taken at three-hour intervals from stations within the United States east of about 105 degrees west longitude. Methods of data reduction and estimates of data accuracy are discussed. Examples of the data records produced are shown. The AVE 2 pilot experiment was conducted as part of NASA's program to better understand and establish the extent of applications for meteorological satellite sensor data through correlative ground truth experiments and to provide basic experimental data for use in studies of atmospheric scales-of-motion interrelationships.

Fuelberg, H. E.↗
NASA NTRS1975

Pressure Contact Sounding Data for NASA's Atmospheric Variability Experiment (AVE 3)

The basic rawinsonde data are described at each pressure contact from the surface to sounding termination for the 41 stations participating in the AVE III measurement program that began at 0000 GMT on February 6 and ended at 1200 GMT on February 7, 1975. Soundings were taken at 3-hour intervals during a large period of the experiment from most stations within the United States east of about 105 degrees west longitude. Methods of data processing, change in reduction scheme since the AVE II pilot experiment, and data accuracy are briefly discussed. An example of contact data is presented, and microfiche cards of all the contact data are included in the appendix. The AVE III project was conducted to better understand and establish the extent of applications for meteorological satellite sensor data through correlative ground truth experiments and to provide basic experimental data for use in studies of atmospheric scales of-motion interrelationships.

Fuelberg, H. E.↗
NASA NTRS1988

Energy distributions of blazars

This paper presents observations of 162 blazars, over one-half of which were detected with data from the IRAS satellite using a coadded survey and pointed observations. The far-IR data were combined with published data at other wavelengths to complete 93 energy distributions from 10 to the 8th to 10 to the 18th Hz. The IRAS data were used to search for variability on time scales of 3-9 months in 24 objects. The detection rate of variability found for blazars was nearly 40 percent, as compared with a complete absence of detected variability in data sets for quasars and Seyfert galaxies. The range of variability in both spectral index and flux density increases with decreasing wavelength. Half of the blazars with total luminosities less than 10 to the 12th solar luminosity show evidence for thermal IR component. The global energy budget of blazars is dominated by IR emission from 1 to 100 microns.

Impey, C. D.↗
NASA NTRS2004

Recent Climate Variability in Antarctica from Satellite-derived Temperature Data

Recent Antarctic climate variability on month-to-month to interannual time scales is assessed through joint analysis of surface temperatures from satellite thermal infrared observations (T(sub IR)) and passive microwave brightness temperatures (T(sub B)). Although Tw data are limited to clear-sky conditions and T(sub B) data are a product of the temperature and emissivity of the upper approx. 1m of snow, the two data sets share significant covariance. This covariance is largely explained by three empirical modes, which illustrate the spatial and temporal variability of Antarctic surface temperatures. T(sub B) variations are damped compared to TIR variations, as determined by the period of the temperature forcing and the microwave emission depth; however, microwave emissivity does not vary significantly in time. Comparison of the temperature modes with Southern Hemisphere (SH) 500-hPa geopotential height anomalies demonstrates that Antarctic temperature anomalies are predominantly controlled by the principal patterns of SH atmospheric circulation. The leading surface temperature mode strongly correlates with the Southern Annular Mode (SAM) in geopotential height. The second temperature mode reflects the combined influences of the zonal wavenumber-3 and Pacific South American (PSA) patterns in 500-hPa height on month-to-month timescales. ENSO variability projects onto this mode on interannual timescales, but is not by itself a good predictor of Antarctic temperature anomalies. The third temperature mode explains winter warming trends, which may be caused by blocking events, over a large region of the East Antarctic plateau. These results help to place recent climate changes in the context of Antarctica's background climate variability and will aid in the interpretation of ice core paleoclimate records.

Schneider, David P.↗
NASA NTRS1956

Preliminary Internal Performance Data for a Variable-Ejector Assembly on the XJ79-GE-1 Turbojet Engine: Nonafterburning Configurations - 1

Internal performance of an XJ79-GE-1 variable ejector was experimentally determined with the primary nozzle in a representative nonafterburning position. Jet-thrust and air-handling data were obtained in quiescent air for 11 selected ejector configurations over a wide range of operation. Additional data, at specific operating conditions, were obtained which indicate the ejector diameter ratio for peak jet-thrust performance. The experimental ejector data are presented in both graphical and tabulated form.

Greathouse, William K.↗