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At least 703 records · Page 39

Evaluation and Analysis of Seasat a Scanning Multichannel Microwave Radiometer (SMMR) Antenna Pattern Correction (APC) Algorithm

The accuracy of the SEASAT-A SMMR antenna pattern correction (APC) algorithm was assessed. Interim APC brightness temperature measurements for the SMMR 6.6 GHz channels are compared with surface truth derived sea surface temperatures. Plots and associated statistics are presented for SEASAT-A SMMR data acquired for the Gulf of Alaska experiment. The cross-track gradients observed in the 6.6 GHz brightness temperature data are discussed.

Kitzis, S. N.↗

Improved VAS regression soundings of mesoscale temperature structure observed during the 1982 atmospheric variability experiment

An Atmospheric Variability Experiment (AVE) was conducted over the central U.S. in the spring of 1982, collecting radiosonde date to verify mesoscale soundings from the VISSR Atmospheric Sounder (VAS) on the GOES satellite. Previously published VAS/AVE comparisons for the 6 March 1982 case found that the satellite retrievals scarcely detected a low level temperature inversion or a mid-tropospheric cold pool over a special mesoscale radiosonde verification network in north central Texas. The previously published regression and physical retrieval algorithms did not fully utilize VAS' sensitivity to important subsynoptic thermal features. Therefore, the 6 March 1982 case was reprocessed adding two enhancements to the VAS regression retrieval algorithm: (1) the regression matrix was determined using AVE profile data obtained in the region at asynoptic times, and (2) more optimistic signal-to-noise statistical conditioning factors were applied to the VAS temperature sounding channels. The new VAS soundings resolve more of the low level temperature inversion and mid-level cold pool. Most of the improvements stems from the utilization of asynoptic radiosonde observations at NWS sites. This case suggests that VAS regression soundings may require a ground-based asynoptic profiler network to bridge the gap between the synoptic radiosonde network and the high resolution geosynchronous satellite observations during the day.

Chesters, Dennis↗

Characterizing multiscale variability of zero intermittency in spatial rainfall

In this paper the authors study how zero intermittency in spatial rainfall, as described by the fraction of area covered by rainfall, changes with spatial scale of rainfall measurement or representation. A statistical measure of intermittency that describes the size distribution of 'voids' (nonrainy areas imbedded inside rainy areas) as a function of scale is also introduced. Morphological algorithms are proposed for reconstructing rainfall intermittency at fine scales given the intermittency at coarser scales. These algorithms are envisioned to be useful in hydroclimatological studies where the rainfall spatial variability at the subgrid scale needs to be reconstructed from the results of synoptic- or mesoscale meteorological numerical models. The developed methodologies are demsonstrated and tested using data from a severe springtime midlatitude squall line and a mild midlatitude winter storm monitored by a meteorological radar in Norman, Oklahoma.

Kumar, Praveen↗

Load Balancing Using Time Series Analysis for Soft Real Time Systems with Statistically Periodic Loads

This thesis provides design and analysis of techniques for global load balancing on ensemble architectures running soft-real-time object-oriented applications with statistically periodic loads. It focuses on estimating the instantaneous average load over all the processing elements. The major contribution is the use of explicit stochastic process models for both the loading and the averaging itself. These models are exploited via statistical time-series analysis and Bayesian inference to provide improved average load estimates, and thus to facilitate global load balancing. This thesis explains the distributed algorithms used and provides some optimality results. It also describes the algorithms' implementation and gives performance results from simulation. These results show that the authors' techniques allow more accurate estimation of the global system loading, resulting in fewer object migrations than local methods. The authors' method is shown to provide superior performance, relative not only to static load-balancing schemes but also to many adaptive load-balancing methods. Results from a preliminary analysis of another system and from simulation with a synthetic load provide some evidence of more general applicability.

LOAD CONTROL↗

Load Balancing Using Time Series Analysis for Soft Real Time Systems with Statistically Periodic Loads

This thesis provides design and analysis of techniques for global load balancing on ensemble architectures running soft-real-time object-oriented applications with statistically periodic loads. It focuses on estimating the instantaneous average load over all the processing elements. The major contribution is the use of explicit stochastic process models for both the loading and the averaging itself. These models are exploited via statistical time-series analysis and Bayesian inference to provide improved average load estimates, and thus to facilitate global load balancing. This thesis explains the distributed algorithms used and provides some optimality results. It also describes the algorithms' implementation and gives performance results from simulation. These results show that our techniques allow more accurate estimation of the global system load ing, resulting in fewer object migration than local methods. Our method is shown to provide superior performance, relative not only to static load-balancing schemes but also to many adaptive methods.

REAL TIME↗

Gamma-Ray Burst Class Properties

Guided by the supervised pattern recognition algorithm C4.5 developed by Quinlan in 1986, we examine the three gamma-ray burst classes identified by Mukherjee et al. in 1998. C4.5 provides strong statistical support for this classification. However, with C4.5 and our knowledge of the Burst and Transient Source Experiment (BATSE) instrument, we demonstrate that class 3 (intermediate fluence, intermediate duration, soft) does not have to be a distinct source population: statistical/systematic errors in measuring burst attributes combined with the well-known hardness/intensity correlation can cause low peak flux class 1 (high fluence, long, intermediate hardness) bursts to take on class 3 characteristics naturally. Based on our hypothesis that the third class is not a distinct one, we provide rules so that future events can be placed in either class 1 or class 2 (low fluence, short, hard). We find that the two classes are relatively distinct on the basis of Band's work in 1993 on spectral parameters alpha, beta, and E (sub peak) alone. Although this does not indicate a better basis for classification, it does suggest that different physical conditions exist for class 1 and class 2 bursts. In the process of studying burst class characteristics, we identify a new bias affecting burst fluence and duration measurements. Using a simple model of how burst duration can be underestimated, we show how this fluence duration bias can affect BATSE measurements and demonstrate the type of effect it can have on the BATSE fluence versus peak flux diagram.

Hakkila, Jon↗

Linear Least Squares for Correlated Data

Throughout the literature authors have consistently discussed the suspicion that regression results were less than satisfactory when the independent variables were correlated. Camm, Gulledge, and Womer, and Womer and Marcotte provide excellent applied examples of these concerns. Many authors have obtained partial solutions for this problem as discussed by Womer and Marcotte and Wonnacott and Wonnacott, which result in generalized least squares algorithms to solve restrictive cases. This paper presents a simple but relatively general multivariate method for obtaining linear least squares coefficients which are free of the statistical distortion created by correlated independent variables.

Dean, Edwin B.↗

Unsupervised classification of Space Acceleration Measurement System (SAMS) data using ART2-A

The Space Acceleration Measurement System (SAMS) has been developed by NASA to monitor the microgravity acceleration environment aboard the space shuttle. The amount of data collected by a SAMS unit during a shuttle mission is in the several gigabytes range. Adaptive Resonance Theory 2-A (ART2-A), an unsupervised neural network, has been used to cluster these data and to develop cause and effect relationships among disturbances and the acceleration environment. Using input patterns formed on the basis of power spectral densities (psd), data collected from two missions, STS-050 and STS-057, have been clustered.

STS-57 Shuttle Project↗

Numerical experiments with model monophyletic and paraphyletic taxa

The problem of how accurately paraphyletic taxa versus monophyletic (i.e., holophyletic) groups (clades) capture underlying species patterns of diversity and extinction is explored with Monte Carlo simulations. Phylogenies are modeled as stochastic trees. Paraphyletic taxa are defined in an arbitrary manner by randomly choosing progenitors and clustering all descendants not belonging to other taxa. These taxa are then examined to determine which are clades, and the remaining paraphyletic groups are dissected to discover monophyletic subgroups. Comparisons of diversity patterns and extinction rates between modeled taxa and lineages indicate that paraphyletic groups can adequately capture lineage information under a variety of conditions of diversification and mass extinction. This suggests that these groups constitute more than mere "taxonomic noise" in this context. But, strictly monophyletic groups perform somewhat better, especially with regard to mass extinctions. However, when low levels of paleontologic sampling are simulated, the veracity of clades deteriorates, especially with respect to diversity, and modeled paraphyletic taxa often capture more information about underlying lineages. Thus, for studies of diversity and taxic evolution in the fossil record, traditional paleontologic genera and families need not be rejected in favor of cladistically-defined taxa.

Non-NASA Center↗

Assessment of the Impact of Discontinuity in Satellite Instruments and Retrievals on Global PM2.5

Exposure to fine particulate matter (PM2.5) is the leading environmental risk factor for mortality globally. Satellite-derived estimates of surface PM2.5 developed from a combination of satellites, simulations, and ground monitor data are relied upon for health impact studies. The ability to develop satellite-derived PM2.5 estimates requires the continued availability of aerosol optical depth (AOD) sources. This work examines the impact of the addition or loss of satellite AOD data sources on global PM2.5 estimation and the impact of continuing the longterm record with AOD from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi-National Polar orbiting Partnership (S-NPP) satellite after the loss of the MODIS (MODerate resolution Imaging Spectroradiometer) and MISR (Multi-angle Imaging Spectroradiometer) instruments on board the Terra and Aqua satellites. We find that the addition of VIIRS S-NPP AOD products to geophysical PM2.5 estimates from satellites and simulations causes regional differences that correspond to differences in the VIIRS and MODIS Deep Blue AOD algorithms and sampling. Changes in long-term trends and timeseries due to the addition or loss of AOD data sources are generally within their uncertainties. Statistical fusion with ground monitor data partially corrects for changes due to sampling differences when introducing the VIIRS AOD products, but uncertainty remains over desert regions where ground monitor coverage is sparse. This work provides promise for the sustained development of global satellite-derived PM2.5 estimates, despite discontinuities in instruments and retrieval methods.

Remote sensing↗

A statistical correlation method for the retrieval of atmospheric moisture profiles by microwave radiometry

A statistical correlation technique is applied to the retrieval of vertical moisture profiles under clear-sky conditions from down-looking radiometric measurements of atmospheric radiation at microwave wavelengths. For a given set of channels, the method selects the optimum radiometric channels for estimating water vapor at specific pressure levels between the surface and 300 mb. The water vapor mixing ratio at these pressure levels is then calculated from a linear combination of the selected channel brightness temperatures. To test its validity the algorithm was applied, in a numerical experiment, to fifty independent tropical radiosondes. The rms absolute deviation of the estimated moisture profiles from the actual profiles was comparable to that obtained using an iterative retrieval method reported earlier. The statistical method, however, requires several orders of magnitude less computer time than the iterative method; it is suitable for high speed processing of large amounts of data.

Kakar, R. K.↗

A Statistical Method for Reducing Sidelobe Clutter for the Ku-Band Precipitation Radar on Board the GPM Core Observatory

A statistical method to reduce the sidelobe clutter of the Ku-band precipitation radar (KuPR) of the Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) Core Observatory is described and evaluated using DPR observations. The KuPR sidelobe clutter was much more severe than that of the Precipitation Radar on board the Tropical Rainfall Measuring Mission (TRMM), and it has caused the misidentification of precipitation. The statistical method to reduce sidelobe clutter was constructed by subtracting the estimated sidelobe power, based upon a multiple regression model with explanatory variables of the normalized radar cross section (NRCS) of surface, from the received power of the echo. The saturation of the NRCS at near-nadir angles, resulting from strong surface scattering, was considered in the calculation of the regression coefficients.The method was implemented in the KuPR algorithm and applied to KuPR-observed data. It was found that the received power from sidelobe clutter over the ocean was largely reduced by using the developed method, although some of the received power from the sidelobe clutter still remained. From the statistical results of the evaluations, it was shown that the number of KuPR precipitation events in the clutter region, after the method was applied, was comparable to that in the clutter-free region. This confirms the reasonable performance of the method in removing sidelobe clutter. For further improving the effectiveness of the method, it is necessary to improve the consideration of the NRCS saturation, which will be explored in future work.

Atm/Ocean Structure/ Phenomena; Precipitation; Obs↗

Statistical Studies on Thin Cirrus from MODIS Data

The 1.38 micron channel on the MODerate resolution Imaging Spectroradiomater (MODIS) is an ideal channel to identify and quantify thin cirrus on a global basis. This channel is used to produce the cirrus reflectance product in MOD06 and also used extensively by the MODIS aerosol algorithms to mask clouds for the MOD04 product. The aerosol product uses a lower threshold of the 1.38 micron channel reflectance of 0.01. A cirrus channel reflectance of 0.01 corresponds to approximately an aerosol optical thickness of 0.10. Therefore, the ambiguity due to the minor cirrus contamination may introduce artificial optical thickness in the aerosol products. The questions arise: How prevalent are the thinnest cirrus clouds over the globe? Do they persist over specific regions and seasons? Can we distinguish between the noise of the channel and the actual cloudiness by extrapolating the cloudiness signal to very dark scenes, statistically. We analyze the Terra data, over land and ocean to answer these questions.

Li, Rong-Rong↗

A new technique for enlargement and reconstruction of digital sensor imagery

A new image enlargement and reconstruction routine, the digital image enlarging balanced reconstruction algorithm (DIEBRA), is described. Using a highly modified form of 'balanced' two-dimensional polynomial interpolation, this program enlarges digital imagery, creating synthetic high spatial resolution images. Statistical analysis shows the DIEBRA-generated imagery to be significantly closer to true high spatial resolution imagery at all frequencies than imagery generated by a cubic convolutional reconstruction filter.

Rhea, W. Joseph↗

Application of the multiple PRF technique to resolve Doppler centroid estimation ambiguity for spaceborne SAR

Estimation of the Doppler centroid ambiguity is a necessary element of the signal processing for SAR systems with large antenna pointing errors. Without proper resolution of the Doppler centroid estimation (DCE) ambiguity, the image quality will be degraded in the system impulse response function and the geometric fidelity. Two techniques for resolution of DCE ambiguity for the spaceborne SAR are presented; they include a brief review of the range cross-correlation technique and presentation of a new technique using multiple pulse repetition frequencies (PRFs). For SAR systems, where other performance factors control selection of the PRF's, an algorithm is devised to resolve the ambiguity that uses PRF's of arbitrary numerical values. The performance of this multiple PRF technique is analyzed based on a statistical error model. An example is presented that demonstrates for the Shuttle Imaging Radar-C (SIR-C) C-band SAR, the probability of correct ambiguity resolution is higher than 95 percent for antenna attitude errors as large as 3 deg.

Chang, C. Y.↗

Medical Image Compression Using a New Subband Coding Method

A recently introduced iterative complexity- and entropy-constrained subband quantization design algorithm is generalized and applied to medical image compression. In particular, the corresponding subband coder is used to encode Computed Tomography (CT) axial slice head images, where statistical dependencies between neighboring image subbands are exploited. Inter-slice conditioning is also employed for further improvements in compression performance. The subband coder features many advantages such as relatively low complexity and operation over a very wide range of bit rates. Experimental results demonstrate that the performance of the new subband coder is relatively good, both objectively and subjectively.

Kossentini, Faouzi↗

Automated navigation assessment for earth survey sensors using island targets

An automated method has been developed for performing navigation assessment on satellite-based Earth sensor data. The method utilizes islands as targets which can be readily located in the sensor data and identified with reference locations. The essential elements are an algorithm for classifying the sensor data according to source, a reference catalog of island locations, and a robust pattern-matching algorithm for island identification. The algorithms were developed and tested for the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), an ocean color sensor. This method will allow navigation error statistics to be automatically generated for large numbers of points, supporting analysis over large spatial and temporal ranges.

Patt, Frederick S.↗

An Automated Method for Navigation Assessment for Earth Survey Sensors Using Island Targets

An automated method has been developed for performing navigation assessment on satellite-based Earth sensor data. The method utilizes islands as targets which can be readily located in the sensor data and identified with reference locations. The essential elements are an algorithm for classifying the sensor data according to source, a reference catalogue of island locations, and a robust pattern-matching algorithm for island identification. The algorithms were developed and tested for the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), an ocean colour sensor. This method will allow navigation error statistics to be automatically generated for large numbers of points, supporting analysis over large spatial and temporal ranges.

Patt, F. S.↗