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Atmospheric radiation calculations essential part of climate models and inversion techniques for analyzing remote sensing observations. Two spectral mapping transformation methods developed to reduce computing time and storage requirements greatly while preserving accuracy achievable with rigorous methods. Evolved from "correlated-k" (c-k) method.
The power spectra of many geophysical phenomena are well approximated by a power-law dependence on frequency or wavenumber. A simple expression for the root-mean-square variability of a process with such a spectrum over an interval of time or space is derived. The resulting expression yields the powerlaw time dependence characteristic of fractal processes, but can be generalized to give the temporal variability for more general spectral behaviors. The method is applied to spectra of crustal strain (to show what size of strain events can be detected over periods of months to seconds) and of sea level (to show the difficulty of extracting long-term rates from short records).
Flow transitions in a Two Dimensional (2D) model of crystal growth were examined using the Bridgman-Stockbarger me thod. Using a pseudo-spectral Chebyshev collocation method, the governing equations yield solutions which exhibit a symmetry breaking flow tansition and oscillatory behavior indicative of a Hopf bifurcation at higher values of Ra. The results are discussed from fluid dynamic viewpoint, and broader implications for process models are also addressed.
Field techniques for estimating vegetative biomass are labor intensive, and rarely are used to monitor changes in biomass over time. Remote-sensing offers an attractive alternative to field measurements; however, because there is no simple correspondence between encoded radiance in multispectral images and biomass, it is not possible to measure vegetative biomass directly from AVIRIS images. Ways to estimate vegetative biomass by identifying community types and then applying biomass scalars derived from field measurements are investigated. Field measurements of community-scale vegetative biomass can be made, at least for local areas, but it is not always possible to identify vegetation communities unambiguously using remote measurements and conventional image-processing techniques. Furthermore, even when communities are well characterized in a single image, it typically is difficult to assess the extent and nature of changes in a time series of images, owing to uncertainties introduced by variations in illumination geometry, atmospheric attenuation, and instrumental responses. Our objective is to develop an improved method based on spectral mixture analysis to characterize and identify vegetative communities, that can be applied to multi-temporal AVIRIS and other types of images. In previous studies, multi-temporal data sets (AVIRIS and TM) of Owens Valley, CA were analyzed and vegetation communities were defined in terms of fractions of reference (laboratory and field) endmember spectra. An advantage of converting an image to fractions of reference endmembers is that, although fractions in a given pixel may vary from image to image in a time series, the endmembers themselves typically are constant, thus providing a consistent frame of reference.
The success of imaging spectrometry in mineralogic mapping of natural terrains indicates that the technology can also be used to assess the environmental impact of human activities in certain instances. Specifically, this paper describes an investigation into the use of data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) for mapping the spread of, and assessing changes in, the mineralogic character of tailings from a major silver and base metal mining district. The area under investigation is the Coeur d'Alene River Valley in northern Idaho. Mining has been going on in and around the towns of Kellogg and Wallace, Idaho since the 1880's. In the Kellogg-Smelterville Flats area, west of Kellogg, mine tailings were piled alongside the South Fork of the Coeur d'Alene River. Until the construction of tailings ponds in 1968 much of these waste materials were washed directly into the South Fork. The Kellogg-Smelterville area was declared an Environmental Protection Agency (EPA) Superfund site in 1983 and remediation efforts are currently underway. Recent studies have demonstrated that sediments in the Coeur d'Alene River and in the northern part of Lake Coeur d'Alene, into which the river flows, are highly enriched in Ag, Cu, Pb, Zn, Cd, Hg, As, and Sb. These trace metals have become aggregated in iron oxide and oxyhydroxide minerals and/or mineraloids. Reflectance spectra of iron-rich tailing materials are shown. Also shown are spectra of hematite and goethite. The broad bandwidth and long band center (near 1 micron) of the Fe(3+) crystal-field band of the iron-rich sediment samples combined with the lack of features on the Fe(3+) -O(2-) charge transfer absorption edge indicates that the ferric oxide and/or oxyhydroxide in these sediments is poorly crystalline to amorphous in character. Similar features are seen in poorly crystalline basaltic weathering products (e.g., palagonites). The problem of mapping and analyzing the downriver occurrences of iron rich tailings in the Coeur d'Alene (CDA) River Valley using remotely sensed data is complicated by the full vegetation cover present in the area. Because exposures of rock and soil were sparse, the data processing techniques used in this study were sensitive to detecting materials at subpixel scales. The methods used included spectral mixture analysis and a constrained energy minimization technique.
We investigate the low-mass population of the young cluster IC348 down to the deuterium-burning limit, a fiducial boundary between brown dwarf and planetary mass objects, using a new and innovative method for the spectral classification of late-type objects. Using photometric indices, constructed from HST/NICMOS narrow-band imaging, that measure the strength of the 1.9 micron water band, we determine the spectral type and reddening for every M-type star in the field, thereby separating cluster members from the interloper population. Due to the efficiency of our spectral classification technique, our study is complete from approximately 0.7 solar mass to 0.015 solar mass. The mass function derived for the cluster in this interval, dN/d log M alpha M(sup 0.5), is similar to that obtained for the Pleiades, but appears significantly more abundant in brown dwarfs than the mass function for companions to nearby sun-like stars. This provides compelling observational evidence for different formation and evolutionary histories for substellar objects formed in isolation vs. as companions. Because our determination of the IMF is complete to very low masses, we can place interesting constraints on the role of physical processes such as fragmentation in the star and planet formation process and the fraction of dark matter in the Galactic halo that resides in substellar objects.
A brief description of the newly developed Empirical Mode Decomposition (ENID) and Hilbert Spectral Analysis (HSA) method will be given. The decomposition is adaptive and can be applied to both nonlinear and nonstationary data. Example of the method applied to a sample earthquake record will be given. The results indicate those low frequency components, totally missed by the Fourier analysis, are clearly identified by the new method. Comparisons with Wavelet and window Fourier analysis show the new method offers much better temporal and frequency resolutions.
Dr. S. N. Zhang has lead a seven member group (Dr. Yuxin Feng, Mr. XuejunSun, Mr. Yongzhong Chen, Mr. Jun Lin, Mr. Yangsen Yao, and Ms. Xiaoling Zhang). This group has carried out the following activities: continued data analysis from space astrophysical missions CGRO, RXTE, ASCA and Chandra. Significant scientific results have been produced as results of their work. They discovered the three-layered accretion disk structure around black holes in X-ray binaries; their paper on this discovery is to appear in the prestigious Science magazine. They have also developed a new method for energy spectral analysis of black hole X-ray binaries; four papers on this topics were presented at the most recent Atlanta AAS meeting. They have also carried Monte-Carlo simulations of X-ray detectors, in support to the hardware development efforts at Marshall Space Flight Center (MSFC). These computation-intensive simulations have been carried out entirely on the computers at UAH. They have also carried out extensive simulations for astrophysical applications, taking advantage of the Monte-Carlo simulation codes developed previously at MSFC and further improved at UAH for detector simulations. One refereed paper and one contribution to conference proceedings have been resulted from this effort.
Magnetospheric Multiscale (MMS) mission will study small-scale reconnection structures and their rapid motions from closely spaced platforms using instruments capable of high angular, energy, and time resolution measurements. The Dual Electron Spectrometer (DES) of the Fast Plasma Instrument (FPI) for MMS meets these demanding requirements by acquiring the electron velocity distribution functions (VDFs) for the full sky with high-resolution angular measurements every 30 ms. This will provide unprecedented access to electron scale dynamics within the reconnection diffusion region. The DES consists of eight half-top-hat energy analyzers. Each analyzer has a 6 deg. x 11.25 deg. Full-sky coverage is achieved by electrostatically stepping the FOV of each of the eight sensors through four discrete deflection look directions. Data compression and burst memory management will provide approximately 30 minutes of high time resolution data during each orbit of the four MMS spacecraft. Each spacecraft will intelligently downlink the data sequences that contain the greatest amount of temporal structure. Here we present the results of a simulation of the DES analyzer measurements, data compression and decompression, as well as ground-based analysis using as a seed re-processed Cluster/PEACE electron measurements. The Cluster/PEACE electron measurements have been reprocessed through virtual DES analyzers with their proper geometrical, energy, and timing scale factors and re-mapped via interpolation to the DES angular and energy phase-space sampling measurements. The results of the simulated DES measurements are analyzed and the full moments of the simulated VDFs are compared with those obtained from the Cluster/PEACE spectrometer using a standard quadrature moment, a newly implemented spectral spherical harmonic method, and a singular value decomposition method. Our preliminary moment calculations show a remarkable agreement within the uncertainties of the measurements, with the results obtained by the Cluster/PEACE electron spectrometers. The data analyzed was selected because it represented a potential reconnection event as currently published.
A multiple-bit differential-detection method has been proposed for the reception of radio signals modulated with offset quadrature phase-shift keying (offset QPSK or OQPSK). The method is also applicable to other spectrally efficient offset quadrature modulations. This method is based partly on the same principles as those of a multiple-symbol differential-detection method for M-ary QPSK, which includes QPSK (that is, non-offset QPSK) as a special case. That method was introduced more than a decade ago by the author of the present method as a means of improving performance relative to a traditional (two-symbol observation) differential-detection scheme. Instead of symbol-by-symbol detection, both that method and the present one are based on a concept of maximum-likelihood sequence estimation (MLSE). As applied to the modulations in question, MLSE involves consideration of (1) all possible binary data sequences that could have been received during an observation time of some number, N, of symbol periods and (2) selection of the sequence that yields the best match to the noise-corrupted signal received during that time. The performance of the prior method was shown to range from that of traditional differential detection for short observation times (small N) to that of ideal coherent detection (with differential encoding) for long observation times (large N).
We present a measurement of the angular power spectrum of the cosmic microwave background (CMB) radiation observed at 148 GHz. The measurement uses maps with 1.4' angular resolution made with data from the Atacama Cosmology Telescope (ACT). The observations cover 228 deg(sup 2) of the southern sky, in a 4 deg. 2-wide strip centered on declination 53 deg. South. The CMB at arc minute angular scales is particularly sensitive to the Silk damping scale, to the Sunyaev-Zel'dovich (SZ) effect from galaxy dusters, and to emission by radio sources and dusty galaxies. After masking the 108 brightest point sources in our maps, we estimate the power spectrum between 600 less than l less than 8000 using the adaptive multi-taper method to minimize spectral leakage and maximize use of the full data set. Our absolute calibration is based on observations of Uranus. To verify the calibration and test the fidelity of our map at large angular scales, we cross-correlate the ACT map to the WMAP map and recover the WMAP power spectrum from 250 less than l less than 1150. The power beyond the Silk damping tail of the CMB (l approximately 5000) is consistent with models of the emission from point sources. We quantify the contribution of SZ clusters to the power spectrum by fitting to a model normalized to sigma 8 = 0.8. We constrain the model's amplitude A(sub sz) less than 1.63 (95% CL). If interpreted as a measurement of as, this implies sigma (sup SZ) (sub 8) less than 0.86 (95% CL) given our SZ model. A fit of ACT and WMAP five-year data jointly to a 6-parameter ACDM model plus point sources and the SZ effect is consistent with these results.
Norden E. Huang et al. had proposed and published the Hilbert-Huang Transform (HHT) concept correspondently in 1996, 1998. The HHT is a novel method for adaptive spectral analysis of non-linear and non-stationary signals. The HHT comprises two components: - the Huang Empirical Mode Decomposition (EMD), resulting in an adaptive data-derived basis of Intrinsic Mode functions (IMFs), and the Hilbert Spectral Analysis (HSA1) based on the Hilbert Transform for 1-dimension (1D) applied to the EMD IMF's outcome. Although paper describes the HHT concept in great depth, it does not contain all needed methodology to implement the HHT computer code. In 2004, Semion Kizhner and Karin Blank implemented the reference digital HHT real-time data processing system for 1D (HHT-DPS Version 1.4). The case for 2-Dimension (2D) (HHT2) proved to be difficult due to the computational complexity of EMD for 2D (EMD2) and absence of a suitable Hilbert Transform for 2D spectral analysis (HSA2). The real-time EMD2 and HSA2 comprise the real-time HHT2. Kizhner completed the real-time EMD2 and the HSA2 reference digital implementations respectively in 2013 & 2014. Still, the HHT2 outcome synthesis remains an active research area. This paper presents the initial concepts and preliminary results of HHT2-based synthesis and its application to processing of signals contaminated by Radio-Frequency Interference (RFI), as well as optical systems' fringe detection and mitigation at design stage. The Soil Moisture Active Passive (SMAP mission (SMAP) carries a radiometer instrument that measures Earth soil moisture at L1 frequency (1.4 GHz polarimetric - H, V, 3rd and 4th Stokes parameters). There is abundant RFI at L1 and because soil moisture is a strategic parameter, it is important to be able to recover the RFI-contaminated measurement samples (15% of telemetry). State-of-the-art only allows RFI detection and removes RFI-contaminated measurements. The HHT-based analysis and synthesis facilitates recovery of measurements contaminated by all kinds of RFI, including jamming [7-8]. The fringes are inherent in optical systems and multi-layer complex contour expensive coatings are employed to remove the unwanted fringes. HHT2-based analysis allows test image decomposition to analyze and detect fringes, and HHT2-based synthesis of useful image.
The objective of this presentation is to give a brief overview of the theory behind the (DBA) method, an overview of the derivation and a practical application of the theory using the Python computer language. The Theory and Derivation will use both Acceleration and Pseudo Velocity methods to derive a series of equations for processing by Python. We will take the results and compare both Acceleration and Pseudo Velocity methods and discuss implementation of the Python functions. Also, we will discuss the efficiency of the methods and the amount of computer time required for the solution. In conclusion, (DBA) offers a powerful method to evaluate the amount of energy imparted into a system in the form of both Amplitude and Duration during qualification testing and flight environments. Many forms of steady state and transient vibratory motion can be characterized using this technique. (DBA) provides a more robust alternative to traditional methods such Power Spectral Density (PSD) using a Maximax approach.
The traditional method of applying a digital optimal filter to measure X-ray pulses from transition-edge sensor (TES) devices does not achieve the best energy resolution when the signals have a highly non-linear response to energy, or the noise is non-stationary during the pulse. We present an implementation of a method to analyze X-ray data from TESs, which is based upon principal component analysis (PCA). Our method separates the X-ray signal pulse into orthogonal components that have the largest variance. We typically recover pulse height, arrival time, differences in pulse shape, and the variation of pulse height with detector temperature. These components can then be combined to form a representation of pulse energy. An added value of this method is that by reporting information on more descriptive parameters (as opposed to a single number representing energy), we generate a much more complete picture of the pulse received. Here we report on progress in developing this technique for future implementation on X-ray telescopes. We used an 55Fe source to characterize Mo/Au TESs. On the same dataset, the PCA method recovers a spectral resolution that is better by a factor of two than achievable with digital optimal filters.
The objective of this presentation is to give a brief overview of the theory behind the (DBA) method, an overview of the derivation and a practical application of the theory using the Python computer language. The Theory and Derivation will use both Acceleration and Pseudo Velocity methods to derive a series of equations for processing by Python. We will take the results and compare both Acceleration and Pseudo Velocity methods and discuss implementation of the Python functions. Also, we will discuss the efficiency of the methods and the amount of computer time required for the solution. In conclusion, (DBA) offers a powerful method to evaluate the amount of energy imparted into a system in the form of both Amplitude and Duration during qualification testing and flight environments. Many forms of steady state and transient vibratory motion can be characterized using this technique. (DBA) provides a more robust alternative to traditional methods such Power Spectral Density (PSD) using a maximax approach.
The paper presents a time-domain method for computation of sound radiation from aircraft engine sources to the far-field. The effects of nonuniform flow around the aircraft and scattering of sound by fuselage and wings are accounted for in the formulation. Our approach is based on the discretization of the inviscid flow equations through a collocation form of the Discontinuous Galerkin spectral element method. An isoparametric representation of the underlying geometry is used in order to take full advantage of the spectral accuracy of the method. Largescale computations are made possible by a parallel implementation based on message passing. Results obtained for radiation from an axisymmetric nacelle alone are compared with those obtained when the same nacelle is installed in a generic con.guration, with and without a wing.
NASA's Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder (CPF) mission will deploy an Earth-observing reflected solar (RS) spectrometer, designed to measure Earth-reflected solar radiation from the International Space Station with a remarkable SI-traceable radiometric uncertainty of 0.3% (k=1). This spectrometer, known as the Hyperspectral Imager for Climate Science (HySICS), will provide measurements within a spectral range of 350-2300 nm with 3-nm spectral intervals. Covering a nadir swath of 70 km, HySICS captures 480 discrete measurement pixels that provide spectrally-resolved Earth-reflected radiances. The CPF mission encompasses two principal objectives. The first objective is to demonstrate on-orbit calibration methodologies that achieve and uphold an unprecedented level of accuracy while maintaining traceability to SI standards. The second objective is to showcase an innovative on-orbit intercalibration approach, which involves the intercalibration of two other RS sensors—namely, the shortwave (SW) channel of the Clouds and the Earth’s Radiant Energy System (CERES) and the Reflective Solar (RS) bands of the Visible Infrared Imager Radiometer Suite (VIIRS)—against CPF benchmark measurements. The targeted intercalibration methodology uncertainty for these target instruments is 0.3% (k=1). Empowered by the CPF payload's two-axis pointing capability, moderate spatial sampling of 0.5 km, and wide spectral coverage, the CPF instrument will capture near-simultaneous temporal, spatial, angular, and spectrally matched observations with intercalibration targets. The CPF intercalibration science development team has devised novel methods to address spatial, spectral, polarization, and angular differences between CPF and the target instruments' intercalibration footprints to achieve the stringent 0.3% intercalibration methodology uncertainty. Comprehensive details of these methods and their validation will be elaborated upon during the conference presentation.
Several critical aspects may influence the analysis of the relationship between the solar wind (SW) and magnetospheric fluctuations, for example, the characteristics and frequency of SW fluctuations that are expected to impinge the magnetosphere may not be the same when they are observed by spacecraft located at different places in front of the magnetosphere; similarly, the choice of analytical methods adopted for the spectral analysis might influence the frequency estimate (as well as the wave identification itself) both in the SW and magnetosphere. Focusing our attention on these aspects, we present an analysis of SW compressional fluctuations (f ≈ 1–5 mHz), following two interplanetary shocks observed by two interplanetary spacecraft, regarded as two different situations in terms of spacecraft separation and distance from the magnetosphere. Our results show that some differences in the characteristics of SW fluctuations emerge when the same stream is observed at different places and confirm the critical role of analytical methods in determining fluctuation characteristics. We compared aspects of SW fluctuations with those of magnetospheric fluctuations following the sudden impulses due to the impact of interplanetary shocks. For this scope, we examined observations by two satellites at geostationary orbit and at several ground-based stations. We found that the magnetospheric fluctuations were related to compressional SW fluctuations approximately at the same frequencies, with no evidence for wave activity of internal origin or directly driven by the shock impact.