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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.

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NASA NTRS2011

The EVE Doppler Sensitivity and Flare Observations

The Extreme-ultraviolet Variability Experiment (EVE) obtains continuous EUV spectra of the Sun viewed as a star. Its primary objective is the characterization of solar spectral irradiance, but its sensitivity and stability make it extremely interesting for observations of variability on time scales down to the limit imposed by its basic 10 s sample interval. In this paper we characterize the Doppler sensitivity of the EVE data. We find that the 30.4 nm line of He II has a random Doppler error below 0.001 nm (1 pm, better than 10 km/s as a redshift), with ample stability to detect the orbital motion of its satellite, the Solar Dynamics Observatory (SDO). Solar flares also displace the spectrum, both because of Doppler shifts and because of EVE's optical layout, which (as with a slitless spectrograph) confuses position and wavelength. As a flare develops, the centroid of the line displays variations that reflect Doppler shifts and therefore flare dynamics. For the impulsive phase of the flare SOL2010-06-12, we find the line centroid to have a redshift of 16.8 +/- 5.9 km/s relative to that of the flare gradual phase (statistical errors only). We find also that high-temperature lines, such as Fe XXIV 19.2 nm, have well-determined Doppler components for major flares, with decreasing apparent blueshifts as expected from chromospheric evaporation flows.

Hudson, H. S.↗
NASA NTRS2021

Assimilation of Lidar Planetary Boundary Layer Height Observations

Lidar backscatter and wind retrievals of the planetary boundary layer height (PBLH) are assimilated into 22 hourly forecasts from the NASA Unified - Weather and Research Forecast (NU-WRF) model during the Plains Elevated Convection Convection at Night (PECAN) campaign on July 11, 2015 in Greensburg, Kansas, using error statistics collected from the model profiles to compute the necessary covariance matrices. Two separate forecast runs using different PBL physics schemes were employed, and comparisons with 6 independent radiosonde profiles were made for each run. Both of the forecast runs accurately predicted the PBLH and the state variable profiles within the planetary boundary layer during the early morning, and the assimilation had a small impact during this time. In the late afternoon, the forecast runs showed decreased accuracy as the convective boundary layer developed. However, assimilation of the Doppler lidar PBLH observations were found to improve the temperature and V velocity profiles relative to independent radiosonde profiles. Water vapor was overcorrected, leading to increased differences with independent data. Errors in the U velocity were made slightly larger. The computed forecast error covariances between the PBLH and state variables were found to rise in the late afternoon, leading to the larger improvements in the afternoon. This work represents the first effort to assimilate PBLH into forecast states using ensemble methods.

Andrew Tangborn↗
NASA NTRS2004

Multivariate Error Covariance Estimates by Monte-Carlo Simulation for Assimilation Studies in the Pacific Ocean

One of the most difficult aspects of ocean state estimation is the prescription of the model forecast error covariances. The paucity of ocean observations limits our ability to estimate the covariance structures from model-observation differences. In most practical applications, simple covariances are usually prescribed. Rarely are cross-covariances between different model variables used. Here a comparison is made between a univariate Optimal Interpolation (UOI) scheme and a multivariate OI algorithm (MvOI) in the assimilation of ocean temperature. In the UOI case only temperature is updated using a Gaussian covariance function and in the MvOI salinity, zonal and meridional velocities as well as temperature, are updated using an empirically estimated multivariate covariance matrix. Earlier studies have shown that a univariate OI has a detrimental effect on the salinity and velocity fields of the model. Apparently, in a sequential framework it is important to analyze temperature and salinity together. For the MvOI an estimation of the model error statistics is made by Monte-Carlo techniques from an ensemble of model integrations. An important advantage of using an ensemble of ocean states is that it provides a natural way to estimate cross-covariances between the fields of different physical variables constituting the model state vector, at the same time incorporating the model's dynamical and thermodynamical constraints as well as the effects of physical boundaries. Only temperature observations from the Tropical Atmosphere-Ocean array have been assimilated in this study. In order to investigate the efficacy of the multivariate scheme two data assimilation experiments are validated with a large independent set of recently published subsurface observations of salinity, zonal velocity and temperature. For reference, a third control run with no data assimilation is used to check how the data assimilation affects systematic model errors. While the performance of the UOI and MvOI is similar with respect to the temperature field, the salinity and velocity fields are greatly improved when multivariate correction is used, as evident from the analyses of the rms differences of these fields and independent observations. The MvOI assimilation is found to improve upon the control run in generating the water masses with properties close to the observed, while the UOI failed to maintain the temperature and salinity structure.

Borovikov, Anna↗
NASA NTRS2008

Precision Measurement of Black Hole Binary Dynamics: Analyzing the LISA Data Stream

One of the richest potential sources of insight into fundamental physics that LISA will be capable of observing is the inspiral of supermassive black hole binaries (BHBs). However, the data analysis challenge presented by the LISA data stream is quite unlike the situation for present day gravitational wave detectors. In order to make the precision measurements necessary to achieve LISA's science goals, the BHB signal must be distinguished from a data stream that not only contains instrumental noise, but potentially thousands of other signals as well, so that the "background" we wish to separate out to focus on the BHB signal is likely to be highly nonstationary and nongaussian, as well as being of scientific interest in its own right. In addition, whereas the theoretical templates that we calculate in order to ultimately estimate the parameters can afford to be somewhat inaccurate and still be effective for present day and near future detectors, this is not the case for LISA, and extremely high fidelity of the theoretical templates for high signal-to-noise signals will be required to prevent theoretical errors from dominating the parameter estimates. NVe, will describe efforts in the community of LISA data analysts to address the challenges regarding the specific issue of BHB signals. These efforts include using a Markov Chain Monte Carlo approach with the freedom to model the BHB and the other signals present in the data stream simultaneously, rather than trying to remove other signals and risk biasing the remaining data. The Mock LISA Data Challenge is a community of LISA scientists who generate rounds of simulated LISA noise with increasingly difficult signal content, and invite the LISA data analysis community to exercise their methods, or develop new methods, in an attempt to extract the parameters for the signals embedded in the mock data. In addition to practical approaches such ,is this to assess the level of parameter accuracy, one can apply the Fisher matrix formalism to assess both the statistical errors from noise and the theoretical errors

McWilliams, Sean T.↗
NASA NTRS2014

Background Error Covariance Estimation Using Information from a Single Model Trajectory with Application to Ocean Data Assimilation

An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory.SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

Error Covariance↗
NASA NTRS2014

Background Error Covariance Estimation using Information from a Single Model Trajectory with Application to Ocean Data Assimilation into the GEOS-5 Coupled Model

An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory. SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

Data Assimilation↗
NASA NTRS2001

Bias Correction and Random Error Characterization for the Assimilation of HRDI Line-of-Sight Wind Measurements

A new approach to the analysis of systematic and random observation errors is presented in which the error statistics are obtained using forecast data rather than observations from a different instrument type. The analysis is carried out at an intermediate retrieval level, instead of the more typical state variable space. This method is carried out on measurements made by the High Resolution Doppler Imager (HRDI) on board the Upper Atmosphere Research Satellite (UARS). HRDI, a limb sounder, is the only satellite instrument measuring winds in the stratosphere, and the only instrument of any kind making global wind measurements in the upper atmosphere. HRDI measures doppler shifts in the two different O2 absorption bands (alpha and B) and the retrieved products are tangent point Line-of-Sight wind component (level 2 retrieval) and UV winds (level 3 retrieval). This analysis is carried out on a level 1.9 retrieval, in which the contributions from different points along the line-of-sight have not been removed. Biases are calculated from O-F (observed minus forecast) LOS wind components and are separated into a measurement parameter space consisting of 16 different values. The bias dependence on these parameters (plus an altitude dependence) is used to create a bias correction scheme carried out on the level 1.9 retrieval. The random error component is analyzed by separating the gamma and B band observations and locating observation pairs where both bands are very nearly looking at the same location at the same time. It is shown that the two observation streams are uncorrelated and that this allows the forecast error variance to be estimated. The bias correction is found to cut the effective observation error variance in half.

Tangborn, Andrew↗
NASA NTRS1972

Search for carbon 14 in tektites.

An attempt was made to detect cosmic-ray produced C-14 in australites and far-east tektites. No C-14 activity was found outside of statistical fluctuations. If a terrestrial age of 10 thousand years is assumed for australites, a maximum cosmic-ray exposure time of one thousand years is obtained for these tektites on the basis of a 2-sigma statistical error.

Boeckl, R. S.↗
NASA NTRS1972

Photographic and photometric enhancement of Lunar Orbiter products, projects A, B and C

A detailed discussion is presented of the framelet joining, photometric data improvement, and statistical error analysis. The Lunar Orbiter film handling system, readout system, and the digitization are described, along with the technique of joining adjacent framelets by a using a digital computer. Time and cost estimates are given. The problems and techniques involved in improving the digitized data are discussed. It was found that spectacular improvements are possible. Program documentations are included.

Source record↗
NASA NTRS1974

Refractive index of He in the region 920-1910 A

The refractive index of He has been determined in the region 920-1910 A by measurements of wavelength shifts in a 3-m spectrograph alternately filled with He and evacuated. Differential pumping systems were used to allow operation of the light source at conveniently low pressures. Several plates were measured and analyzed in order to reduce statistical errors. The results at 919 A agree with the theory within 1%, i.e., less than the experimental error.

Huber, M. C. E.↗
NASA NTRS1975

The design of a digital voice data compression technique for orbiter voice channels

Voice bandwidth compression techniques were investigated to anticipate link margin difficulties in the shuttle S-band communication system. It was felt that by reducing the data rate on each voice channel from the baseline 24 (or 32) Kbps to 8 Kbps, additional margin could be obtained. The feasibility of such an alternate voice transmission system was studied. Several factors of prime importance that were addressed are: (1) achieving high quality voice at 8 Kbps; (2) performance in the presence of the anticipated shuttle cabin environmental noise; (3) performance in the presence of the anticipated channel error statistics; and (4) minimal increase in size, weight, and power over the current baseline voice processor.

Source record↗
NASA NTRS1975

A two-parameter scintillation spectrometer system for measurement of secondary proton, deuteron, and triton distributions from materials under 558-MeV-proton irradiation

A two-parameter scintillation spectrometer system developed and used to obtain proton, deuteron, and triton double differential cross sections from materials under 558-MeV-proton irradiation is described. The system measures both the time of flight of secondary particles over a 488-cm flight path and the energy deposited in a scintillator, 12.7 cm in diameter and 30.48 cm long. The time resolution of the system is 0.39 nsec. The calculated energy resolution based on this time resolution varies with energy from 1.6 precent to 7.75 percent for 50- and 558-MeV protons. Various systematic and statistical errors are evaluated, and the double differential cross sections for secondary proton and deutron production at 20 deg from a 2.35 g/sq cm thick beryllium target are shown as an example of the results obtainable with this system. The uncertainly in the cross sections for secondary protons varies with particle energy from approximately + or - 9 percent at 50 MeV to approximately + or - 11 percent at 558 MeV.

Beck, S. M.↗
NASA NTRS1974

Cosmic ray anisotropies observed late in the decay phase of solar flare events

Data was obtained from instrumentation on Explorers 34 and 41 on cosmic-ray anisotropy and magnetic field vectors during five solar flare events. The analysis was conducted in the energy range from 0.7 to 7.6 MeV, of the late decay phase, to evaluate the dependence of net cosmic-ray anisotropy vector amplitude and direction on the magnetic field azimuth. Results showed that in the late decay phase the direction of the net cosmic-ray anisotropy vector was invariant in relation to the direction of the magnetic field, particle energy, and species. Within the statistical error of the available data the invariant direction was perpendicular to the mean magnetic field direction.

Allum, F. R.↗