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Thin film replica technique for obtaining high quality electron micrographs of craters in metal substrates formed by microscopic hypervelocity particle impact
We use a nascent data-driven causal discovery method to find and compare causal relationships in observed data and climate model output. We consider ten different features in the Arctic climate collected from public databases on observational and Energy Exascale Earth System Model (E3SM) data. In identifying and analyzing the resulting causal networks, we make meaningful comparisons between observed and climate model interdependencies. This work demonstrates our ability to apply the PCMCI causal discovery algorithm to Arctic climate data, that there are noticeable similarities between observed and simulated Arctic climate dynamics, and that further work is needed to identify specific areas for improvement to better align models with natural observations.
The present study considers two types of fluid models as methods for reproducing the blueshifts and line broadening observed in soft X-ray lines during the rise of solar flares. The fluid models representing the laminar flow of material in a jet and through a pipe were used to derive the velocity at each location in the flow. The optimal values for the free parameters in the jet model were within the ranges expected for solar flare conditions. The jet was much smaller than typical flare volumes estimated from spatial images, but the resulting densities were consistent with the upper limit of densities derived using density-sensitive line ratios. This supports previous reports of small filling factors and is interpreted as evidence for a chromospheric origin for the flow. Stability analysis indicates that, although the laminar jet can reproduce the observations, instabilities may cause the flow to become turbulent, and that this laminar treatment is not strictly valid.
Abstract Atmospheric gravity waves (GWs) impact the circulation and variability of the atmosphere. Sub‐grid scale GWs, which are too small to be resolved, are parameterized in weather and climate models. However, some models are now available at resolutions at which these waves become resolved and it is important to test whether these models do this correctly. In this study, a GW resolving run of the European Center for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS), run with a 1.4 km average grid spacing (TCo7999 resolution), is compared to observations from the Atmospheric Infrared Sounder (AIRS) instrument, on NASA's Aqua satellite, to test how well the model resolves GWs that AIRS can observe. In this analysis, nighttime data are used from the first 10 days of November 2018 over part of Asia and surrounding regions. The IFS run is resampled with AIRS's observational filter using two different methods for comparison. The ECMWF ERA5 reanalysis is also resampled as AIRS, to allow for comparison of how the high resolution IFS run resolves GWs compared to a lower resolution model that uses GW drag parametrizations. Wave properties are found in AIRS and the resampled models using a multi‐dimensional S‐Transform method. Orographic GWs can be seen in similar locations at similar times in all three data sets. However, wave amplitudes and momentum fluxes in the resampled IFS run are found to be significantly lower than in the observations. This could be a result of horizontal and vertical wavelengths in the IFS run being underestimated.
The rawinsonde sounding program for the AVE/VAS II experiment is described and tabulated data at 25 mb intervals are presented. Soundings were taken at 3 hr intervals, was an 18 hour period. An additional sounding was taken at the normal synoptic observation time. The processing soundings method is discussed, estimates of the RMS errors in the data are presented, and an example of contact data is given. Termination pressures of soundings taken in the meso-beta-scale network are tabulated, as are observations of ground temperature at a depth of 2 cm.
The ratio is a new reaction observable suggested to extract accurately structure information on halo nuclei. It corresponds to the ratio of differential cross sections for scattering and breakup, which is predicted to remove the uncertainty related to the reaction dynamics. We present here the first experimental test of the method for the 11 Be + 12 C collision at E Lab = 20A MeV performed at Texas A&M University. Differential cross sections for scattering and inclusive one-neutron breakup have been measured with the new detector array BlueSTEAl. The ratio of cross sections is very smooth and independent of the projectile-target interaction, which demonstrates the validity of the ratio method. We extend our analysis to existing 11 Be + 208 Pb data, confirming that the method works well on any target.
The observation of gravitational-radiation damping in the binary pulsar PSR 1913 + 16 and the ongoing experimental search for gravitational waves of extraterrestrial origin have made the theory of gravitational radiation an active branch of classical general relativity. In calculations of gravitational radiation, approximation methods play a crucial role. Recent developments are summarized in two areas in which approximations are important: (a) the quadrupole approxiamtion, which determines the energy flux and the radiation reaction forces in weak-field, slow-motion, source-within-the-near-zone systems such as the binary pulsar; and (b) the normal modes of oscillation of black holes, where the Wentzel-Kramers-Brillouin approximation gives accurate estimates of the complex frequencies of the modes.
Real-time state estimation and forecasting are critical for the efficient operation of power grids. In this paper, a physics-informed Gaussian process regression (PhI-GPR) method is presented and used for forecasting and estimating the phase angle, angular speed, and wind mechanical power of a three-generator power grid system using sparse measurements. In standard data-driven Gaussian process regression (GPR), parameterized models for the prior statistics are fit by maximizing the marginal likelihood of observed data. In the PhI-GPR method, we propose to compute the prior statistics offline by solving stochastic differential equations (SDEs) governing the power grid dynamics. The short-term forecast of a power grid system dominated by wind generation is complicated by the stochastic nature of the wind and the resulting uncertainty in wind mechanical power. Here, we assume that the power grid dynamics are governed by swing equations, with the wind mechanical power fluctuating randomly in time. We solve these equations for the mean and covariances of the power grid states using the Monte Carlo simulation method. We demonstrate that the proposed PhI-GPR method can accurately forecast and estimate observed and unobserved states. For the considered problem, PhI-GPR has computational advantages over the ensemble Kalman filter (EnKF) method: In PhI-GPR, ensembles are computed offline and independently of the data acquisition process, whereas for EnFK, ensembles are computed online with data acquisition, rendering real-time forecast more challenging. We also demonstrate that the PhI-GPR forecast is more accurate than the EnKF forecast when the random mechanical wind power is non-Markovian. In contrast, the two methods produce similar forecasts for the Markovian mechanical wind power. For observed states, we show that PhI-GPR provides a forecast comparable to the standard data-driven GPR; both forecasts are significantly more accurate than the autoregressive integrated moving average (ARIMA) forecast. We also show that the ARIMA forecast is more sensitive to observation frequency and measurement errors than the PhI-GPR forecast.
The broad-line region of quasars has long been probed by reverberation mapping techniques that measure time lags between continuum and broad emission-line variations. Stacked reverberation mapping has been proposed as a less observationally expensive alternative to traditional methods. This ensemble approach also reduces biases from small-number statistics. The Dark Energy Spectroscopic Instrument (DESI) is conducting the most extensive spectroscopic survey of quasars to date. We create mock light curves emulating expected DESI quasar observations at redshifts $1.48\lt z\lt 5.2$ and luminosities $44.68 \le \log \lambda L_{1350 \mathring{\rm A}{}} / \mathrm{erg\, s^{-1}} \le 45.99$ to test stacked reverberation mapping feasibility using sparse spectroscopic data paired with well-sampled photometric data. The pipeline, using the lag estimation code JAVELIN (Just Another Vehicle for Estimating Lags In Nuclei), successfully recovers the simulated C IV lags within 1σ of the true values using spectroscopic light curves composed of only a few spectral epochs (2–10) with irregular cadences. We investigate how observational factors, including C IV flux error magnitude, number of stacked quasars, and spectral epoch count, affect performance. This work motivates a pathway for future stacked reverberation mapping projects with large-scale spectroscopic surveys of quasars having $\ge 2$ spectroscopic observations. Our results suggest an economical alternative for constraining and extending the radius–luminosity relation to higher redshifts and luminosities. Subsequently, this relation can be employed more reliably in single-epoch black hole mass measurements and quasar cosmology in these distant regimes.
Two different but complementary methods for predicting Mira luminosities are presented. One method is derived from a Fourier analysis, it requires performing deconvolution, and its results are not certain due to the inherent instability of deconvolution problems. The other method is a learning method utilizing artificial intelligence techniques where a light curve is presented as an ordered sequence of pseudocycles, and rules are learned by linking the characteristics of several consecutive pseudocycles to one characteristic of the future cycle. It is observed that agreement between these methods is obtainable when it is possible to eliminate similar false frequencies from the preliminary power spectrum and to improve the degree of confidence in the rules.
The Moderate Resolution Imaging Spectrometer (MODIS) observing facility will operate on the Earth Observing System (EOS) in the late 1990's. It is estimated that this observing facility will produce over 200 gigabytes of data per day requiring a storage capability of just over 300 gigabytes per day. Archiving, browsing, and distributing the data associated with MODIS represents a rich opportunity for testing and applying both lossless and lossy data compression methods.
Comparing the observed brightness of various buried objects is a straightforward way to characterize the performance of a ground penetrating radar (GPR) system. However, a limitation arises. A simple comparison of buried object brightness values does not disentangle the effects of the GPR system itself from the system's operating environment and the objects being observed. Therefore, with brightness values exhibiting an unknown synthesis of systemic, environmental, and object factors, GPR system analysis becomes a convoluted affair. In this work, we use an experimentally collected dataset of over 25,000 object observations from five different multistatic radar arrays to develop models of buried object brightness and control for these various effects. Additionally, our modeling efforts provide a means for quantifying the relative brightness of GPR systems, the objects they detect, and the physical properties of those objects that influence observed brightness. To evaluate the models' performance on new object observations, we repeatedly simulate fitting them to half the dataset and predicting the observed brightness values of the unseen half. In addition, we introduce a method for estimating the probability that individual observations constitute a visible object, which aids in failure analysis, performance characterization, and dataset cleaning.
The three main objectives of the overall project were: 1. Adaptation of environmental constraint methods to take advantage of EOS sensors, specifically, MODIS, ASTER, and Landsat-7, in addition to the PM AVHRR observations 2. Refinement of environmental constraint methods based on fundamental scientific knowledge. 3. Assessment of spatial scaling patterns in environmental constraint measurements to evaluate the potential biases and errors that occur when estimating regional and global-scale NPP patterns with moderate to coarse satellite observations. These goals were modified because, on one hand, MODIS data did not become available until after the first year of the project and because of project staffing issues at the University of Maryland., The OSU portion of the project contained a modest amount of funding and responsibility compared to the University of Maryland and the University of Toledo.
The radio scintillations observed during occultations of Voyagers 1 and 2 by Saturn are analyzed to determine the morphology of plasma irregularities and hence the magnetic field orientation in Saturn's upper atmosphere. The measurement techniques, the weak scattering theory, and the method used to relate the observed radio scintillations to physical properties of the ionospheric irregularities are briefly described. Results on the spatial characteristics of the irregularities are presented, and the magnetic field orientation in Saturn's ionosphere is inferred. Although the occultation measurements generally confirm the accuracy of the Saturnian magnetic field model of Connerney et al. (1982), it is found that a small adjustment of the coefficients in that model's zonal harmonic expansion would remove the discrepancy between the model predictions and the measurements. A strategy for obtaining improved measurements of Saturn's magnetic field from radio occultation observations of scintillations and Faraday rotation using an orbiting spacecraft is briefly discussed.
A new method for forecasting turbulence is developed and evaluated using the high resolution weather model and in situ turbulence observations from commercial aircraft. The new method is an ensemble of various turbulence metrics from multiple time-lagged ensemble forecasts created using a sequence of four procedures. These include weather modeling, calculation of turbulence metrics, mapping the metrics into a common turbulence-scale, and production of final forecast. The new method uses similar methodology as current operational turbulence forecast with three improvements. First, it uses a higher resolution ((delta)x = 3 km) weather model to capture cloud resolving scale phenomena. Second, it computes the metrics for multiple forecasts that are combined at the same valid time resulting in a time-lagged ensemble of multiple turbulence metrics. Finally, it provides both deterministic and probabilistic turbulence forecasts. Results show the new forecasts match well with observed radar reflectivity along a surface front as well as convectively induced turbulence outside the clouds on research period. Overall performance skill of the new turbulence forecast compared with the observed EDR data during the research period is superior to any single turbulence metric. The probabilistic turbulence forecast is used in an example air traffic management application for creating a wind-optimal route considering turbulence information. The wind-optimal route passing through areas of 50% potential for moderate-or-greater turbulence and the lateral turbulence avoidance routes starting from three different waypoints along the wind-optimal route from Los Angeles international airport to John F. Kennedy international airport are calculated using different turbulence forecasts. This example shows additional flight time is required to avoid potential turbulence encounters.
An eigenspace assignment approach to the design of parameter insensitive control laws for linear multivariable systems is presented. The control design scheme utilizes constrained optimization techniques to exploit the flexibility in eigenvector assignments to reduce control system sensitivity to changes in system parameters while maintaining performance requirements; it thus provides a systematic approach for choosing values for eigensystem design variables. The methods involve use of the singular value decomposition to provide an exact description of allowable eigenvectors in terms of a minimum number of design parameters. In a design example, the methods are applied to the problem of symmetric flutter suppression in an aeroelastic vehicle. In this example the flutter mode is sensitive to changes in dynamic pressure and eigenspace methods are used to enhance the performance of a stabilizing minimum energy/linear quadratic regulator controller and associated observer. Numerical results indicate that the methods provide feedback control laws that make the stability of the nominal closed loop systems less sensitive to changes in dynamic pressure, while maintaining acceptable control power and robustness constraints.
Given here are predictions of fragment velocities and azimuths resulting from the Space Transportation System Solid Rocket Motor range destruct, or random failure occurring at any time during the 120 seconds of Solid Rocket Motor burn. Results obtained using the analytical methods described showed good agreement between predictions and observations for two specific events. It was shown that these methods have good potential for use in predicting the fragmentation process of a number of generically similar casing systems. It was concluded that coupled Eulerian-Lagrangian calculational methods of the type described here provide a powerful tool for predicting Solid Rocket Motor response.
We have cataloged 160 CMEs detected in the HELIOS 1 and 2 90 deg zodiacal light photometers observed from 1975-1985. The HELIOS 1 and 2 spacecraft orbited from 0.3 to 1.0 AU on 6-month orbits. From the photometer observations of Thomson-scattered light in the inner heliosphere, we have determined CME masses for these events using two methods: (1) by integration over the contours drawn between the three photometers at a given time; and (2) by integration of the mass flow over time past a given photometer. The second method, not readily available using coronagraph observations, is derived from CME speeds measured by using the timing of the peak CME brightness from the 16 deg to 31 deg sets of photometers. The two different HELIOS methods of determining CME mass are consistent with one another for individual CMEs. We find that the CME mass values range from 10(exp 15)g to nearly 10(exp 17)g. We compare the mass distributions of HELIOS-measured CMEs with those from coronagraphs and find that CMEs measured by HELIOS over the same time interval are generally more massive. The solar cycle variation of the total CME mass present in the heliosphere varies by over a factor of approximately 15 from solar minimum to solar maximum. Slightly more massive CMEs carry the bulk of the CME mass during maximum. The total CME mass at solar maximum is found to be near 15% of the total solar wind mass.