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At least 343 records · Page 19

SCALE 6.2 Lattice Physics Performance Assessment

The US Nuclear Regulatory Commission relies on the lattice physics analysis capabilities of the SCALE code system to perform confirmatory licensing analyses. Either SCALE lattice physics code—TRITON/NEWT or Polaris—can be used to generate cross section data used by the PARCS nodal core simulator for full-core neutronics calculations. This report presents an assessment of the accuracy of SCALE lattice physics codes for preparation of lattice physics data that are used to support simulator codes such as the NRC’s PARCS, for UO 2 -mixed oxide (MOX)/Zr fueled light water reactor (LWR) analyses. Due to the nature of lattice physics calculations, critical reactor experiment benchmarks cannot be modeled in explicit detail in a lattice physics code. However, this limitation does not mean that these measurement data are not usable for lattice physics studies. Therefore, either geometry approximations or axial buckling must be implemented to determine the critical water height. These modeling limitations have led to development of a three-phase assessment strategy. In the first phase, selected critical experiment benchmarks are modeled using the SCALE 3D continuous-energy (CE) Monte Carlo (MC) code KENO, which is the most rigorous neutron transport method available in SCALE, with no approximations in the spatial, angle, or energy treatments. Biases and statistical uncertainties in quantities of interest such as k eff and pin power distributions are determined by comparing CE KENO results to experimental data. This first phase in the accuracy assessment is to demonstrate and establish the use of CE KENO as a reference solution for the second phase. In the second phase, 14 numerical test suites are used to compare SCALE lattice physics calculations with CE KENO as a reference solution. In the third phase, SCALE lattice physics depletion calculations are performed, and the spent fuel isotopic results are compared with available radiochemical assay measurements. Isotopic measurement comparisons provide quantitative assessment of isotopic density distribution predictions with the depletion models in SCALE lattice physics codes. This report documents results for all test suites. The assessment was performed using standard production techniques unless otherwise noted. Both TRITON/NEWT and Polaris exhibited acceptable accuracy for most test cases. For the few test cases in which acceptable accuracy criteria were not met, further code and data development are planned. The computer codes used in this assessment are as follows: SCALE 6.2 rev19189 (pre-release of 6.2.1) was used for CE KENO, TRITON/NEWT and Polaris (PWR only) calculations; SCALE 6.2.2 was used for Polaris calculations to address several updates in support of support boiling water reactor (BWR) lattice geometries. For this work, there is no difference between SCALE 6.2.1 and SCALE 6.2.2 beyond the inclusion of the Polaris BWR analysis capability; The ENDF/B-VII.1 continuous energy and 252 group libraries deployed in SCALE 6.2 were generated with AMPX 6.2 that is distributed with SCALE.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

HYBRD (High Resolution HYBrid Regional Downscaling) Model: Input data and Code

The HYBRD (HYBrid Regional Downscaling) model is a high-resolution urban land downscaling model that can be used to downscale intermediate urban land use and land cover (LULC) products into a high-resolution (30-meters). HYBRD uses a sequential hybrid process, combining statistical models with cellular-automata-based spatial algorithms. This repository contains all the necessary model code and inputs needed to successfully run HYBRD for Los Angeles, California. The repo also contains example outputs of each model step, except the final simulated raster outputs. Examples of simulated raster outputs for multiple scenarios for Los Angeles are available at DOI: 10.57931/2575233. Please refer to Related Works below.

Land↗

Combined magnetic and gravity analysis

Efforts are made to identify methods of decreasing magnetic interpretation ambiguity by combined gravity and magnetic analysis, to evaluate these techniques in a preliminary manner, to consider the geologic and geophysical implications of correlation, and to recommend a course of action to evaluate methods of correlating gravity and magnetic anomalies. The major thrust of the study was a search and review of the literature. The literature of geophysics, geology, geography, and statistics was searched for articles dealing with spatial correlation of independent variables. An annotated bibliography referencing the Germane articles and books is presented. The methods of combined gravity and magnetic analysis techniques are identified and reviewed. A more comprehensive evaluation of two types of techniques is presented. Internal correspondence of anomaly amplitudes is examined and a combined analysis is done utilizing Poisson's theorem. The geologic and geophysical implications of gravity and magnetic correlation based on both theoretical and empirical relationships are discussed.

Hinze, W. J.↗

Zonal average earth radiation budget measurements from satellites for climate studies

Data from 29 months of satellite radiation budget measurements, taken intermittently over the period 1964 through 1971, are composited into mean month, season and annual zonally averaged meridional profiles. Individual months, which comprise the 29 month set, were selected as representing the best available total flux data for compositing into large scale statistics for climate studies. A discussion of spatial resolution of the measurements along with an error analysis, including both the uncertainty and standard error of the mean, are presented.

Ellis, J. S.↗

Information density and efficiency of two-dimensional /2-D/ sampled imagery

Information density and efficiency (i.e., the ratio of information density to data density) are used as criteria for assessing the quality of 2-D sampled and quantized imagery as a function of the statistical properties of random radiance fields, the spatial response (PSF or MTF) and sensitivity of imaging systems, and the sampling and quantization intervals. Computational results are intuitively satisfying: they are consistent with experimental and theoretical results obtained by earlier investigators concerned with the performance of TV cameras, and they provide useful guidelines for optimizing the design of line-scan and sensor-array imaging systems, especially if these systems use a digital communication link for transmitting data.

Huck, F. O.↗

Research Relative to High Spatial Resolution Passive Microwave Sounding Systems

Methods to obtain high resolution passive microwave weather observations, and understanding of their probable impact on numerical weather prediction accuracy were investigated. The development of synthetic aperture concepts for geosynchronous passive microwave sounders were studied. The effects of clouds, precipitation, surface phenomena, and atmospheric thermal fine structure on a scale of several kilometers were examined. High resolution passive microwave sounders (e.g., AMSU) with an increased number of channels will produce initialization data for numerical weather prediction (NWP) models with both increased spatial resolution and coverage. The development of statistical models for error growth in high resolution primitive equation NWP models which permit the consequences of various observing system alternatives, including sensors and assimilation times and procedures is discussed. A high resolution three dimensional primitive equation NWP model to determine parameters in an error growth model similar to that formulated by Lorenz, but with more degrees of freedom is utilized.

Staelin, D. H.↗

Extended testing of a general contextual classifier using the massively parallel processor - Preliminary results and test plans

Earlier encouraging test results of a contextual classifier that combines spatial and spectral information employing a general statistical approach are expanded. The earlier results were of limited meaning because they were produced from small (50-by-50 pixel) data sets. An implementation of the contextual classifier on NASA Goddard's Massively Parallel Processor (MPP) is presented; for the first time the MPP makes feasible the testing of the classifier on large data sets (a 12-hour test on a VAX-11/780 minicomputer now takes 5 minutes on the MPP). The MPP is a Single-Instruction, Multiple Data Stream computer, consisting of 16,384 bit serial microprocessors connected in a 128-by-128 mesh array with each element having data transfer connections with its four nearest neighbors so that the MPP is capable of billions of operations per second. Preliminary results are given (with more expected for the conference) and plans are mentioned for extended testing of the contextual classifier on Thematic Mapper data sets.

Tilton, J. C.↗

The atmosphere as a research window - Signal or noise

The interpretation of measurements by instruments with FOV that are large enough to contain pronounced radiometric variations is discussed, focusing on the retrieval of cloud properties from satellite radiance measurements. Examples are presented for three retrieval approaches. The use of a physical principle to establish a relationship between the measurement and the desired parameter is shown for cirrus-cloud optical depth and precipitation from passive microwave measurements. Examples of the use of inherent variability of the atmosphere and the statistical character of this variability are the spatial covariance method and the threshold method. Also, two examples using a hybrid of these two methods are presented. It is shown that the natural variability (noise) within the instrument FOV biases the relationship between the measurement and the parameter of interest which would be predicted by simple one-dimensional radiative-transfer theory. It is suggested that the noise of the measurement also contains signal, forming the basis for hybrid methods.

Stephens, Graeme L.↗

Analysis of stratocumulus cloud fields using LANDSAT imagery: Size distributions and spatial separations

Stratocumulus cloud fields in the FIRE IFO region are analyzed using LANDSAT Thematic Mapper imagery. Structural properties such as cloud cell size distribution, cell horizontal aspect ratio, fractional coverage and fractal dimension are determined. It is found that stratocumulus cloud number densities are represented by a power law. Cell horizontal aspect ratio has a tendency to increase at large cell sizes, and cells are bi-fractal in nature. Using LANDSAT Multispectral Scanner imagery for twelve selected stratocumulus scenes acquired during previous years, similar structural characteristics are obtained. Cloud field spatial organization also is analyzed. Nearest-neighbor spacings are fit with a number of functions, with Weibull and Gamma distributions providing the best fits. Poisson tests show that the spatial separations are not random. Second order statistics are used to examine clustering.

Welch, R. M.↗

Raman scattering measurements in flames using a tunable KrF excimer laser

A narrow-band tunable KrF excimer laser is used as a spontaneous vibrational Raman scattering source to demonstrate that single-pulse concentration and temperature measurements, with only minimal fluorescence interference, are possible for all major species (O2, N2, H2O, and H2) at all stoichiometries (fuel-lean to fuel rich) of H2-air flames. Photon-statistics-limited precisions in these instantaneous and spatially resolved single-pulse measurements are typically 5 percent, which are based on the relative standard deviations of single-pulse probability distributions. In addition to the single-pulse N2 Stokes/anti-Stokes ratio temperature measurement technique, a time-averaged temperature measurement technique is presented that matches the N2 Stokes Raman spectrum to theoretical spectra by using a single intermediate state frequency to account for near-resonance enhancement. Raman flame spectra in CH4-air flames are presented that have good signal-to-noise characteristics and show promise for single-pulse UV Raman measurements in hydrocarbon flames.

Wehrmeyer, Joseph A.↗

Towards Inner Magnetosphere Particle and Field Models

We report on the initial stages of an effort to construct comprehensive empirical models of the plasma and fields in the inner magnetosphere. The models are based not only on archival data (e.g., SCATHA) but also on current spacecraft mission data (e.g., POLAR). In this component of the effort, we incorporate the recently archived data provided by the SCATRA (Spacecraft Charging AT High Altitude) satellite. The SCAT14A satellite was in a near-geostationary orbit and was in operation for more than a decade. In this paper, we focus on the SCATRA plasma and magnetic field data from approximately the first two years of operation. The time-series data are binned according to spatial location and geomagnetic activity. Examples of statistical, empirical models from this initial effort are presented; even the simply-constructed preliminary models reveal such subtle features as the previously reported warm (greater than 100 eV) plasma density enhancement near the magnetic equator.

Spence, H. E.↗

Decomposition of Sources of Errors in Seasonal Streamflow Forecasting over the U.S. Sunbelt

Seasonal streamflow forecasts, contingent on climate information, can be utilized to ensure water supply for multiple uses including municipal demands, hydroelectric power generation, and for planning agricultural operations. However, uncertainties in the streamflow forecasts pose significant challenges in their utilization in real-time operations. In this study, we systematically decompose various sources of errors in developing seasonal streamflow forecasts from two Land Surface Models (LSMs) (Noah3.2 and CLM2), which are forced with downscaled and disaggregated climate forecasts. In particular, the study quantifies the relative contributions of the sources of errors from LSMs, climate forecasts, and downscaling/disaggregation techniques in developing seasonal streamflow forecast. For this purpose, three month ahead seasonal precipitation forecasts from the ECHAM4.5 general circulation model (GCM) were statistically downscaled from 2.8deg to 1/8deg spatial resolution using principal component regression (PCR) and then temporally disaggregated from monthly to daily time step using kernel-nearest neighbor (K-NN) approach. For other climatic forcings, excluding precipitation, we considered the North American Land Data Assimilation System version 2 (NLDAS-2) hourly climatology over the years 1979 to 2010. Then the selected LSMs were forced with precipitation forecasts and NLDAS-2 hourly climatology to develop retrospective seasonal streamflow forecasts over a period of 20 years (1991-2010). Finally, the performance of LSMs in forecasting streamflow under different schemes was analyzed to quantify the relative contribution of various sources of errors in developing seasonal streamflow forecast. Our results indicate that the most dominant source of errors during winter and fall seasons is the errors due to ECHAM4.5 precipitation forecasts, while temporal disaggregation scheme contributes to maximum errors during summer season.

Decomposition↗

Regional Changes in Earths Color and Texture as Observed From Space Over a 15-Year Period

Earth-observing satellites provide global observations of many geophysical variables. As these variables are derived from measured radiances, the underlying radiance data are the most reliable sources of information for change detection. Here, we identify statistically significant trends in the color and spatial texture of the Earth as viewed from multiple directions from the Multi-angle Imaging SpectroRadiometer (MISR), which has been sampling the angular distribution of scattered sunlight since 2000. Globally, our results show that the Earth has been appearing relatively bluer (up to 1.6 % per decade from both nadir and oblique views) and smoother (up to 1.5 % per decade only from oblique views) over the past 15 years. The magnitude of the global blueing trends is comparable to that of uncertainties in radiometric calibration stability. Regional shifts in color and texture, which are significantly larger than global means, are observed, particularly over polar regions, along the boundaries of the subtropical highs, the tropical western Pacific, Southwestern Asia, and Australia. We demonstrate that the large regional trends cannot be explained either by uncertainties in radiometric calibration or variability in total or spectral solar irradiance; hence, they reflect changes internal to the Earths climate system. The 15-year-mean true color composites and texture images of the Earth at both nadir and oblique views are also presented for the first time.

regional trends↗

Spatial and temporal variability of VAS radiance measurements by structure and correlation analysis

The statistical structure function analysis presently applied to VISSR Atmospheric Sounder (VAS) measurements has been extended to include time, and yields structure plots in either two spatial dimensions or one spatial dimension and time that indicate three-dimensional measurement variability. The analyses that include time as a coordinate also yield an indication of the mean speed and direction of the analyzed data. Results for three-hourly VAS data indicate that sampling at a high, approximately 1-hour, frequency is required in order to correctly monitor VAS measurements' temporal variability in a way equivalent to high spatial resolution.

Hillger, Donald W.↗

A comparison of the climate simulated by the NCAR Community Climate Model (CCM1:R15) with ECMWF analyses

The Community Climate Model (CCM) is a comprehensive three-dimensional global atmospheric model assembled at the National Center for Atmospheric Research. Detailed comparisons are made between the climate simulated by a seasonal version of CCM, namely CCM1, at 12 level, R15 spectral resolution, and that revealed by ECMWF operational analyses over 1980-86 truncated to a similar resolution. A variety of circulation statistics are presented in order to reveal the spatial character and seasonality of CCM1 biases in temperature, winds, and wave flux quantities. The biases revealed for CCM1 are similar in many respects to those found in other climate models run at similar resolution. Results of this study are then presented, discussing temperature biases and thermodynamic balances, wind fields, zonal mean wave diagnostics, stationary waves, and local eddy statistics. The major findings are then summarized.

Randel, William J.↗

Magnetic imaging and statistical analysis of the metamagnetic phase transition of FeRh with electron spins in diamond

Magnetic imaging based on nitrogen-vacancy (NV) centers in diamond has emerged as a powerful tool for probing magnetic phenomena in fields ranging from biology to physics. A key strength of NV sensing is its local-probe nature, enabling high-resolution spatial images of magnetic stray fields emanating from a sample. However, this local character can also form a drawback for analyzing the global properties of a system, such as a phase transition temperature. Here, we address this challenge by using statistical analyses of magnetic-field maps to characterize the first-order temperature-driven metamagnetic phase transition from the antiferromagnetic to the ferromagnetic state in FeRh. After imaging the phase transition and identifying the regimes of nucleation, growth, and coalescence of ferromagnetic domains, we statistically characterize the spatial magnetic-field maps to extract the transition temperature and thermal hysteresis width. By analyzing the spatial correlations of the maps in relation to the magnetocrystalline anisotropy and external magnetic field, we detect a reorientation of domain walls across the phase transition. The employed statistical approach can be extended to the study of other magnetic phenomena with NV magnetometry or other sensing techniques.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Object-Based Evaluation of Dynamical and Statistical Downscaled Precipitation Products over CONUS

High-resolution precipitation data, generated through dynamical downscaling (DD) or statistical downscaling (SD) of global climate model output, provide critical information for regional climate assessment and adaptation planning. Most downscaling development and validation have focused on accurate gridscale precipitation construction and ignored the spatial structure of precipitation across model grids and at the event scale. However, many applications, e.g., hydrologic modeling and the analysis using the downscaled precipitation, require a reasonable representation of the spatial structure of precipitation within watersheds. Therefore, a set of standard metrics to evaluate the representation of the spatial structure of individual storms across diverse downscaled precipitation products is desired. To address this need, we conducted an object-based evaluation of precipitation in decades-long DD and SD products over the contiguous United States (CONUS). Specifically, we evaluate their ability to reproduce various features of precipitation objects in the observations: total volume, precipitation area, peak intensity, and spatial structure. Multiple metrics (bias, Perkins score, and nonparametric statistical tests) are used to quantify model performance. Our evaluation reveals notable variations in performance among individual products across different climate zones and seasons, as well as between extreme and nonextreme events. In general, most DD products exhibit balanced performance across the four precipitation object features, while SD products vary more significantly in their performance across products. Based on this comprehensive evaluation, we provide guidance on choosing downscaled products for specific regions, seasons, and precipitation object features. These findings and recommendations can inform precipitation-relevant modeling and analysis over CONUS, guide future downscaling technique developments, and provide actionable information for climate impact assessment and adaptation.

Downscaling↗

Object-Based Evaluation of Dynamical and Statistical Downscaled Precipitation Products over CONUS

High-resolution precipitation data, generated through dynamical downscaling (DD) or statistical downscaling (SD) of global climate model output, provide critical information for regional climate assessment and adaptation planning. Most downscaling development and validation have focused on accurate gridscale precipitation construction and ignored the spatial structure of precipitation across model grids and at the event scale. However, many applications, e.g., hydrologic modeling and the analysis using the downscaled precipitation, require a reasonable representation of the spatial structure of precipitation within watersheds. Therefore, a set of standard metrics to evaluate the representation of the spatial structure of individual storms across diverse downscaled precipitation products is desired. To address this need, we conducted an object-based evaluation of precipitation in decades-long DD and SD products over the contiguous United States (CONUS). Specifically, we evaluate their ability to reproduce various features of precipitation objects in the observations: total volume, precipitation area, peak intensity, and spatial structure. Multiple metrics (bias, Perkins score, and nonparametric statistical tests) are used to quantify model performance. Our evaluation reveals notable variations in performance among individual products across different climate zones and seasons, as well as between extreme and nonextreme events. In general, most DD products exhibit balanced performance across the four precipitation object features, while SD products vary more significantly in their performance across products. Based on this comprehensive evaluation, we provide guidance on choosing downscaled products for specific regions, seasons, and precipitation object features. These findings and recommendations can inform precipitation-relevant modeling and analysis over CONUS, guide future downscaling technique developments, and provide actionable information for climate impact assessment and adaptation.

Environmental sciences↗