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DOE OSTI2018

Prognostic Power of Extreme Rainfall Scaling Formulas Across Space and Time Scales

Some studies documenting changes in extreme precipitation use scaling formulas to approximate the large percentiles of the rainfall distribution from average dynamical and thermodynamical variables, called predictors. Here we instead assess the performance of these formulas as approximations to the rain rates in individual events. We evaluate the accuracies of the scaling relationships as functions of spatial and temporal scales by analyzing tropical rainfall in a superparameterized model. Relationships using full vertical profiles of the predictors are more accurate than those using their values at specific vertical levels because they better characterize the specific dynamics of each event. Both types of scaling relationships perform well over a range of length scales from 200 to 2,000 km and time scales from an hour to a week, and their precision is higher in the case of simulations with superparameterization than with parameterized convection. Uncertainties emerging from the local use of the scaling relationships suggest that they may only characterize the intensification of individual extremes for a warming of 4–5 K or larger. Finally, we argue that these formulas can be used to reconstruct the tail of the rainfall distribution directly from its predictors without prior information on P. While scalings have been used as diagnostic equations conditioned on the occurrence of extreme rainfall, they are actually able to mimic the prognostic behavior of climate model parameterizations on a variety of scales when estimating the intensity, frequency, and spatial patterns of extremes.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2018

Improving Imaging Instrument Spatial Resolution Using Software

In order to overcome spatial resolution limitations associated with physical sensor limitations when using smallsats and cubesats, we utilize an image processing technology referred to as Super-Resolution (SR). In general, software approaches are increasingly considered in connection with smaller satellites for which size, mass and power constraints limit the sensor capabilities. Being able to perform hardware vs. software trades might enable more capabilities for a lower cost. This paper describes recent experiments conducted to optimize the spatial enhancement of acquired observations using multiple sub-pixel shifted low resolution image.

Image Processing↗

NASA NTRS2018

Improving the Spatial Resolution of Imaging Instruments Using Software

Image Processing↗

DOE OSTI2022

Simple through-plane spatial filter for 2D MRI projections

A simple spatial filter for 2D projection MR imaging is introduced. It works in the third (unresolved) direction to eliminate uniform or slowly varying interfering background signals. A constant amplitude gradient pulse in the unresolved direction is applied at the same time as the usual phase encode gradient during 2D acquisition. The filter is demonstrated for root imaging in soil, where background soil water signals can be troublesome. The filter suppresses the soil water signal while preserving the desired signal of plant roots. Fundamental to the operation of the filter is that the roots are sparse in the image domain, meaning there are relatively few pixels with multiple roots present. Here, the performance of the through-plane filter is demonstrated and compares favorably to more conventional in-plane spatial filtering.

47 OTHER INSTRUMENTATION↗

NASA NTRS2013

A Parameter Estimation Scheme for Multiscale Kalman Smoother (MKS) Algorithm Used in Precipitation Data Fusion

A new approach is presented in this paper to effectively obtain parameter estimations for the Multiscale Kalman Smoother (MKS) algorithm. This new approach has demonstrated promising potentials in deriving better data products based on data of different spatial scales and precisions. Our new approach employs a multi-objective (MO) parameter estimation scheme (called MO scheme hereafter), rather than using the conventional maximum likelihood scheme (called ML scheme) to estimate the MKS parameters. Unlike the ML scheme, the MO scheme is not simply built on strict statistical assumptions related to prediction errors and observation errors, rather, it directly associates the fused data of multiple scales with multiple objective functions in searching best parameter estimations for MKS through optimization. In the MO scheme, objective functions are defined to facilitate consistency among the fused data at multiscales and the input data at their original scales in terms of spatial patterns and magnitudes. The new approach is evaluated through a Monte Carlo experiment and a series of comparison analyses using synthetic precipitation data. Our results show that the MKS fused precipitation performs better using the MO scheme than that using the ML scheme. Particularly, improvements are significant compared to that using the ML scheme for the fused precipitation associated with fine spatial resolutions. This is mainly due to having more criteria and constraints involved in the MO scheme than those included in the ML scheme. The weakness of the original ML scheme that blindly puts more weights onto the data associated with finer resolutions is overcome in our new approach.

multiscale↗

DOE OSTI2021

Guiding the Design of Heterogeneous Electrode Microstructures for Li-Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning

Electrochemical and mechanical properties of lithium-ion battery materials are heavily dependent on their 3D microstructure characteristics. A quantitative understanding of the role played by stochastic microstructures is critical for the prediction of material properties and for guiding synthesis processes. Furthermore, tailoring microstructure morphology is also a viable way of achieving optimal electrochemical and mechanical performances of lithium-ion cells. To facilitate the establishment of microstructure-resolved modeling and design methods, a review covering spatially and temporally resolved imaging of microstructure and electrochemical phenomena, microstructure statistical characterization and stochastic reconstruction, microstructure-resolved modeling for property prediction, and machine learning for microstructure design is presented here. The perspectives on the unresolved challenges and opportunities in applying experimental data, modeling, and machine learning to improve the understanding of materials and identify paths toward enhanced performance of lithium-ion cells are presented.

25 ENERGY STORAGE↗

NASA NTRS2017

Investigating Access Performance of Long Time Series with Restructured Big Model Data

Data sets generated by models are substantially increasing in volume, due to increases in spatial and temporal resolution, and the number of output variables. Many users wish to download subsetted data in preferred data formats and structures, as it is getting increasingly difficult to handle the original full-size data files. For example, application research users such as those involved with wind or solar energy, or extreme weather events are likely only interested in daily or hourly model data at a single point (or for a small area) for a long time period, and prefer to have the data downloaded in a single file. With native model file structures, such as hourly data from NASA Modern-Era Retrospective analysis for Research and Applications Version-2 (MERRA-2), it may take over 10 hours for the extraction of parameters-of-interest at a single point for 30 years. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is exploring methods to address this particular user need. One approach is to create value-added data by reconstructing the data files. Taking MERRA-2 data as an example, we have tested converting hourly data from one-day-per-file into different data cubes, such as one-month, or one-year. Performance is compared for reading local data files and accessing data through interoperable services, such as OPeNDAP. Results show that, compared to the original file structure, the new data cubes offer much better performance for accessing long time series. We have noticed that performance is associated with the cube size and structure, the compression method, and how the data are accessed. An optimized data cube structure will not only improve data access, but also may enable better online analysis services

reanalysis↗

NASA NTRS1992

Desirable features of an infrared imaging system for aerodynamic research

Advantage of non-intrusiveness, capability for field measurement, and increased availability of IR imaging systems have resulted in their wider use for aerodynamic research. However, certain difficulties persist while using currently available systems for such applications. A critical evaluation of the IR imaging systems is presented on the basis of the state-of-the-art of IR imaging technology and experiences in wind tunnel and flight testing at NASA's Langley Research Center. The requirements for using IR thermography as a measurement tool in aerodynamic research are examined in terms of range, sensitivity, and accuracy of temperature measurement, temporal and spatial resolution, and features of target. Deficiencies of present IR imaging systems are identified, and user precautions to avoid such problems by proper selection and operation of these units are suggested. Different aspects of imager performance such as imager optics, video capabilities, and environmental tolerance are discussed. Electronic data recording and image processing hardware and software requirements are evaluated. Slit response tests and spatial resolution are discussed with the objective of obtaining reliable, accurate, and meaningful information from IR thermography measurements for aerodynamic studies.

Wright, Robert E., Jr.↗

NASA NTRS2002

A Method to Retrieve Rainfall Rate over Land from TRMM Observations

Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) observations over mesoscale convective systems (MCSs) reveal that there are localized maxima in the rain rate with a scale of about 10 to 20 km that represent thunderstorms (Cbs). Some of these Cbs are developing or intense, while others are decaying or weak. These Cbs constitute only about 20 % of the rain area of a given MCS. Outside of Cbs, the average rain rate is much weaker than that within Cbs. From an analysis of the PR data, we find that the spatial distribution of rain and its character, convective or stratiform, is highly inhomogeneous. This complex nature of rain exists on a scale comparable to that of a Cb. The 85 GHz brightness temperature, T85, observations of the TRMM Microwave Imager (TMI) radiometer taken over an MCS reflect closely the PR rain rate pattern over land. Local maxima in rain rate shown by PR are observed as local minima in T85. Where there are no minima in T85, PR observations indicate there is light rain. However, the TMI brightness temperature measurements (Tbs) have poor ability to discriminate convective rain from stratiform rain. For this reason, a TMI rain retrieval procedure that depends primarily on the magnitude of Tbs performs poorly. In order to retrieve rain rate from TMI data on land one has to include the spatial distribution information deduced from the T85 data in the retrieval method. Then, quantitative estimation of rain rate can be accomplished. A TMI rain retrieval method developed along these lines can yield estimates of rain rate and its frequency distribution which agree closely with that given by PR. We find the current TRMM project TMI (Version 5) rain retrieval algorithm on land could be improved with the retrieval scheme developed here. To support the conceptual frame work of the rain retrieval method developed here, a theoretical analysis of the TMI brightness temperatures in convective and stratiform regions is presented.

Prabhakara, C.↗

DOE OSTI2017

Prospects for identification the direct and indirect effects of extra spatial dimensions at the Large Hadron Collider

The existence of massive graviton states, that may be produced as real and virtual particles in high-energy proton collisions, is predicted by the modern developments of Kaluza-Klein models with extra spatial dimensions. The direct and indirect signatures of large and warped extra spatial dimensions may be revealed by analyzing the specific characteristics of dilepton and diphoton final states formed in proton-proton collisions at the Large Hadron Collider (LHC). Virtual effects in the Kaluza-Klein models with large extra spatial dimensions can be discovered by the specic behavior of the dilpton and diphoton invariant-mass distributions, and their identication (in case of discovery) can be performed by the analysis of their angular distributions with integrated center-edge asymmetry. Assuming the nominal values of the LHC collision energy (14 TeV) and luminosity (100 fb{sup –1}), for the models with large extra spatial dimensions we find that the sensitivity to the cutoff parameter M{sub S} will extend up to 8.5 and 7.6 TeV for their discovery and identification, respectively. For the Randall-Sundrum model with a warped extra dimension, the LHC experiments will be sensitive to the graviton resonance with mass up to 4.4 and 3.1 TeV for its discovery and identification, respectively.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Improving seasonal precipitation forecasts in the Western United States through statistical downscaling

Abstract Seasonal precipitation forecasts in the western United States are critical resources for water resource management, especially during winter. While current seasonal forecasting systems provide monthly precipitation forecasts operationally, their coarse resolution limits their effectiveness in capturing the localized precipitation patterns and snowpack conditions essential for water resource managers in the mountainous regions. Here, analog statistical downscaling is demonstrated as an effective approach to enhance the spatial resolution of operational seasonal forecasts provided by the North American Multi-Model Ensemble. Downscaling was performed by building an analog ‘library’, in which corresponding model forecasts and observed values during the training period were stored. In the testing period, unseen model forecasts referenced the closest historical forecast from the analog library and applied the corresponding observational value for each point. This analysis indicates that downscaled products can capture localized features more accurately than the original coarse resolution forecasts, reducing forecast error across the western United States. Moreover, downscaling individual ensemble members—rather than downscaling the ensemble mean—further reduces forecasting error for their multi-model ensemble mean products. The greatest error reductions in the downscaled product, measured by root mean squared error (RMSE), were observed at low to mid-elevations (500–2000 meters), with 50%–70% improvement relative to the original forecasts. In the higher elevations (2000 meters and above), changes in RMSE relative to the original forecast were limited to 10%–30% improvements. The improvement is more substantial for forecast systems with 10 ensemble members compared to that with 4 members, but this relationship does not hold for the system with 24 ensemble members. These findings show that analog statistical downscaling can effectively address the spatial limitations of seasonal precipitation forecasts with minimal computational cost, providing a valuable framework for enhancing coarse resolution forecasting products while providing insights into the timing of ensemble mean calculations during the downscaling process.

Vernon, B. (ORCID:0009000891670689)↗

DOE OSTI2016

Modeling the influence of preferential flow on the spatial variability and time-dependence of mineral weathering rates

Inferences of weathering rates from laboratory and field observations suggest significant scale and time-dependence. Preferential flow induced by heterogeneity (manifest as permeability variations or discrete fractures) has been suggested as one potential mechanism causing scale/time-dependence. In this paper, we present a quantitative evaluation of the influence of preferential flow on weathering rates using reactive transport modeling. Simulations were performed in discrete fracture networks (DFNs) and correlated random permeability fields (CRPFs), and compared to simulations in homogeneous permeability fields. The simulations reveal spatial variability in the weathering rate, multidimensional distribution of reactions zones, and the formation of rough weathering interfaces and corestones due to preferential flow. In the homogeneous fields and CRPFs, the domain-averaged weathering rate is initially constant as long as the weathering front is contained within the domain, reflecting equilibrium-controlled behavior. The behavior in the CRPFs was influenced by macrodispersion, with more spread-out weathering profiles, an earlier departure from the initial constant rate and longer persistence of weathering. DFN simulations exhibited a sustained time-dependence resulting from the formation of diffusion-controlled weathering fronts in matrix blocks, which is consistent with the shrinking core mechanism. A significant decrease in the domain-averaged weathering rate is evident despite high remaining mineral volume fractions, but the decline does not follow a math formula dependence, characteristic of diffusion, due to network scale effects and advection-controlled behavior near the inflow boundary. Finally, the DFN simulations also reveal relatively constant horizontally averaged weathering rates over a significant depth range, challenging the very notion of a weathering front.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Parametric Evaluation of SiC/SiC Composite Cladding with UO 2 Fuel for LWR Applications: Fuel Rod Interactions and Impact of Nonuniform Power Profile in Fuel Rod

SiC/SiC composites are candidates for accident tolerant fuel cladding in light water reactors. In the extreme nuclear reactor environment, SiC-based fuel cladding will be exposed to neutron damage, significant heat flux, and a corrosive environment. Here, to ensure reliable and safe operation of accident tolerant fuel cladding concepts such as SiC-based materials, it is important to assess thermo-mechanical performance under in-reactor conditions including irradiation and realistic temperature distributions. The effect of non-uniform dimensional changes caused by neutron irradiation with spatially varying temperatures, along with the closing of the fuel–cladding gap, on the stress development in the cladding over the course of irradiation were evaluated. The effect of non-uniform circumferential power profile in the fuel rod on the mechanical performance of the cladding is also evaluated. These analyses have been performed using the BISON fuel performance modeling code and the commercial finite element analysis code Abaqus. A constitutive model is constructed and solved numerically to predict the stress distribution in the cladding under normal operating conditions. The dependence of dimensions and thermophysical properties on irradiation dose and temperature has been incorporated into the models. Initial scoping results from parametric analyses provide time varying stress distributions in the cladding as well as the interaction of fuel rod with the cladding under different conditions of initial fuel rod-cladding gap and linear heat rate. It is found that a non-uniform circumferential power profile in the fuel rod may cause significant lateral bowing in the cladding, and motivates further analysis and evaluation.

Accident tolerant fuel↗

DOE OSTI2017

Impact of initial catalyst form on the 3D structure and performance of ball-milled Ni-catalyzed MgH 2 for hydrogen storage

Although it has been shown that the hydrogen storage kinetics of metal hydrides can be significantly improved by the addition of transition metal-based catalysts, relatively little attention has been paid to the impact that the form in which these catalysts are introduced during synthesis has on the resulting structure and how this alters performance. Two mixtures of MgH 2 doped with Ni were prepared via high-energy ball-milling under identical conditions, one using a pure Ni nanopowder catalyst and the other using anhydrous NiCl 2 . The resulting Ni catalyst particles of the NiCl 2 -doped material were 10-100 times smaller, as well as more uniform in size and shape. Electron tomography revealed that the additive form also altered its incorporation and 3D spatial distribution, with Ni particles limited to the outer surface in the NiCl 2 -doped case. The significantly lower desorption performance measured in the NiCl 2 -doped material is attributed to regions of MgCl 2 acting as barriers between the MgH 2 and Ni, hindering the ability of the latter to effectively catalyze the reactions. Finally, this work demonstrates the hazards in assuming different catalyst forms produce similar final structures and highlights the potential of catalyst form as a synthesis tool for optimizing the material structure and performance.

08 HYDROGEN↗

NASA NTRS1993

Tracking capabilities of SPADs for laser ranging

The spatial sensitivity of Single-Photon Avalanche Diodes (SPADs) can be exploited in laser ranging measurements to finely tune the laser spot in the center of the detector sensitive area. We report the performance of a SPAD with l00 micron diameter. It features a time resolution better than 80 ps rms when operated 4V above V(b) at minus 30 C, and a spatial sensitivity better than 20 microns to radial displacements of the laser spot. New SPAD structures with auxiliary delay detectors are proposed. These improved devices could allow a two dimensional sensitivity, that could be employed for the design of pointing servos.

Zappa, F.↗

NASA NTRS1978

Onboard processing for future space-borne imaging systems

There is a strong rationale for increasing the rate of information return from imaging class experiments aboard both terrestrial and planetary spacecraft. Future imaging systems will be designed with increased spatial resolution, broader spectral range and more spectral channels (or higher spectral resolution). The data rate implied by these improved performance characteristics can be expected to grow more rapidly than the projected telecommunications capability. One solution to this dilemma is the use of improved onboard data processing. The use of onboard classification processing in a multispectral imager can result in orders of magnitude increase in information transfer for very specific types of imaging tasks. Several of these processing functions are included in the conceptual design of an Infrared Multispectral Imager which would map the spatial distribution of characteristic geologic features associated with deposits of economic minerals.

Wellman, J. B.↗

DOE OSTI2020

Nematic transitions in iron pnictide superconductors imaged with a quantum gas

The Scanning Quantum Cryogenic Atom Microscope (SQCRAMscope) uses an atomic Bose–Einstein condensate to measure magnetic fields emanating from solid-state samples. The quantum sensor does so with unprecedented d.c. sensitivity at micrometre resolution, from room to cryogenic temperatures. An additional advantage of the SQCRAMscope is the preservation of optical access to the sample so that magnetometry imaging of, for example, electron transport may be performed in concert with other imaging techniques. Here, we apply this multimodal imaging capability to the study of nematicity in iron pnictide high-temperature superconductors, where the relationship between electronic and structural symmetry breaking resulting in a nematic phase is under debate. We combine the SQCRAMscope with an in situ microscope that measures optical birefringence near the surface. This enables simultaneous and spatially resolved detection of both bulk and near-surface manifestations of nematicity via transport and structural deformation channels, respectively. By performing local measurements of emergent resistivity anisotropy in iron pnictides, we observe sharp, nearly concurrent transport and structural transitions. More broadly, these measurements demonstrate the SQCRAMscope’s ability to reveal important insights into the physics of complex quantum materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Performance of Pixel and Strip AC-LGAD Sensors with Different Design Parameter on a 120 GeV Proton Beam

We present the results of an extensive evaluation of strip and pixel AC-LGAD sensors tested with a 120 GeV proton beam, focusing on the influence of design parameters on the sensor temporal and spatial resolutions. Results show that reducing the thickness of pixel sensors significantly enhances their time resolution, with 20- μm-thick sensors achieving around 20 ps. Uniform performance is attainable with optimized sheet resistance, making these sensors ideal for future timing detectors. Conversely, 20-μm-thick strip sensors exhibit higher jitter than similar pixel sensors, negatively impacting time resolution, despite reduced Landau fluctuations with respect to the 50-μm-thick versions. Additionally, it is observed that a low resistivity in strip sensors limits signal size and time resolution, whereas higher resistivity improves performance. This study highlights the importance of tuning the n+ sheet resistance and suggests that further improvements should target specific applications like the Electron-Ion Collider or other future collider experiments. In addition, the detailed performance of four AC-LGADs sensor designs is reported as examples of possible candidates for specific detector applications. These advancements position AC-LGADs as promising candidates for future 4D tracking systems, pending the development of specialized readout electronics.

Lara, Carlos Pérez↗

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