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NASA NTRS2010

Establishing a Modern Ground Network for Space Geodesy Applications

Ground-based networks of co-located space-geodesy techniques (VLBI, SLR, GLASS, DORIS) are the basis for the development and maintenance of the :International Terrestrial deference Frame (ITRE), which is the basis for our metric measurements of global change. The Global Geodetic Observing System (GGOS) within the International Association of Geodesy has established a task to develop a strategy to design, integrate and maintain the fundamental geodetic network and supporting infrastructure in a sustainable way to satisfy the long-term requirements for the reference frame. The GGOS goal is an origin definition at I mm or better and a temporal stability on the order of 0.1 mm/y, with similar numbers for the scale and orientation components. These goals are based on scientific requirements to address sea level rise with confidence. As a first step, simulations focused on establishing the optimal global SLR and VLBI network, since these two techniques alone are sufficient to define the reference frame. The GLASS constellations will then distribute the reference frame to users anywhere on the Earth. Using simulated data to be collected by the future networks, we investigated various designs and the resulting accuracy in the origin, scale and orientation of the resulting ITRF. We present here the results of extensive simulation studies aimed at designing optimal global geodetic networks to support GGOS science products. Current estimates are the network will require 24 - 32 globally distributed co-location sites. Stations in the near global network will require geologically stable sites witla good weather, established infrastructure, and local support and personnel. EGOS will seek groups that are interested in participation. GGOS intends to issues a Call for Participation of groups that would like to take part in the network implementation and operation_ Some examples of integrated stations currently in operation or under development will be presented. We will examine necessary conditions and challenges in designing a co-location station.

Pearlman, M.↗

NASA NTRS2020

Using Two-Stream Theory to Capture Fluctuations of Satellite-Perceived TOA SW Radiances Reflected from Clouds Over Ocean

Shortwave (SW) fluxes estimated from broadband radiometry rely on empirically gathered and hemispherically resolved fields of outgoing top-of-atmosphere (TOA) radiances. This study aims to provide more accurate and precise fields of TOA SW radiances reflected from clouds over ocean by introducing a novel semiphysical model predicting radiances per narrow sun-observer geometry. This model was statistically trained using CERES-measured radiances paired with MODIS-retrieved cloud parameters as well as reanalysis-based geophysical parameters. By using radiative transfer approximations as a framework to ingest the above parameters, the new approach incorporates cloud-top effective radius and above-cloud water vapor in addition to traditionally used cloud optical depth, cloud fraction, cloud phase, and surface wind speed. A two-stream cloud albedo – serving to statistically incorporate cloud optical thickness and cloud-top effective radius – and Cox–Munk ocean reflectance were used to describe an albedo over each CERES footprint. Effective-radius-dependent asymmetry parameters were obtained empirically and separately for each viewing-illumination geometry. A simple equation of radiative transfer, with this albedo and attenuating above-cloud water vapor as inputs, was used in its log-linear form to allow for statistical optimization. We identified the two-stream functional form that minimized radiance residuals calculated against CERES observations and outperformed the state-of-the-art approach for most observer geometries outside the sun-glint and solar zenith angles between 20 and 70∘, reducing the median SD of radiance residuals per solar geometry by up to 13.2 % for liquid clouds, 1.9 % for ice clouds, and 35.8 % for footprints containing both cloud phases. Geometries affected by sun glint (constituting between 10 % and 1 % of the discretized upward hemisphere for solar zenith angles of 20 and 70∘, respectively), however, often showed weaker performance when handled with the new approach and had increased residuals by as much as 60 % compared to the state-of-the-art approach. Overall, uncertainties were reduced for liquid-phase and mixed-phase footprints by 5.76 % and 10.81 %, respectively, while uncertainties for ice-phase footprints increased by 0.34 %. Tested for a variety of scenes, we further demonstrated the plausibility of scene-wise predicted radiance fields. This new approach may prove useful when employed in angular distribution models and may result in improved flux estimates, in particular dealing with clouds characterized by small or large droplet/crystal sizes.

Two- stream theory↗

DOE OSTI2024

Evaluation of Rooftop Solar Potential in Chernihiv and Lviv, Ukraine, and Efficacy of High-Resolution 3D Data Digital Twins [Slides]

NREL evaluated rooftop solar photovoltaic (PV) siting opportunities in the cities of Chernihiv and Lviv, Ukraine, leveraging very-high-resolution 3D elevation data to calculate technical potential. The study assessed total rooftop solar capacity and annual energy production. In Chernihiv and Lviv, 116,503 buildings were analyzed for their rooftop solar potential. The buildings in the study areas include a mixture of residential, commercial, and industrial buildings, characterized by diverse roof shapes and sizes. The total estimated rooftop solar capacity is 332 MWDC in Chernihiv and 873 MWDC in Lviv with annual energy production up to 376.2 GWhDC in Chernihiv and 995.5 GWhDC in Lviv. This accounting provides a clear estimate of the potential rooftop solar installations that could be realized under optimal conditions, considering both technical constraints and the geographic distribution of available rooftop space. Related to Russia's invasion of Ukraine, NREL estimates loss from buildings damaged or destroyed of 2,754 buildings, 20.15 MWDC of capacity, and 22,869 MWhDC of annual energy production in Chernihiv as well as a loss of 1,316 buildings, 34.49 MWDC of capacity, and 39,369 MWhDC of annual energy production in Lviv. The study also assessed the feasibility of adapting this methodology on a national scale using either simulated digital surface models (DSMs) or a digital twin approach. While the very-high-resolution DSM provided more precise results, the simulated DSM demonstrated reasonable accuracy for broader applications in modeling aggregated distributed solar supply. The feasibility of using a digital twin for Ukraine's national rooftop solar potential is considered promising, with certain limitations in areas with highly variable building stock and heavy war damage.

14 SOLAR ENERGY↗

DOE OSTI2020

Sputter-deposited low-stress boron carbide films

Significant challenges have been faced in the manufacturing of low-stress B 4 C films. Here, we demonstrate a set of process parameters for direct-current magnetron sputter deposition of smooth, high-purity, amorphous B 4 C films with near-zero total residual stress and with thicknesses up to 10 μm. Films are characterized by a combination of high-energy ion scattering, x-ray diffraction, electron microscopy, and stress measurements based on substrate curvature. In order to facilitate the process transfer to other sputtering geometries, the favorable deposition conditions are correlated with distributions of landing energies and incident angles of depositing species estimated by Monte Carlo simulations of ballistic collisions and gas phase atomic transport. Based on such simulations, a decrease in compressive stress with increasing Ar working gas pressure is attributed to the corresponding broadening of the angular distribution of depositing species and associated atomic shadowing effects.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Modeling the small-scale deposition of snow onto structured Arctic sea ice during a MOSAiC storm using snowBedFoam 1.0.

The remoteness and extreme conditions of the Arctic make it a very difficult environment to investigate. In these polar regions covered by sea ice, the wind is relatively strong due to the absence of obstructions and redistributes a large part of the deposited snow mass, which complicates estimates for precipitation hardly distinguishable from blowing or drifting snow. Moreover, the snow mass balance in the sea ice system is still poorly understood, notably due to the complex structure of its surface. Quantitatively assessing the snow distribution on sea ice and its connection to the sea ice surface features is an important step to remove the snow mass balance uncertainties (i.e., snow transport contribution) in the Arctic environment. In this work we introduce snowBedFoam 1.0., a physics-based snow transport model implemented in the open-source fluid dynamics software OpenFOAM. We combine the numerical simulations with terrestrial laser scan observations of surface dynamics to simulate snow deposition in a MOSAiC (Multidisciplinary Drifting Observatory for the Study of Arctic Climate) sea ice domain with a complicated structure typical for pressure ridges. The results demonstrate that a large fraction of snow accumulates in their vicinity, which compares favorably against scanner measurements. However, the approximations imposed by the numerical framework, together with potential measurement errors (precipitation), give rise to quantitative inaccuracies, which should be addressed in future work. The modeling of snow distribution on sea ice should help to better constrain precipitation estimates and more generally assess and predict snow and ice dynamics in the Arctic.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Maximum Likelihood Estimation: Some Basics

The maximum likelihood estimation is a general estimation procedure. It is often compared to estimation procedures like the ordinary least squares regression or generalized method of moments, to name a few. We discuss some basics about the maximum likelihood estimation, its advantages and disadvantages, and provide an example application to a gamma distribution function.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Maximum Likelihood Estimation: Some Basics

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2019

Improvements in the Uncertainty Model in the Goddard Institute for Space Studies Surface Temperature (GISTEMP) Analysis

We outline a new and improved uncertainty analysis for the Goddard Institute of Space Studies (GISS) Surface Temperature product version 4 (GISTEMP v4). Historical spatial variations in surface temperature anomalies are derived from historical weather station data and ocean data from ships, buoys and other sensors. Uncertainties arise from measurement uncertainty, changes in spatial coverage of the station record, and systematic biases due to technology shifts and land cover changes. Previously published uncertainty estimates for GISTEMP included only the effect of incomplete station coverage. Here, we update this term using currently available spatial distributions of source data, state‐of‐the‐art reanalyses and incorporate independently derived estimates for ocean data processing, station homogenization and other structural biases. The resulting 95% uncertainties are near 0.05° C in the global annual mean for the last 50 years, and increase going back further in time reaching 0.15° C in 1880. In addition, we quantify the benefits and inherent uncertainty due to the GISTEMP interpolation and averaging method. We use the total uncertainties to estimate the probability for each record year in the GISTEMP to actually be the true record year (to that date), and conclude with 86% likelihood that 2016 was indeed the hottest year of the instrumental period (so far).

Nathan J L Lenssen↗

NASA NTRS1980

The estimation of the propagation delay through the troposphere from microwave radiometer data

The uncertainity in propagation delay estimates is due primarily to tropospheric water, the total amount and vertical distribution of which is variable. Because water vapor both delays and attenuates microwave signals, the propagation delay, or wet path length, can be estimated from the microwave brightness temperature near the 22.235 GHz transition of water vapor. The data from a total of 240 radiosonde launches taken simultaneously were analyzed. Estimates of brightness temperature at 19 and 22 GHz and wet path length were made from these data. The wet path length in the zenith direction could be estimated from the surface water vapor density to an accuracy of 5 cm for the summer data and 2 cm for winter data. Using the brightness temperatures, the wet path could be estimated to an accuracy of 0.3 cm. Two dual frequency radiometers were refurbished in order to test these techniques. These radiometers were capable of measuring the difference in the brightness temperature at 30 deg elevation angle and at the zenith to an accuracy of about 1 K. In August 1975, 45 radiosondes were launched over an 11 day period. Brightness temperature measurements were made simultaneously at 19 and 22 GHz with the radiometers. The rms error for the estimation of wet path length from surface meteorological parameters was 3.2 cm, and from the radiometer brightness temperatures, 1.5 cm.

Moran, J. M.↗

NASA NTRS2023

Inland Water Greenhouse Gas Budgets for RECCAP2: 2. Regionalization and Homogenization of Estimates

Inland waters are important emitters of the greenhouse gasses (GHGs) carbon dioxide (CO 2 ), methane (CH 4 ), and nitrous oxide (N 2 O) to the atmosphere. In the framework of the 2 nd phase of the REgional Carbon Cycle Assessment and Processes (RECCAP-2) initiative, we synthesize existing estimates of GHG emissions from streams, rivers, lakes and reservoirs, and homogenize them with regard to underlying global maps of water surface area distribution and the effects of seasonal ice cover. We then produce regionalized estimates of GHG emissions over 10 extensive land regions. According to our synthesis, inland water GHG emissions have a global warming potential of an equivalent emission of 13.5 (9.9-20.1) and 8.3 (5.7-12.7) Pg CO 2 -eq. yr -1 at a 20 and 100 year horizon (GWP 20 and GWP 100 ), respectively. Contributions of CO 2 dominate GWP100, with rivers being the largest emitter. For GWP 20 , lakes and rivers are equally important emitters, and the warming potential of CH4 is more important than that of CO2. Contributions from N 2 O are about two orders of magnitude lower. Normalized to the area of RECCAP-2 regions, S-America and SE-Asia show the highest emission rates, dominated by riverine CO 2 emissions.

Ronny Lauerwald↗

DOE OSTI2021

A Novel Data-based Stochastic Distribution Control for Non-Gaussian Stochastic Systems

In this note, we present a novel data-based approach to investigate the non-Gaussian stochastic distribution control problem. As the motivation of this note, the existing methods have been summarised regarding to the drawbacks, for example, neural network weights training for unknown stochastic distribution and so on. To overcome these disadvantages, a new transformation for dynamic probability density function is given by kernel density estimation using interpolation. Based upon this transformation, a representative model has been developed while the stochastic distribution control problem has been transformed into an optimisation problem. Then, data-based direct optimisation and identification-based indirect optimisation have been proposed. In addition, the convergences of the presented algorithms are analysed and the effectiveness of these algorithms has been evaluated by numerical examples. In summary, the contributions of this note are as follows: 1) a new data-based probability density function transformation is given; 2) the optimisation algorithms are given based on the presented model; and 3) a new research framework is demonstrated as the potential extensions to the existing stochastic distribution control.

42 ENGINEERING↗

NASA NTRS1977

Distribution and flux of micrometeoroids

The mass distribution, flux, and distribution in space of the micrometeoroid complex at 1 AU are estimated on the basis of data from Apollo 17 rocks and recent calibrations of solar-flare track-production rates. It is found that the size frequency distribution of microcraters on lunar rocks suggests a bimodal mass distribution of micrometeoroids, but the precise form of the curve requires further definition, particularly insofar as the degree of depletion of particles producing craters 10 to 100 microns in diameter is concerned. Variations in slope with crater-diameter or particle-mass increments are shown to indicate that different processes affect one or more particle populations. Fluxes corresponding to varied lunar surface orientation and residence time are calculated, but no striking difference is observed between the flux of submicron-diameter particles with orbits in the plane of the ecliptic and fluxes of particles with orbits normal to the plane in the solar apex direction.

Morrison, D. A.↗

DOE OSTI2022

DIGS: deep inference of galaxy spectra with neural posterior estimation

Abstract With the advent of billion-galaxy surveys with complex data, the need of the hour is to efficiently model galaxy spectral energy distributions (SEDs) with robust uncertainty quantification. The combination of simulation-based inference (SBI) and amortized neural posterior estimation (NPE) has been successfully used to analyse simulated and real galaxy photometry both precisely and efficiently. In this work, we utilise this combination and build on existing literature to analyse simulated noisy galaxy spectra. Here, we demonstrate a proof-of-concept study of spectra that is (a) an efficient analysis of galaxy SEDs and inference of galaxy parameters with physically interpretable uncertainties; and (b) amortized calculations of posterior distributions of said galaxy parameters at the modest cost of a few galaxy fits with Markov chain Monte Carlo (MCMC) methods. We utilise the SED generator and inference framework Prospector to generate simulated spectra, and train a dataset of 2 × 10 6 spectra (corresponding to a five-parameter SED model) with NPE. We show that SBI—with its combination of fast and amortized posterior estimations—is capable of inferring accurate galaxy stellar masses and metallicities. Our uncertainty constraints are comparable to or moderately weaker than traditional inverse-modelling with Bayesian MCMC methods (e.g. 0.17 and 0.26 dex in stellar mass and metallicity for a given galaxy, respectively). We also find that our inference framework conducts rapid SED inference (0.9–1.2 × 10 5 galaxy spectra via SBI/NPE at the cost of 1 MCMC-based fit). With this work, we set the stage for further work that focuses of SED fitting of galaxy spectra with SBI, in the era of JWST galaxy survey programs and the wide-field Roman Space Telescope spectroscopic surveys.

spectroscopy↗

DOE OSTI2023

Robust neural network-enhanced estimation of local primordial non-Gaussianity

When applied to the nonlinear matter distribution of the universe, neural networks have been shown to be very statistically sensitive probes of cosmological parameters, such as the linear perturbation amplitude σ 8 . However, when used as a “black box,” neural networks are not robust to baryonic uncertainty. We propose a robust architecture for constraining primordial non-Gaussianity f NL , by training a neural network to locally estimate σ 8 , and correlating these local estimates with the large-scale density field. We apply our method to N-body simulations, and show that σ⁡(f NL ) is 3.5 times better than the constraint obtained from a standard halo-based approach. Finally, we show that our method has the same robustness property as large-scale halo bias: baryonic physics can change the normalization of the estimated f NL , but cannot change whether f NL is detected.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Learning robust parameter inference and density reconstruction in flyer plate impact experiments

Estimating physical parameters or material properties from experimental observations is a common objective in many areas of physics and material science. In many experiments, especially in shock physics, radiography is the primary means of observing the system of interest. However, radiography does not provide direct access to key state variables, such as density, which prevents the application of traditional parameter estimation approaches. Here we focus on flyer plate impact experiments on porous materials, and resolving the underlying parameterized equation of state (EoS) and crush porosity model parameters given radiographic observation(s). We use machine learning as a tool to demonstrate with high confidence that using only high impact velocity data does not provide sufficient information to accurately infer both EoS and crush model parameters, even with fully resolved density fields or a dynamic sequence of images. We thus propose an observable data set consisting of low and high impact velocity experiments/simulations that capture different regimes of compaction and shock propagation, and proceed to introduce a generative machine learning approach which produces a posterior distribution of physical parameters directly from radiographs. We demonstrate the effectiveness of the approach in estimating parameters from simulated flyer plate impact experiments, and show that the obtained estimates of EoS and crush model parameters can then be used in hydrodynamic simulations to obtain accurate and physically admissible density reconstructions. Finally, we examine the robustness of the approach to model mismatches, and find that the learned approach can provide useful parameter estimates in the presence of out-of-distribution radiographic noise and previously unseen physics, thereby promoting a potential breakthrough in estimating material properties from experimental radiographic images.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Markov Chain Monte Carlo Parameter Estimation of Deflagration Losses in a Rotating Detonation Engine

One of the practical challenges of the studies of rotating detonation engines (RDEs) is the direct estimation of losses from experimental measurements. This study attempts at resolving this limitation by combining a reduced order model (ROM) of the detonation wave characteristics with a Markov chain Monte Carlo parameter estimation framework. The model considers simple deflagration losses and the overall impact of deflagration on RDE performance. To evaluate this model, a Markov Chain Monte Carlo (MCMC) sampling technique was applied to estimate the loss parameters within the model for a set of conditions operated in hydrogen-air over a range of mass flow rates and equivalence ratios. The MCMC parameter estimation framework allowed for the determination of a posterior distribution of the loss parameters for each test condition, an examination of the correlation between the loss parameters and measured performance metrics of the RDE, and an uncertainty propagation of these parameters. The predicted model loss parameters were then compared to indirect experimental measurements of the deflagration combustion fractions to evaluating the validity of the approach, and shed light on the benefits and drawbacks of the model, measurement techniques, and the estimation framework.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS1978

Analysis of vector wind change with respect to time for Cape Kennedy, Florida

Multivariate analysis was used to determine the joint distribution of the four variables represented by the components of the wind vector at an initial time and after a specified elapsed time is hypothesized to be quadravariate normal; the fourteen statistics of this distribution, calculated from 15 years of twice-daily rawinsonde data are presented by monthly reference periods for each month from 0 to 27 km. The hypotheses that the wind component changes with respect to time is univariate normal, that the joint distribution of wind component change with respect to time is univariate normal, that the joint distribution of wind component changes is bivariate normal, and that the modulus of vector wind change is Rayleigh are tested by comparison with observed distributions. Statistics of the conditional bivariate normal distributions of vector wind at a future time given the vector wind at an initial time are derived. Wind changes over time periods from 1 to 5 hours, calculated from Jimsphere data, are presented. Extension of the theoretical prediction (based on rawinsonde data) of wind component change standard deviation to time periods of 1 to 5 hours falls (with a few exceptions) within the 95 percentile confidence band of the population estimate obtained from the Jimsphere sample data. The joint distributions of wind change components, conditional wind components, and 1 km vector wind shear change components are illustrated by probability ellipses at the 95 percentile level.

Adelfang, S. I.↗

DOE OSTI2019

Evaluating next-generation intensity–duration–frequency curves for design flood estimates in the snow-dominated western United States

Civil infrastructure such as culverts and bridges are commonly designed using precipitation-based intensity-duration-frequency (PREC-IDF) curves, which assume that the occurrence of precipitation is in the form of rainfall and immediately available for the rainfall-runoff process. In snow-dominated regions, where most winter precipitation occurs as snow that melts during spring to early summer, the use of standard PREC-IDF curves may lead to substantial underestimation of design floods and high failure risk of infrastructure. In this context, we developed next-generation IDF (NG-IDF) curves that characterize the actual water reaching the land surface (i.e., rainfall plus snowmelt) to enhance standard infrastructure design in snow-dominated regions. This study evaluates the performance of NG-IDF curves coupled with U.S. Department of Agriculture Technical Release 55 hydrologic model in estimating design floods for 246 snowy locations in different hydroclimate regimes of the western United States. Design flood estimates from a well-validated continuous simulation using a physics-based hydrologic model, the Distributed Hydrology Soil Vegetation Model (DHSVM), were used as the performance benchmark. Compared with the benchmark estimates, the standard PREC-IDF curves led to substantial errors in design flood estimates while the NG-IDF curves significantly reduced these errors. For example, the averaged error in the 50-year design flood estimates over the 246 locations was reduced from 31% with the use of PREC-IDF curves to 12% with the use of NG-IDF curves. Despite the different model structures, the single-event NG-IDF approach versus the continuous simulation DHSVM did not exhibit statistically significant differences in 91% of the 246 locations for the 50-year design flood estimates. We find this indicates a satisfactory performance of NG-IDF curves to estimate design flow under the conditions tested in the snow-dominated western United States. This article also presents technical suggestions and the limitations of infrastructure design using NG-IDF curves for regulatory agencies and practicing engineers.

54 ENVIRONMENTAL SCIENCES↗