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NASA NTRS2021

Chapter 6: Fundamental Satellite Precipitation Data Records

The observation and measurement of precipitation across a range of temporal and spatial scales is of great importance for many user requirements and applications. Precipitation is a key component of the global energy and water cycle moving water, and its’ associated energy, through the Earth system at relatively fine spatial and temporal scales. At the societal level, precipitation provides fresh water and moderates our climate, but can also be destructive through storms, droughts and spreading diseases. Monitoring and measuring the distribution and quantities of precipitation, across the Earth’s surface is therefore crucial to physical and social applications, together with changes in precipitation over time. While conventional surface instruments provide a reasonably long record, these tend to be confined to well populated land surface areas: most ocean regions and sparsely populated regions have few measurements. Satellite-based precipitation products can provide global measurements, although the timespan of such products is limited and the data record is comprised of different instruments that operate over different periods of record. The challenge of any climate record is to provide a consistent product over the full period of record. Many satellite precipitation products incorporate ancillary data, such as surface gauge data or model data, which may limit their utility in the validation or verification of other climate data records. This chapter provides a review of the importance of precipitation to the climate system, the basis of satellite precipitation retrievals, climate data records of satellite data for precipitation retrievals and the development of fundamental precipitation data records for climate applications.

Climate data record↗

NASA NTRS1998

Quantity, Revisited: An Object-Oriented Reusable Class

"Quantity", a prototype implementation of an object-oriented class, was developed for two reasons: to help engineers and scientists manipulate the many types of quantities encountered during routine analysis, and to create a reusable software component to for large domain-specific applications. From being used as a stand-alone application to being incorporated into an existing computational mechanics toolkit, "Quantity" appears to be a useful and powerful object. "Quantity" has been designed to maintain the full engineering meaning of values with respect to units and coordinate systems. A value is a scalar, vector, tensor, or matrix, each of which is composed of Value Components, each of which may be an integer, floating point number, fuzzy number, etc., and its associated physical unit. Operations such as coordinate transformation and arithmetic operations are handled by member functions of "Quantity". The prototype has successfully tested such characteristics as maintaining a numeric value, an associated unit, and an annotation. In this paper we further explore the design of "Quantity", with particular attention to coordinate systems.

Funston, Monica Gayle↗

DOE OSTI2026

Beyond interpolation: Physics-inspired gating transformers for extrapolating irradiation conditions to novel nuclear fuels

The qualification of advanced nuclear fuels relies on irradiation experiments in test reactors that emulate commercial conditions. Designing these tests requires accurate prediction of key irradiation quantities, particularly heat generation rate and burnup, yet obtaining them typically involves computationally expensive multi-step simulation workflows. We propose a physics-inspired gating transformer (PIGT) that integrates an inverse-square, distance-based attenuation into the encoder representation to bias attention toward physically relevant spatial relationships while retaining data-driven flexibility. Using MiniFuel irradiation data from the High Flux Isotope Reactor at Oak Ridge National Laboratory, we benchmark against ensemble methods, feedforward and recurrent networks, convolutional models, and standard transformers. While baseline models perform well under interpolation, they exhibit a pronounced generalization gap when evaluated on fuels not included in the training set. The proposed model consistently improves extrapolative accuracy and stability, yielding the strongest performance on unseen fuel configurations. These results indicate that a lightweight physics structure embedded within attention mechanisms can substantially improve robustness, enabling more reliable surrogate predictions to accelerate the design of nuclear fuel irradiation experiments.

Fuel qualification↗

DOE OSTI2023

Three-pion effects in $K^0-\bar{K}^0$ mixing

The rate of mixing between a neutral kaon and an anti-kaon ($K^0$-$\bar{K}^0$) is given, in part, by a long-range matrix element, defined with two insertions of the weak Hamiltonian separated by physical, Minkowski time evolution. For physical quark masses, the kaon mass lies above the two- and three-pion thresholds and, as a result, this long-range matrix element receives contributions from intermediate on-shell $2\pi$ and $3\pi$ states. These contributions cannot easily be captured in a finite Euclidean spacetime, meaning that such matrix elements are not directly accessible via lattice QCD. In this talk, we present a strategy for combining quantities that can be extracted in numerical lattice QCD calculations in order to reproduce the physical, infinite-volume long-range amplitude for $K^0-\bar{K}^0$. The key novelty relative to published work is that we fully include the effects of three-particle states that were previously neglected. The strategy is built on existing formalism for long-range matrix elements with two-particle intermediate states, together with the relativistic-field-theory finite-volume formalism for extracting three-hadron weak decays.

Jackura, Andrew↗

DOE OSTI2022

A Physics-Based DNI Model for Advancing Solar Resource Assessment and Forecasting: Preprint

Direct Normal Irradiance (DNI) is one of the most used quantities to quantify the magnitude of solar energy resource. The concept of DNI is often interpreted differently for ground measurements and solar forecasting by numerical weather prediction (NWP) models, leading to substantial bias during evaluation of DNI forecasts especially under cloudy-sky conditions. To eliminate the bias, we use the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI) to provide a physics-based solution of solar radiation in the circumsolar region. The FARMS-DNI is implemented in the Weather Research and Forecasting model with solar extensions (WRF-Solar) to forecast day-ahead DNI in the north America. By comparing with conventional predictions from WRF-Solar and satellite observations from the National Solar Radiation Data Base (NSRDB), we found significant improvements in our prediction of DNI.

DNI↗

DOE OSTI2019

Nuclear Data – Benchmarking 19 F(α,n) Yield Data for Nuclear Safeguards

Fluorine compounds of U and Pu are ubiquitous in the nuclear fuel cycle, so F(α,n) neutrons are an important signature and quantitative source term that needs to be understood for physics-based interpretation of nondestructive assay measurements. Historically there have been large differences in the basic nuclear data for this reaction reported by various groups. UF 6 is the most prominent material in the nuclear fuel cycle, with the potential for short-term production into weaponizable form. Verification of bulk quantities, natural feed, depleted tails, and especially low enriched product in cylinders is particularly important. The principal physical measurement is neutron counting. For enriched material, the 19 F(α,n) reaction, driven by 234U, is the dominant source of neutrons. Basic nuclear data, cross sections, needed to calculate the yield, as well as information on the source spectrum for sensitivity studies are sparse and highly discrepant. This limits defensible physics-based performance evaluations. The same applies to holdup and criticality studies in which hydrated uranyl fluoride is the material of interest which accumulates in enrichment facilities. In the future we anticipate that the physics community will have made improved accelerator-based measurements and undertaken a more thorough evaluation and adjustment of all relevant available data. But in the short term, this does not help the safeguards community face the pressing real-world nondestructive assay requirements. To address this need, we have performed quality neutron measurements on UF6 materials using well-known material and high-capability neutron counters. A robust determination of the n/s/g of 234 U in UF 6 was generated along with a scientifically defensible uncertainty analysis. The weighted average value of the neutron yield is 509 n/s/g 234 U with a random uncertainty of approximately 1%. Dominant sources of systematic uncertainty are in the efficiency determination where we incur approximately 1.1% relative standard deviation associated with the 252 Cf reference source and about 0.9% uncertainty coming from the uncertainty in the F(α,n) spectrum. The stated uncertainties are an order of magnitude better than current data based on accelerator data reported in the literature. Aside from being of immediate and direct use by the safeguards community, this result will serve as an enduring integral benchmark for subsequent data evaluations that will need to match it in order to be credible. In addition to yield normalization, safeguards users of F(α,n) data also need guidance of the neutron emission spectrum. We have generated a neutron emission using a modified version of the well-known SOURCES 4C code, updated with new alpha stopping power coefficients, and a blended microscopic cross section data set. The yield and spectra are available in a form that MCNP users can readily use.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Exploring 2 + 2 answers to 3 + 1 questions

We explore potential uses of physics formulated in Kleinian (i.e. 2 + 2) signature spacetimes as a tool for understanding properties of physics in Lorentzian (i.e. 3 + 1) signature. Much as Euclidean (i.e. 4 + 0) signature quantities can be used to formally construct the ground state wavefunction of a Lorentzian signature quantum field theory, a similar analytic continuation to Kleinian signature constructs a state of low particle flux in the direction of analytic continuation. There is also a natural supersymmetry algebra available in 2 + 2 signature, which serves to constrain the structure of correlation functions. Spontaneous breaking of Lorentz symmetry can produce various [Formula: see text] supersymmetry algebras that in 3 + 1 signature correspond to nonsupersymmetric systems. We speculate on the possible role of these structures in addressing the cosmological constant problem.

Physics↗

DOE OSTI2022

The tiny (g-2) muon wobble from small-μ supersymmetry

A new measurement of the muon anomalous magnetic moment has been recently reported by the Fermilab Muon g-2 collaboration and shows a 4.2σ departure from the most precise and reliable calculation of this quantity in the Standard Model. Assuming that this discrepancy is due to new physics, we consider its relation with other potential anomalies, especially in the muon sector, as well as clues from the early universe. Here, we comment on new physics solutions discussed extensively in the literature in the past decades, to finally concentrate on a simple supersymmetric model that also provides a dark matter explanation. We show results for an interesting region of supersymmetric parameter space that can be probed at the high luminosity LHC and future colliders, while leading to values of (g μ – 2) consistent with the Fermilab and Brookhaven (g μ – 2) measurements. Such a parameter region can simultaneously realize a Bino-like dark matter candidate compatible with direct detection constraints for small to moderate values of the Higgsino mass parameter |μ|.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Computational techniques for the analysis of small signals in high-statistics neutrino oscillation experiments

The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

The anomalous magnetic moment of the muon in the Standard Model

We review the present status of the Standard Model calculation of the anomalous magnetic moment of the muon. This is performed in a perturbative expansion in the fine-structure constant α and is broken down into pure QED, electroweak, and hadronic contributions. The pure QED contribution is by far the largest and has been evaluated up to and including O(α 5 ) with negligible numerical uncertainty. The electroweak contribution is suppressed by (m μ / M W ) 2 and only shows up at the level of the seventh significant digit. It has been evaluated up to two loops and is known to better than one percent. Hadronic contributions are the most difficult to calculate and are responsible for almost all of the theoretical uncertainty. The leading hadronic contribution appears at O(α 2 ) and is due to hadronic vacuum polarization, whereas at O(α 3 ) the hadronic light-by-light scattering contribution appears. Given the low characteristic scale of this observable, these contributions have to be calculated with nonperturbative methods, in particular, dispersion relations and the lattice approach to QCD. The largest part of this review is dedicated to a detailed account of recent efforts to improve the calculation of these two contributions with either a data-driven, dispersive approach, or a first- principle, lattice-QCD approach. The final result reads $a$$^{SM}_{μ}$ = 116 591 810(43) × 10 −11 and is smaller than the Brookhaven measurement by 3.7σ. The experimental uncertainty will soon be reduced by up to a factor four by the new experiment currently running at Fermilab, and also by the future J-PARC experiment. Furthermore, this and the prospects to further reduce the theoretical uncertainty in the near future – which are also discussed here – make this quantity one of the most promising places to look for evidence of new physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Chaos in inhomogeneous neutrino fast flavor instability

In dense neutrino gases, the neutrino-neutrino coherent forward scattering gives rise to a complex flavor oscillation phenomenon not fully incorporated in simulations of neutron star mergers (NSM) and core collapse supernovae (CCSNe). Moreover, it has been proposed to be chaotic, potentially limiting our ability to predict neutrino flavor transformations in simulations. To address this issue, we explore how small flavor perturbations evolve in the nonlinear regime of the neutrino quantum kinetic equation within a narrow centimeter-scale region inside a NSM and a toy neutrino distribution. Our findings reveal that paths in the flavor state space of solutions with similar initial conditions diverge exponentially, exhibiting chaos. This inherent chaos makes the microscopic scales of neutrino flavor transformations unpredictable. However, the domain-averaged neutrino density matrix remains relatively stable, with chaos minimally affecting it. This particular property suggests that domain-averaged quantities remain reliable despite the exponential amplification of errors. Published by the American Physical Society 2024

Astronomy & Astrophysics↗

DOE OSTI2022

Machine Learning for the Discovery, Design, and Engineering of Materials

Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based models, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward ( a) the discovery of new materials through large-scale enumerative screening, ( b) the design of materials through identification of rules and principles that govern materials properties, and ( c) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design.

Chemistry↗

DOE OSTI2021

Temperature Effects on Droplet Oscillation Decay with Application to Fuel Property Measurement

Observation of oscillation decay in droplets has been shown to be an effective approach in determining physical properties such as viscosity and surface tension for emerging biofuels using μl quantities. Herein this work extends the approach to higher temperatures relevant to fuel injection conditions for internal combustion engines. Experiments are conducted that use high-resolution strobed imaging of moving, heated, μm-sized, fuel droplets to capture shape oscillation decay through image processing and analysis. Two fuels are investigated, iso-butanol and a primary reference fuel (PRF 84), which is a mixture of iso-octane and n-heptane. A piezoelectric droplet generator is used to generate a continuous train of single droplets, which are given an initial perturbation and subsequently undergo damped oscillations, captured using strobed imaging. Surface tension and viscosity calculated at four different temperatures ranging from 30°C-55°C using the frequency and decay time associated with the fundamental mode are found to be within ~ 10% of reference values obtained from the literature. Complementary numerical simulations are performed that utilize a volume-of-fluid approach to track transient droplet oscillation phenomena along with heat and mass transfer in a 2D axisymmetric domain. Simulations, where droplet size and temperature can be independently varied, capture an expected decrease in oscillation frequency and increase in decay time, with increase in fuel temperature. The simulations are further used to study the relative contribution to deviations in surface tension and viscosity predictions due to heat and mass transfer from the droplet, as well as viscous effects violating the inviscid flow assumption in the droplet oscillation theory. Mass loss effects are found to be negligible. Temperature change due to heat transfer is found to have the next highest sensitivity, particularly for the more volatile PRF 84, which has a lower viscosity and associated Ohnesorge number. For isobutanol, which has a higher viscosity and Ohnesorge number, viscous effects contribute the most to deviation in fuel property predictions.

09 BIOMASS FUELS↗

DOE OSTI2021

Modeling Kilonova Light Curves: Dependence on Nuclear Inputs

The mergers of binary neutron stars, as well as black hole–neutron star systems, are expected to produce an electromagnetic counterpart that can be analyzed to infer the element synthesis that occurred in these events. We investigate one source of uncertainties pertinent to lanthanide-rich outflows: the nuclear inputs to rapid neutron capture nucleosynthesis calculations. We begin by examining 32 different combinations of nuclear inputs: eight mass models, two types of spontaneous fission rates, and two types of fission daughter product distributions. We find that such nuclear physics uncertainties typically generate at least one order of magnitude uncertainty in key quantities such as the nuclear heating (one and a half orders of magnitude at 1 day post-merger), the bolometric luminosity (one order of magnitude at 5 days post-merger), and the inferred mass of material from the bolometric luminosity (factor of 8 when considering the 8–10 day region). Since particular nuclear processes are critical for determining the electromagnetic signal, we provide tables of key nuclei undergoing β-decay, α-decay, and spontaneous fission important for heating at different times, identifying decays that are common among the many nuclear input combinations.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1979

The extended mission of Viking

The extended mission of the Viking project emphasizing the study of weather on Mars is presented. Samples were acquired for the analysis of surface material, and investigations were made of the physical and magnetic properties of the surface, with orbiters and landers producing large quantities of photographs of the surface and of atmospheric phenomena. The operation and chronology of Viking Orbiter 1 and 2 and Viking Lander 1, including the Viking continuation, the survey, and the orbiter completion missions are discussed.

Snyder, C. W.↗

NASA NTRS1990

Diabatic forcing and initialization with assimilation of cloud and rain water in a forecast model: Methodology

The focus of this part of the investigation is to find one or more general modeling techniques that will help reduce the time taken by numerical forecast models to initiate or spin-up precipitation processes and enhance storm intensity. If the conventional data base could explain the atmospheric mesoscale flow in detail, then much of our problem would be eliminated. But the data base is primarily synoptic scale, requiring that a solution must be sought either in nonconventional data, in methods to initialize mesoscale circulations, or in ways of retaining between forecasts the model generated mesoscale dynamics and precipitation fields. All three methods are investigated. The initialization and assimilation of explicit cloud and rainwater quantities computed from conservation equations in a mesoscale regional model are examined. The physical processes include condensation, evaporation, autoconversion, accretion, and the removal of rainwater by fallout. The question of how to initialize the explicit liquid water calculations in numerical models and how to retain information about precipitation processes during the 4-D assimilation cycle are important issues that are addressed. The explicit cloud calculations were purposely kept simple so that different initialization techniques can be easily and economically tested. Precipitation spin-up processes associated with three different types of weather phenomena are examined. Our findings show that diabatic initialization, or diabatic initialization in combination with a new diabatic forcing procedure, work effectively to enhance the spin-up of precipitation in a mesoscale numerical weather prediction forecast. Also, the retention of cloud and rain water during the analysis phase of the 4-D data assimilation procedure is shown to be valuable. Without detailed observations, the vertical placement of the diabatic heating remains a critical problem.

Raymond, William H.↗

NASA NTRS1997

Selected Diagnostics for Microgravity Combustion Science

The evolution of our understanding of combustion phenomena occurring under reduced gravity conditions poses a continuing demand for diagnostic tools of increased sophistication. Existing methods have become insufficient to keep pace with emerging refinements in the underlying theories and predictive models. The coupling of physical mechanisms inherent in combustion systems mandates the simultaneous determination of numerous thermophysical quantities, principally temperature, velocity, and species concentrations. Observed differences attributable to variations in experimental configuration, fuels and diluents, and initial conditions enhance the difficulty of developing diagnostic methods suitable for the acquisition of the required data over the desired range of experimental parameters. Efforts to provide for ongoing diagnostic development supporting microgravity combustion science experiments are conducted within the Microgravity Science Division at the NASA-Lewis Research Center. The following describes recent results from several diagnostic development efforts conducted under this project.

Greenberg, Paul S.↗

DOE OSTI2026

Poisson Log-Normal Process for Count Data Prediction

Modeling count data is important in physics and other scientific disciplines, where measurements often involve discrete, non-negative quantities such as photon or neutrino detection events. Traditional parametric approaches can be trained to generate integer-count predictions but may struggle with capturing complex, non-linear dependencies often observed in the data. Gaussian process (GP) regression provides a robust non-parametric alternative to modeling continuous data; however, it cannot generate integer outputs. We propose the Poisson Log-Normal (PoLoN) process, a framework that employs GP to model Poisson log-rates. As in GP regression, our approach relies on the correlations between data points captured via GP kernel structure rather than explicit functional parameterizations. We demonstrate that the PoLoN predictive distribution is Poisson-LogNormal and provide an algorithm for optimizing kernel hyperparameters. Furthermore, we adapt the PoLoN approach to the problem of detecting weak localized signals superimposed on a smoothly varying background - a task of considerable interest in many areas of science and engineering. Our framework allows us to predict the strength, location and width of the detected signals. We evaluate PoLoN's performance using both synthetic and real-world datasets, including the open dataset from CERN which was used to detect the Higgs boson at the Large Hadron Collider. Our results indicate that the PoLoN process can be used as a non-parametric alternative for analyzing, predicting, and extracting signals from integer-valued data.

Saha, Anushka [Rutgers U., Piscataway]↗