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NASA NTRS2026

Changes in Arctic and Antarctic Sea-Ice Properties and Processes

Although sea ice covers ~9% of the world’s oceans, its presence, and absence, vastly alter the energetic balance of Earth’s climate. Because of geographic differences, Arctic and Antarctic sea ice have different dominant properties and processes, which produce different responses to natural variability and anthropogenic change. In this Review, we survey changes in sea-ice properties and processes of the Arctic and Antarctic environments. In the Arctic, the melt season has lengthened by ~7.4 days decade −1 (1979-2024). There has been a corresponding decrease in winter sea-ice thickness (1.6 m mean total over 1980-2023), summer surface albedo (0.03 decade −1 over 1979-2020), and spring snow depth (2.5 cm decade −1 over 1954-2024). In comparison, corresponding changes in the Antarctic are small overall due to contrasting regional and temporal trends. From a dynamic perspective, sea-ice motion in both hemispheres has increased, with 0.63 cm s −1 decade −1 in the Arctic (1978-2024) and 0.69 cm s −1 decade −1 in the Antarctic (1982-2024), whereas trends in polynya occurrence differ by region. Future research should prioritize sea-ice observations that can inform how the climate system responds to natural variability and anthropogenic change, be standardized to increase uptake by the broader scientific community, and target process-oriented interactions to support model development.

Melinda A Webster↗

DOE OSTI2022

Extraction of Deep Inelastic Cross Sections Using a 10.4 GeV Electron Beam and a Polarized Helium-3 Target

Experiment E12-06-121 at Jefferson Lab aims to do a precision measurement of the neutron spin structure function g2 using inclusive inelastic scattering of electrons over a large kinematic range of x and Q2. The third moment of the linear combination of the spin structure functions g1 and g2, d2, is one of the cleanest higher twist observables and contains information on quark-gluon correlations. It is connected to the \color polarizability or \color Lorentz force" of the nucleon. The experimental data taking was successfully conducted in Hall C using a longitudinally polarized electron beam with 10.4 GeV energy, and a polarized 3He gas target. The combination of the Super High Momentum Spectrometer (SHMS) and the High Momentum Spectrometer (HMS) allowed us to run the experiment for three constant Q2 values (3.0, 4.3, and 5.6 GeV2/c2) over a wide range of x (0:20 < x < 0:95) for the ?first time. To extract the neutron spin structure functions, precision determination of the unpolarized cross-sections and double spin asymmetries are required. The analysis of the deep inelastic scattering data and the results from the ?first measurements of unpolarized 3He cross-sections are reported in this work. The extracted cross-sections will be combined with the electron asymmetry results (ongoing work) to obtain the g2 and d2 of 3He. Finally, nuclear correction to the 3He results will have to be applied to extract g2 and d2 for the neutrons.

Roy, Murchhana↗

DOE OSTI2026

HydroDCM: Hydrological Domain-Conditioned Modulation for Cross-Reservoir Inflow Prediction

Deep learning models have shown promise in reservoir inflow prediction, yet their performance often deteriorates when applied to different reservoirs due to distributional differences, referred to as the domain shift problem. Domain generalization (DG) solutions aim to address this issue by extracting domain-invariant representations that mitigate errors in unseen domains. However, in hydrological settings, each reservoir exhibits unique inflow patterns, while some metadata beyond observations like spatial information exerts indirect but significant influence. This mismatch limits the applicability of conventional DG techniques to many-domain hydrological systems. To overcome these challenges, we propose HydroDCM, a scalable DG framework for cross-reservoir inflow forecasting. Spatial metadata of reservoirs is used to construct pseudo-domain labels that guide adversarial learning of invariant temporal features. During inference, HydroDCM adapts these features through light-weight conditioning layers informed by the target reservoir’s metadata, reconciling DG’s invariance with location-specific adaptation. Experiment results on 30 real-world reservoirs in the Upper Colorado River Basin demonstrate that our method substantially outperforms state-of-the-art DG baselines under many-domain conditions and remains computationally efficient.

Hu, Pengfei [ORNL] (ORCID:0009000367130950)↗

DOE OSTI2021

Ground-based temperature and humidity profiling: combining active and passive remote sensors

Abstract. Thermodynamic profiles in the planetary boundary layer (PBL) are important observations for a range of atmospheric research and operational needs. These profiles can be retrieved from passively sensed spectral infrared (IR) or microwave (MW) radiance observations or can be more directly measured by active remote sensors such as water vapor differential absorption lidars (DIALs). This paper explores the synergy of combining ground-based IR, MW, and DIAL observations using an optimal-estimation retrieval framework, quantifying the reduction in the uncertainty in the retrieved profiles and the increase in information content as additional observations are added to IR-only and MW-only retrievals. This study uses ground-based observations collected during the Perdigão field campaign in central Portugal in 2017 and during the DIAL demonstration campaign at the Atmospheric Radiation Measurement Southern Great Plains site in 2017. The results show that the information content in both temperature and water vapor is higher for the IR instrument relative to the MW instrument (thereby resulting in smaller uncertainties) and that the combined IR + MW retrieval is very similar to the IR-only retrieval below 1.5 km. However, including the partial profile of water vapor observed by the DIAL increases the information content in the combined IR + DIAL and MW + DIAL water vapor retrievals substantially, with the exact impact vertically depending on the characteristics of the DIAL instrument itself. Furthermore, there is a slight increase in the information content in the retrieved temperature profile using the IR + DIAL relative to the IR-only; this was not observed in the MW + DIAL retrieval.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1985

Temporal variations of the microwave signatures of sea ice during the late spring and early summer near Mould Bay, NWT

It has been shown that passive microwave imagery obtained from satellite-borne sensors provides an important basis for the study of the polar regions. Because of the optical thinness of high-latitude clouds at microwave frequencies, radiometry can provide all-weather all-time observing capability. However, in order to clarify observational uncertainties and investigate the information content of passive microwave imagery, detailed ground-based observations are needed. Multifrequency data are also required to utilize the strong spectral dependence of both the dielectric properties of liquid water and volume scattering. The present investigation has the aim to provide information of the considered type for the calibration and interpretation of satellite observations of the Arctic during the summer season. Attention is given to instruments and calibration, the field program and the state of the ice cover, and the results.

Grenfell, T. C.↗

NASA NTRS1971

NASA directory of observation station locations, volume 2

The directory documents geodetic information for NASA tracking stations and observation stations in the NASA Geodetic Satellites Program, including stations participating in the National Geodetic Satellite Program. Station positions of these facilities are given on local or preferred major datums, and on the Modified Mercury Datum 1968. A geodetic data sheet is provided for each station, giving the position of the station and describing briefly how it was established. Geodetic positions and geocentric coordinates of these stations are tabulated on local or major geodetic datums, and on selected world geodetic systems when available information permits.

Source record↗

NASA NTRS2019

Soil Moisture Data Assimilation

Accurate knowledge of soil moisture at the continental scale is important for improving predictions of weather, agricultural productivity and natural hazards, but observations of soil moisture at such scales are limited to indirect measurements, either obtained through satellite remote sensing or from meteorological networks. Land surface models simulate soil moisture processes, using observation-based meteorological forcing data, and auxiliary information about soil, terrain and vegetation characteristics. Enhanced estimates of soil moisture and other land surface variables, along with their uncertainty, can be obtained by assimilating observations of soil moisture into land surface models. These assimilation results are of direct relevance for the initialization of hydro-meteorological ensemble forecasting systems. The success of the assimilation depends on the choice of the assimilation technique, the nature of the model and the assimilated observations, and, most importantly, the characterization of model and observation error. Systematic differences between satellite-based microwave observations or satellite-retrieved soil moisture and their simulated counterparts require special attention. Other challenges include inferring root-zone soil moisture information from observations that pertain to a shallow surface soil layer, propagating information to unobserved areas and downscaling of coarse information to finer-scale soil moisture estimates. This chapter summarizes state-of-the-art solutions to these issues with conceptual data assimilation examples, using techniques ranging from simplified optimal interpolation to spatial ensemble Kalman filtering. In addition, operational soil moisture assimilation systems are discussed that support numerical weather prediction at ECMWF and provide value-added soil moisture products for the NASA Soil Moisture Active Passive mission.

radar backscatter↗

NASA NTRS1986

High-spectral resolution solar microwave observations

The application of high-spectral resolution microwave observations to the study of solar activity is discussed with particular emphasis on the frequency dependence of microwave emission from solar active regions. A shell model of gyroresonance emission from active regions is described which suggest that high-spectral resolution, spatially-resolved observations can provide quantitative information about the magnetic field distribution at the base of the corona. Corresponding observations of a single sunspot with the Owens Valley frequency-agile interferometer at 56 frequencies between 1.2 and 14 Ghs are presented. The overall form of the observed size and brightness temperature spectra was consistent with expectations based on the shell model, although there were differences of potential physical significance. The merits and weaknesses of microwave spectroscopy as a technique for measuring magnetic fields in the solar corona are briefly discussed.

Hurford, G. J.↗

NASA NTRS1986

SIR-B measurements and modeling of vegetation

A summary is presented of the results derived from analysis of six SIR-B data takes over an agricultural test site in west central Illinois. The first part describes the procedure used to calibrate the SIR-B imagery, the second part pertains to the observed radar response to soil moisture content, and the last part examines the information derivable from multiangle observations.

Ulaby, Fawwaz T.↗

NASA NTRS1993

Optical specification of time-to-passage: Observers' sensitivity to global tau

Despite its general mathematical formulation, most empirical work on the visual perception of tau (defined as a quantity divided by its temporal derivative) has focused on the case of direct approach, with tau defined as image angle/rate of expansion. Empirical investigators tend to generalize image size analyses to off-axis approaches. However, this generalization is inappropriate for all but a few classes of objects. After mathematically reestablishing the appropriate optical cues specifying time to passage for noncollision cases, we report a series of studies in which we examined observers' sensitivities to this information in both relative- and absolute-judgment paradigms. In general, we found observers' judgments to be accurate and robust.

Kaiser, Mary K.↗

NASA NTRS1997

SUCCESS Evidence for Cirrus Cloud Ice Nucleation Mechanisms

During the SUCCESS mission, several measurements were made which should improve our understanding of ice nucleation processes in cirrus clouds. Temperature and water vapor concentration were made with a variety of instruments on the NASA DC-8. These observations should provide accurate upper tropospheric humidities. In particular, we will evaluate what humidities are required for ice nucleation. Preliminary results suggest that substantial supersaturations frequently exist in the upper troposphere. The leading-edge region of wave-clouds (where ice nucleation occurs) was sampled extensively at temperatures near -40 and -60C. These observations should give precise information about conditions required for ice nucleation. In addition, we will relate the observed aerosol composition and size distributions to the ice formation observed to evaluate the role of soot or mineral particles on ice nucleation. As an alternative technique for determining what particles act as ice nuclei, numerous samples of aerosols inside ice crystals were taken. In some cases, large numbers of aerosols were detected in each crystal, indicating that efficient scavenging occurred. Analysis of aerosols in ice crystals when only one particle per crystal was detected should help with the ice nucleation issue. Direct measurements of the ice nucleating activity of ambient aerosols drawn into airborne cloud chambers were also made. Finally, measurements of aerosols and ice crystals in contrails should indicate whether aircraft exhaust soot particles are effective ice nuclei.

Jensen, Eric↗

NASA NTRS2016

Binary Black Hole Mergers in the First Advanced LIGO Observing Run

The first observational run of the Advanced LIGO detectors, from September 12, 2015 to January 19, 2016, saw the first detections of gravitational waves from binary black hole mergers. In this paper we present full results from a search for binary black hole merger signals with total masses up to 100M solar mass and detailed implications from our observations of these systems. Our search, based on general-relativistic models of gravitational wave signals from binary black hole systems, unambiguously identified two signals, GW150914 and GW151226, with a significance of greater than 5 alpha over the observing period. It also identified a third possible signal, LVT151012, with substantially lower significance, which has a 87 probability of being of astrophysical origin. We provide detailed estimates of the parameters of the observed systems. Both GW150914 and GW151226 provide an unprecedented opportunity to study the two-body motion of a compact-object binary in the large velocity, highly nonlinear regime. We do not observe any deviations from general relativity, and place improved empirical bounds on several high-order post-Newtonian coefficients. From our observations we infer stellar-mass binary black hole merger rates lying in the range 9-240 Gpc-3 yr-1. These observations are beginning to inform astrophysical predictions of binary black hole formation rates, and indicate that future observing runs of the Advanced detector network will yield many more gravitational wave detections.

Abbott, B. P.↗

NASA NTRS2014

Cultivating an Emergent Earth Observation Analytics Ecosystem in the Cloud

A diverse set of data analytics systems for Earth Observations are sprouting up in the Earth Science community, with a wealth of processing algorithms and analysis methods. There is a similar wealth of data resources available via myriad data providers and clearinghouses, including large institutional systems like the Earth Observing System Data and Information System, Comprehensive Large Scale Array-data Stewardship System, and Federated Earth Observation Missions gateway. With Earth system science driving a need to work with more datasets together, and the community developing more analysis tools (some of them dataset-specific), how can we develop analysis workflows that incorporate far-flung datasets and leverage analysis resources from multiple organizations? Cloud computing points the way toward a solution in two different respects. Firstly, the access to and abstraction of virtually unlimited storage and computing power provides an environment that enables more straightforward means of pulling datasets and analysis resources together. Just as importantly, however, cloud computing serves as an example of an "ecosystem" of interoperating services, since the essence of cloud computing is the presentation of all resources as a service, from hardware to infrastructure to platform to software. This enables the combination of off-the-shelf, diverse services to construct entire systems that emerge out of an equally diverse community of architects and developers. This approach can be similarly applied to the data and analysis resources in the Earth Observation community. By exposing these resources via well understood services, and consuming resources in the same way, different organizations can construct bespoke analysis workflows and systems for their own purposes. The key leap the community needs to make is to develop analysis systems in components that interact with other components via services. The result would be a rich ecosystem of analytics components that can be combined to analyze datasets at scale and in conjunction with other datasets from other sources.

chaos↗

NASA NTRSDate not supplied

Shock-Layer Radiation Insights Available Through Flowfield Property Binning

This paper introduces a method for assessing similarity between available shock-layer radiation measurements and the radiative heating to a vehicle of interest. This method will be referred to as the flowfield property binning (FPB) approach. The challenge in developing this approach is that radiometer measurements from a surface normal line of sight, observed radiation measurements of the entire shock-layer from a distance, or measured shock-tube nonequilibrium profiles are notably different geometrically than the radiative flux to a vehicle surface. Therefore, the relative contributions of various regions of the shock-layer to the measurement, and how these relative contributions compare with the radiative heating to a vehicle of interest, are unclear. By tracking the radiation contribution from each flowfield location, and then grouping this contribution based on two local flowfield properties, these geometric differences are removed. This provide a two dimensional group, or bin, distribution that represents the radiation environment in the same format for various types of radiation simulations, such as observed radiation or radiative flux to a vehicle surface. In the limits of optically-thin radiation transport and thermochemical equilibrium flow, the choice of pressure and temperature as the binning flowfield properties is shown to provide a bin distribution that uniquely defines the radiation simulation. This concept is found to be sufficient for establishing similarity between two cases even when the optically-thin radiation transport and thermochemical equilibrium assumptions are not satisfied. A simple metric of comparison between bin distributions from different cases is provided by the histogram intersection algorithm. This algorithm results in a histogram intersection factor (H) between 0 and 1, which represents the fraction of the radiation that is similar between the two cases. Therefore, if H evaluated between bin distributions for two completely different radiation problems is near 1, then they represent essentially the same radiation problem. This means that they depend on the same weighting of various radiation modeling parameters evaluated at the same pressure and temperature. If one of these cases agrees well with a radiation measurement, then the matching bin distributions provide justification for applying this experimental validation to the other case. Even for H values as low as 0.2, the measurement case could be considered relevant if there are no other useful measurements. This approach allows observed radiation measurements to directly inform radiative heating simulations, which allows NASA’s past and future investments in observed radiation measurements to directly inform the radiative heating uncertainty or margins for future vehicle design. This FPB approach is also used to determine shock-tube conditions for informing backshell radiative heating simulations, where the normal shock velocity and Lagrangian time from the shock is used for the binning variables instead of pressure and temperature.

Shock Layer Radiation↗

DOE OSTI2021

An AI-Enabled MODEX Framework for Improving Predictability of Subsurface Water Storage across Local and Continental Scales

Focal Area: (2) Predictive modeling through the use of AI techniques and AI-derived model components. (3) Insight gleaned from complex data using AI, big data analytics, and other advanced methods. We propose an AI-enabled model-experiment (MODEX) framework to improve the predictability of subsurface water storage (SWS) from local to conus scales in a changing environment by taking advantage of DOE’s observation and simulation capabilities, as well as to inform the model and the observation development.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2007

Space Weather Nowcasting of Atmospheric Ionizing Radiation for Aviation Safety

There is a growing concern for the health and safety of commercial aircrew and passengers due to their exposure to ionizing radiation with high linear energy transfer (LET), particularly at high latitudes. The International Commission of Radiobiological Protection (ICRP), the EPA, and the FAA consider the crews of commercial aircraft as radiation workers. During solar energetic particle (SEP) events, radiation exposure can exceed annual limits, and the number of serious health effects is expected to be quite high if precautions are not taken. There is a need for a capability to monitor the real-time, global background radiations levels, from galactic cosmic rays (GCR), at commercial airline altitudes and to provide analytical input for airline operations decisions for altering flight paths and altitudes for the mitigation and reduction of radiation exposure levels during a SEP event. The Nowcast of Atmospheric Ionizing Radiation for Aviation Safety (NAIRAS) model is new initiative to provide a global, real-time radiation dosimetry package for archiving and assessing the biologically harmful radiation exposure levels at commercial airline altitudes. The NAIRAS model brings to bear the best available suite of Sun-Earth observations and models for simulating the atmospheric ionizing radiation environment. Observations are utilized from ground (neutron monitors), from the atmosphere (the METO analysis), and from space (NASA/ACE and NOAA/GOES). Atmospheric observations provide the overhead shielding information and the ground- and space-based observations provide boundary conditions on the GCR and SEP energy flux distributions for transport and dosimetry simulations. Dose rates are calculated using the parametric AIR (Atmospheric Ionizing Radiation) model and the physics-based HZETRN (High Charge and Energy Transport) code. Empirical models of the near-Earth radiation environment (GCR/SEP energy flux distributions and geomagnetic cut-off rigidity) are benchmarked against the physics-based CMIT (Coupled Magnetosphere- Ionosphere-Thermosphere) and SEP-trajectory models.

Mertens, Christopher J.↗

DOE OSTI2022

The information content of jet quenching and machine learning assisted observable design

Jets produced in high-energy heavy-ion collisions are modified compared to those in proton-proton collisions due to their interaction with the deconfined, strongly-coupled quark-gluon plasma (QGP). In this work, we employ machine learning techniques to identify important features that distinguish jets produced in heavy-ion collisions from jets produced in proton-proton collisions. We formulate the problem using binary classification and focus on leveraging machine learning in ways that inform theoretical calculations of jet modification: (i) we quantify the information content in terms of Infrared Collinear (IRC)-safety and in terms of hard vs. soft emissions, (ii) we identify optimally discriminating observables that are in principle calculable in perturbative QCD, and (iii) we assess the information loss due to the heavy-ion underlying event and background subtraction algorithms. We illustrate our methodology using Monte Carlo event generators, where we find that important information about jet quenching is contained not only in hard splittings but also in soft emissions and IRC-unsafe physics inside the jet. This information appears to be significantly reduced by the presence of the underlying event. We discuss the implications of this for the prospect of using jet quenching to extract properties of the QGP. Since the training labels are exactly known, this methodology can be used directly on experimental data without reliance on modeling. We outline a proposal for how such an experimental analysis can be carried out, and how it can guide future measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

From Existing and New Nuclear and Astrophysical Constraints to Stringent Limits on the Equation of State of Neutron-Rich Dense Matter

Through continuous progress in nuclear theory and experiment and an increasing number of neutron-star (NS) observations, a multitude of information about the equation of state (EOS) for matter at extreme densities is available. To constrain the EOS across its entire density range, this information needs to be combined consistently. However, the impact and model dependency of individual observations vary. Given their growing number, assessing the various methods is crucial to compare the respective effects on the EOS and discover potential biases. For this purpose, we present a broad compendium of different constraints and apply them individually to a large set of EOS candidates within a Bayesian framework. Specifically, we explore different ways of how chiral effective field theory and perturbative quantum chromodynamics can be used to place a likelihood on EOS candidates. We also investigate the impact of nuclear experimental constraints, as well as different radio and x-ray observations of NS masses and radii. This is augmented by reanalyses of the existing data from binary neutron star coalescences, in particular of GW170817, with improved models for the tidal waveform and kilonova light curves, which we also utilize to construct a tight upper limit of 2.39 M ⊙ on the TOV mass based on GW170817’s remnant. Our diverse set of constraints is eventually combined to obtain stringent limits on NS properties. We organize the combination in a way to distinguish between constraints where the systematic uncertainties are deemed small and those that rely on less conservative assumptions. For the former, we find the radius of the canonical 1.4 M ⊙ neutron star to be R 1.4 = 12.2 6 − 0.91 + 0.80 km and the TOV mass at M TOV = 2.2 5 − 0.22 + 0.42 M ⊙ (95% credibility). Including all the presented constraints yields R 1.4 = 12.2 0 − 0.48 + 0.50 km and M TOV = 2.3 0 − 0.20 + 0.07 M ⊙ . When comparing these limits to individual data points, we find that the quoted radius of HESS J1731-347 displays noticeable tension with other constraints. Constraining microphysical properties of the EOS proves more challenging. For instance, the symmetry energy slope is restricted to L sym = 48 − 25 + 21 MeV , where this constraint is mainly dominated by our reanalysis of the PREX-II and CREX experiment. Published by the American Physical Society 2025

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗