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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Different Spatiotemporal Patterns in Global Human Population and Built‐Up Land

Abstract Population concentration and built‐up land expansion are two prominent features of contemporary urbanization. Existing literature on the population aspect of urbanization has mostly focused on national and regional aggregates, and literature on the land development aspect has often relied on spatial case studies of individual cities or their meta‐analyses. Using newly‐available data, here we conduct the first global‐coverage, spatial analysis of the relationship between (changes in) population and built‐up land at multiple spatial scales, and compare to existing common beliefs about urbanization based on individual city studies. We find that population and built‐up land show distinctly different spatial and temporal patterns (with a global correlation coefficient around 0.6). Contrary to common impressions, our results show that during recent decades, developed and developing regions across the world experienced comparable amounts of built‐up land expansion. While meta‐analyses have reported that built‐up land in urban areas expands globally on average twice as fast as population grows, our results show the global change rates of built‐up land and population are similar. Also, most global population, including what national statistics agencies call urban population, reside in areas with low land development levels (which are frequently less than 5% built up). These changes in perspective suggest that urbanization's potential large‐scale impacts may need to be re‐evaluated, and lead to best‐practice recommendations for urbanization modeling and analysis. Especially, the common practice in large‐scale earth system modeling of assuming demographically‐defined urban population resides in areas with medium to high built‐up land development levels should change.

Gao, Jing↗
DOE OSTI2023

CASPI: collaborative photon processing for active single-photon imaging

Image sensors capable of capturing individual photons have made tremendous progress in recent years. However, this technology faces a major limitation. Because they capture scene information at the individual photon level, the raw data is sparse and noisy. Here we propose CASPI: Collaborative Photon Processing for Active Single-Photon Imaging, a technology-agnostic, application-agnostic, and training-free photon processing pipeline for emerging high-resolution single-photon cameras. By collaboratively exploiting both local and non-local correlations in the spatio-temporal photon data cubes, CASPI estimates scene properties reliably even under very challenging lighting conditions. We demonstrate the versatility of CASPI with two applications: LiDAR imaging over a wide range of photon flux levels, from a sub-photon to high ambient regimes, and live-cell autofluorescence FLIM in low photon count regimes. We envision CASPI as a basic building block of general-purpose photon processing units that will be implemented on-chip in future single-photon cameras.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Transport coefficient approach for characterizing nonequilibrium dynamics in soft matter

Nonequilibrium states in soft condensed matter require a systematic approach to characterize and model materials, enhancing predictability and applications. Among the tools, X-ray photon correlation spectroscopy (XPCS) provides exceptional temporal and spatial resolution to extract dynamic insight into the properties of the material. However, existing models might overlook intricate details. We introduce an approach for extracting the transport coefficient, denoted as $J(t)$, from the XPCS studies. This coefficient is a fundamental parameter in nonequilibrium statistical mechanics and is crucial for characterizing transport processes within a system. Our method unifies the Green–Kubo formulas associated with various transport coefficients, including gradient flows, particle–particle interactions, friction matrices, and continuous noise. We achieve this by integrating the collective influence of random and systematic forces acting on the particles within the framework of a Markov chain. We initially validated this method using molecular dynamics simulations of a system subjected to changes in temperatures over time. Subsequently, we conducted further verification using experimental systems reported in the literature and known for their complex nonequilibrium characteristics. The results, including the derived $J(t)$ and other relevant physical parameters, align with the previous observations and reveal detailed dynamical information in nonequilibrium states. This approach represents an advancement in XPCS analysis, addressing the growing demand to extract intricate nonequilibrium dynamics. Further, the methods presented are agnostic to the nature of the material system and can be potentially expanded to hard condensed matter systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

AI-Assisted Conceptual Development of a Pre-Geometric Cosmological Model - An Exercise in AI-Assisted Conceptual Framework Generation, Paper I: Foundations and Replication Dynamics

We develop a pre-geometric cosmological framework in which existence is identified with a finite amount of unstructured energy possessing vibration as its only intrinsic property. This vibrational substrate occupies an open, bounded spectral interval $(\omega_{\min},\omega_{\max})$, ensuring finiteness of total energy and excluding infinitely stable configurations. The substrate evolves under two fundamental and competing tendencies---excitation, which amplifies coherence, and randomization, which scrambles it. Their balance produces a metastable unstructured regime in which rare fluctuations may form long-lived self-consistent spectral configurations. Because the substrate is finite and subject to competing order--disorder dynamics, no coherent configuration can be perpetually stable. We show that the only mechanism capable of sustaining long-lived organization is a replication instability: a coherent unit may reproduce into multiple offspring according to a general $1\!\to n$ rule. Replication consumes energy from the finite substrate, breaks the metastable symmetry, and induces a discrete notion of event time through the replication tick $\Delta\tau$. Temporal succession is defined through correlation ordering of spectral microstates, producing an intrinsic pre-causal structure. The compactness of the spectral domain imposes minimal and maximal timescales, bounds the internal coherence of emergent units, and limits their proliferation. These spectral constraints serve as precursors for the emergence of geometry, adjacency, and a limiting propagation speed, developed in subsequent papers of this series. Paper I provides the foundational axioms (PG1--PG9) governing the spectral substrate, its metastable dynamics, the formation of coherent units, and the necessity of replication, establishing a fully pre-geometric stage from which causal and geometric structure naturally emerge.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Spatiotemporal Downscaling Model for Solar Irradiance Forecast Using Nearest-Neighbor Random Forest and Gaussian Process

Accurate solar photovoltaic (PV) capacity estimation requires high-resolution, site-specific solar irradiance data to account for localized variability. However, global datasets, such as the National Solar Radiation Database (NSRDB), provide regional averages that fail to capture the fine-scale fluctuations critical for large-scale grid integration. This limitation is particularly relevant in the context of increasing distributed energy resources (DERs) penetration, such as rooftop PV. Additionally, it is critical to the implementation of the U.S. Federal Energy Regulatory Commission (FERC) Order 2222, which facilitates DER participation in U.S. bulk power markets. To address this challenge, this study evaluates Nearest-Neighbor Random Forest (NNRF) and Nearest-Neighbor Gaussian Process (NNGP) models for spatiotemporal downscaling of global solar irradiance data. By leveraging historical irradiance and meteorological data, these models incorporate spatial, temporal, and feature-based correlations to enhance local irradiance predictions. The NNRF model, a machine-learning approach, prioritizes computational efficiency and predictive accuracy, while the NNGP model offers a level of interpretability and prediction uncertainty by numerically quantifying correlations and dependencies in the data. Model validation was conducted using day-ahead predictions. The results showed that the average Goodness of Fit (GoF) of the NNRF model of 90.61% across all eight sites outperformed the GoF of the NNGP of 85.88%. Additionally, the computational speed of NNRF was 2.5 times faster than the NNGP. Finally, the NNGP displayed polynomial scaling while the NNRF scaled linearly with increasing number of nearest neighbors. Additional validation of the model on five sites in Puerto Rico further confirmed the superiority of the NNRF model over the NNGP model. These findings highlight the robustness and computational efficiency of NNRF for large-scale solar irradiance downscaling, making it a strong candidate for improving PV capacity estimation and real-time electricity market integration for DERs.

Asiedu, Shadrack (ORCID:0009000646004826)↗
DOE OSTI2023

Structuring Nutrient Yields throughout Mississippi/Atchafalaya River Basin Using Machine Learning Approaches

To minimize the eutrophication pressure along the Gulf of Mexico or reduce the size of the hypoxic zone in the Gulf of Mexico, it is important to understand the underlying temporal and spatial variations and correlations in excess nutrient loads, which are strongly associated with the formation of hypoxia. This study’s objective was to reveal and visualize structures in high-dimensional datasets of nutrient yield distributions throughout the Mississippi/Atchafalaya River Basin (MARB). For this purpose, the annual mean nutrient concentrations were collected from thirty-three US Geological Survey (USGS) water stations scattered in the upper and lower MARB from 1996 to 2020. Eight surface water quality indicators were selected to make comparisons among water stations along the MARB over the past two decades. Principal component analysis (PCA) was used to comprehensively evaluate the nutrient yields across thirty-three USGS monitoring stations and identify the major contributing nutrient loads. The results showed that all samples could be analyzed using two main components, which accounted for 81.6% of the total variance. The PCA results showed that yields of orthophosphate (OP), silica (SI), nitrate–nitrites (NO 3 -NO 2 ), and total suspended sediment (TSS) are major contributors to nutrient yields. It also showed that land-planted crops, density of population, domestic and industrial discharges, and precipitation are fundamental causes of excess nutrient loads in MARB. These factors are of great significance for the excess nutrient load management and pollution control of the Mississippi River. It was found that the average nutrient yields were stable within the sub-MARB area, but the large nitrogen yields in the upper MARB and the large phosphorus yields in the lower MARB were of great concern. t-distributed stochastic neighbor embedding (t-SNE) revealed interesting nonlinear and local structures in nutrient yield distributions. Clustering analysis (CA) showed the detailed development of similarities in the nutrient yield distribution. Moreover, PCA, t-SNE, and CA showed consistent clustering results. This study demonstrated that the integration of dimension reduction techniques, PCA, and t-SNE with CA techniques in machine learning are effective tools for the visualization of the structures of the correlations in high-dimensional datasets of nutrient yields and provide a comprehensive understanding of the correlations in the distributions of nutrient loads across the MARB.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1983

Great microwave bursts and hard X-rays from solar flares

The microwave and hard X-ray charateristics of 13 solar flares that produced microwave fluxes greater than 500 Solar Flux Units were analyzed. These Great Microwave Bursts were observed in the frequency range from 3 to 35 GHz at Berne, and simultaneous hard X-ray observations were made in the energy range from 30 to 500 keV with the Hard X-Ray Burst Spectrometer on the Solar Maximum Mission spacecraft. The principal aim of this analysis is to determine whether or not the same distribution of energetic electrons can explain both emissions. Correlations were found between respective temporal characteristics and, for the first time, between microwave and hard X-ray spectral characteristics. A single-temperature and a multi-temperature model from the literature were tested for consistency with the coincident X-ray and microwave spectra at microwave burst maximum. Four events are inconsistent with both of the models tested, and neither of the models attempts to explain the high-frequency part of the microwave spectrum. A model in which the emissions above and below the peak frequency originate in two different parts of a diverging magnetic loop is proposed. With this model the entire microwave spectrum of all but one of the events is explained.

Wiehl, H. J.↗
NASA NTRS1994

Time scales of pattern evolution from cross-spectrum analysis of advanced very high resolution radiometer and coastal zone color scanner imagery

We have selected square subareas (110 km on a side) from coastal zone color scanner (CZCS) and advanced very high resolution radiometer (AVHRR) images for 1981 in the California Current region off northern California for which we could identify sequences of cloud-free data over periods of days to weeks. We applied a two-dimensional fast Fourier transformation to images after median filtering, (x, y) plane removal, and cosine tapering. We formed autospectra and coherence spectra as functions of a scalar wavenumber. Coherence estimates between pairs of images were plotted against time separation between images for several wide wavenumber bands to provide a temporal lagged coherence function. The temporal rate of loss of correlation (decorrelation time scale) in surface patterns provides a measure of the rate of pattern change or evolution as a function of spatial dimension. We found that patterns evolved (or lost correlation) approximately twice as rapidly in upwelling jets as in the 'quieter' regions between jets. The rapid evolution of pigment patterns (lifetime of about 1 week or less for scales of 50-100 km) ought to hinder biomass transfer to zooplankton predators compared with phytoplankton patches that persist for longer times. We found no significant differences between the statistics of CZCS and AVHRR images (spectral shape or rate of decorrelation). In addition, in two of the three areas studied, the peak correlation between AVHRR and CZCS images from the same area occurred at zero lag, indicating that the patterns evolved simutaneously. In the third area, maximum coherence between thermal and pigment patterns occurred when pigment images lagged thermal images by 1-2 days, mirroring the expected lag of high pigment behind low temperatures (and high nutrients) in recently upwelled water. We conclude that in dynamic areas such as coastal upwelling systems, the phytoplankton cells (identified by pigment color patterns) behave largely as passive scalars at the mesoscale and that growth, death, and sinking of phytoplankton collectively play at most a mariginal role in determining the spectral statistics of the pigment patterns.

Denman, Kenneth L.↗
NASA NTRS1995

Spectroscopic measurements of element abundances in the solar corona: Variations on the FIP theme

Solar wind and solar energetic particle (SEP) data yield systematic differences between elemental abundances in the corona and in the photosphere related to the first ionization potential (FIP) of the elements: low-FIP elements are preferentially enhanced relative to high-FIP elements by about a factor of four. Spectroscopic studies of the inner corona show that such a pattern may apply on average but not in detail for coronal loops: substantial abundance differences occur between different types of coronal structures, and variations have been found from flare to flare, from one active region to another, and over time in the same region; further, in some flares, anomalies such as enhanced Ne:O ratios, distinctly at odds with the FIP pattern, show that a competing element selection mechanism sometimes operates. Details of the observed abundance variability -- such as the magnitude of the variations, the relevant temporal and spatial scales, and correlations with other properties of the given coronal structure -- may give important clues to the processes which supply and heat the corona, or they may reflect the changing physical conditions or locations where those processes take place. However, many such details remain to be established definitively. At present, abundance variability is primarily a major complication to data analysis and interpretation. However, once it is better understood, it may provide a new diagnostic tool for probing the lower layers of the solar atmosphere.

Saba, J. L. R.↗
NASA NTRS1993

Evaluation of muscle injury using magnetic resonance imaging

The objective of this study was to investigate spin echo T2 relaxation time changes in thigh muscles after intense eccentric exercise in healthy men. Spin echo and calculated T2 relaxation time images of the thighs were obtained on several occasions after exercise of one limb; the contralateral limb served as control. Muscle damage was verified by elevated levels of serum creatine kinase (CK). Thirty percent of the time no exercise effect was discernible on the magnetic resonance (MR) images. In all positive MR images (70%) the semitendinosus muscle was positive, while the biceps femoris, short head, and gracilis muscles were also positive in 50% and 25% of the total cases, respectively. The peak T2 relaxation time and serum CK were correlated (r = 0.94, p<0.01); temporal changes in muscle T2 relaxation time and serum CK were similar, although T2 relaxation time remained positive after serum CK returned to background levels. We conclude that magnetic resonance imaging can serve as a useful tool in the evaluation of eccentric exercise muscle damage by providing a quantitative indicator of damage and its resolution as well as the specific areas and muscles.

NASA Discipline Number 26-10↗
NASA NTRS2011

Simulation of Acoustics for Ares I Scale Model Acoustic Tests

The Ares I Scale Model Acoustics Test (ASMAT) is a series of live-fire tests of scaled rocket motors meant to simulate the conditions of the Ares I launch configuration. These tests have provided a well documented set of high fidelity acoustic measurements useful for validation including data taken over a range of test conditions and containing phenomena like Ignition Over-Pressure and water suppression of acoustics. To take advantage of this data, a digital representation of the ASMAT test setup has been constructed and test firings of the motor have been simulated using the Loci/CHEM computational fluid dynamics software. Results from ASMAT simulations with the rocket in both held down and elevated configurations, as well as with and without water suppression have been compared to acoustic data collected from similar live-fire tests. Results of acoustic comparisons have shown good correlation with the amplitude and temporal shape of pressure features and reasonable spectral accuracy up to approximately 1000 Hz. Major plume and acoustic features have been well captured including the plume shock structure, the igniter pulse transient, and the ignition overpressure.

Putnam, Gabriel↗
NASA NTRS2011

Validation and Simulation of ARES I Scale Model Acoustic Test -1- Pathfinder Development

The Ares I Scale Model Acoustics Test (ASMAT) is a series of live-fire tests of scaled rocket motors meant to simulate the conditions of the Ares I launch configuration. These tests have provided a well documented set of high fidelity measurements useful for validation including data taken over a range of test conditions and containing phenomena like Ignition Over-Pressure and water suppression of acoustics. To take advantage of this data, a digital representation of the ASMAT test setup has been constructed and test firings of the motor have been simulated using the Loci/CHEM computational fluid dynamics software. Within this first of a series of papers, results from ASMAT simulations with the rocket in a held down configuration and without water suppression have then been compared to acoustic data collected from similar live-fire tests to assess the accuracy of the simulations. Detailed evaluations of the mesh features, mesh length scales relative to acoustic signals, Courant-Friedrichs-Lewy numbers, and spatial residual sources have been performed to support this assessment. Results of acoustic comparisons have shown good correlation with the amplitude and temporal shape of pressure features and reasonable spectral accuracy up to approximately 1000 Hz. Major plume and acoustic features have been well captured including the plume shock structure, the igniter pulse transient, and the ignition overpressure. Finally, acoustic propagation patterns illustrated a previously unconsidered issue of tower placement inline with the high intensity overpressure propagation path.

Putnam, G. C.↗
NASA NTRS2012

Satellite-based Assessment of Climate Controls on US Burned Area

Climate regulates fire activity through the buildup and drying of fuels and the conditions for fire ignition and spread. Understanding the dynamics of contemporary climate-fire relationships at national and sub-national scales is critical to assess the likelihood of changes in future fire activity and the potential options for mitigation and adaptation. Here, we conducted the first national assessment of climate controls on US fire activity using two satellite-based estimates of monthly burned area (BA), the Global Fire Emissions Database (GFED, 1997 2010) and Monitoring Trends in Burn Severity (MTBS, 1984 2009) BA products. For each US National Climate Assessment (NCA) region, we analyzed the relationships between monthly BA and potential evaporation (PE) derived from reanalysis climate data at 0.5 resolution. US fire activity increased over the past 25 yr, with statistically significant increases in MTBS BA for entire US and the Southeast and Southwest NCA regions. Monthly PE was strongly correlated with US fire activity, yet the climate driver of PE varied regionally. Fire season temperature and shortwave radiation were the primary controls on PE and fire activity in the Alaska, while water deficit (precipitation PE) was strongly correlated with fire activity in the Plains regions and Northwest US. BA and precipitation anomalies were negatively correlated in all regions, although fuel-limited ecosystems in the Southern Plains and Southwest exhibited positive correlations with longer lead times (6 12 months). Fire season PE in creased from the 1980s 2000s, enhancing climate-driven fire risk in the southern and western US where PE-BA correlations were strongest. Spatial and temporal patterns of increasing fire season PE and BA during the 1990s 2000s highlight the potential sensitivity of US fire activity to climate change in coming decades. However, climatefire relationships at the national scale are complex, based on the diversity of fire types, ecosystems, and ignition sources within each NCA region. Changes in the seasonality or magnitude of climate anomalies are therefore unlikely to result in uniform changes in US fire activity.

Morton, D. C.↗
NASA NTRS2014

Global-Scale Comparison of Passive (SMOS) and Active (ASCAT) Satellite Based Microwave Soil Moisture Retrievals with Soil Moisture Simulations (MERRA-Land)

Global surface soil moisture (SSM) datasets are being produced based on active and passive microwave satellite observations and simulations from land surface models (LSM). This study investigates the consistency of two global satellite-based SSM datasets based on microwave remote sensing observations from the passive Soil Moisture and Ocean Salinity (SMOS;SMOSL3 version 2.5) and the active Advanced Scatterometer (ASCAT; version TUWien- WARP 5.5) with respect to LSM SSM from the MERRA-Land data product. The relationship between the global-scale SSM products was studied during the 2010-2012 period using (1) a time series statistics (considering both original SSM data and anomalies), (2) a space-time analysis using Hovmoller diagrams, and (3) a triple collocation error model. The SMOSL3 and ASCAT retrievals are consistent with the temporal dynamics of modeled SSM (correlation R (is) greater than 0.70 for original SSM) in the transition zones between wet and dry climates, including the Sahel, the Indian subcontinent, the Great Plains of North America, eastern Australia, and southeastern Brazil. Over relatively dense vegetation covers, a better consistency with MERRA-Land was obtained with ASCAT than with SMOSL3. However, it was found that ASCAT retrievals exhibit negative correlation versus MERRA-Land in some arid regions (e.g., the Sahara and the Arabian Peninsula). In terms of anomalies, SMOSL3 better captures the short term SSM variability of the reference dataset (MERRA-Land) than ASCAT over regions with limited radio frequency interference (RFI) effects (e.g., North America, South America, and Australia). The seasonal and latitudinal variations of SSM are relatively similar for the three products, although the MERRALand SSM values are generally higher and their seasonal amplitude is much lower than for SMOSL3 and ASCAT. Both SMOSL3 and ASCAT have relatively comparable triple collocation errors with similar spatial error patterns: (i) lowest errors in arid regions (e.g., Sahara and Arabian Peninsula), due to the very low natural variability of soil moisture in these areas, and Central America, and (ii) highest errors over most of the vegetated regions (e.g., northern Australia, India, central Asia, and South America). However, the ASCAT SSM product is prone to larger random errors in some regions (e.g., north-western Africa, Iran, and southern South Africa). Vegetation density was found to be a key factor to interpret the consistency with MERRA-Land between the two remotely sensed products (SMOSL3 and ASCAT) which provides complementary information on SSM. This study shows that both SMOS and ASCAT have thus a potential for data fusion into long-term data records.

ASCAT↗
DOE OSTI2025

Depth-dependent links between microbial taxa and nitrous oxide emissions in a long-term cotton cropping system employing soil health practices

Long-term management practices can shape soil microbial communities in ways that influence nitrogen (N) dynamics and nitrous oxide (N 2 O) emissions. We leverage a 41-year continuous cotton cropping experiment with contrasting tillage, cover cropping, and N fertilization regimes to investigate how these long-term strategies influence soil microbial communities and their associations with N 2 O fluxes during the cotton growing season. Using 16S rRNA gene metabarcoding, we assessed microbial composition in surface and subsurface soils and evaluated its relationship with temporal N 2 O emissions. Among the management practices, N fertilization – a known driver of N 2 O emissions – had the strongest effect on microbial community composition and was linked to a greater number of taxa correlated to N 2 O emissions, particularly in surface soils. Soil pH emerged as a key variable influencing microbial structure across depth and was negatively associated with both N 2 O emissions and microbial composition in the surface layers of fertilized soils. In total, 57 archaeal/bacterial taxa were correlated with N 2 O fluxes, but only seven were shared across depths, suggesting distinct microbial contributors in surface and subsurface soils. Several of these taxa have been previously reported to be associated with N and C cycling processes such as nitrate respiration or carbon turnover, indicating functional context to their correlation with N 2 O fluxes. Temporal shifts in the abundance of key taxa aligned with seasonal peaks in N 2 O emissions, notably in early and late August, and were most pronounced under conventional tillage, hairy vetch cover cropping, and N fertilization. While 16S-based associations cannot confirm functional gene presence or activity, these findings demonstrate that long-term fertilization and associated soil acidification are dominant drivers of microbial shifts linked to N 2 O emissions and highlight the importance of accounting for depth-specific and seasonal microbial dynamics when evaluating management impacts on greenhouse gas emissions.

16S rRNA gene sequencing↗
NASA NTRS2003

On the Computation of Space-Time Correlations by Large-Eddy Simulation

Numerical comparisons in decaying isotropic turbulence suggest that there exist discrepancies in time correlations evaluated by DNS and LES using eddy-viscosity-type SGS models. This is consistent with the previous observations in forced isotropic turbulence. Therefore, forcing is not the main cause of the discrepancies. Comparisons among different SGS models in the LES also indicate that the model choice affects the time correlations in the LES. The multi-scale LES method using the dynamic Smagorinsky model on the small scale equation is the most accurate of the all models, the classic Smagorinsky model is the least accurate and the dynamic Smagorinsky model and spectral eddy viscosity model give intermediate results with small differences. The generalized sweeping hypothesis implies that time correlations in decaying isotropic turbulence are mainly determined by the instantaneous energy spectra and sweeping velocities. The analysis based on the sweeping hypothesis explains the discrepancies in our numerical simulations: the LES overpredicts the decorrelation time scales because the sweeping velocities are smaller than the DNS values, and underpredicts the magnitudes of time correlations because the energy spectrum levels are lower than the DNS ones. Since the sweeping velocity is determined by the energy spectra, one concludes that an accurate prediction of the instantaneous energy spectra guarantees the accuracy of time correlations. An analytical expression of sound power spectra based on Lighthill's theory and the quasi-normal closure assumption suggests that the sound power spectra are sensitive to errors in time correlations. Small errors in time correlations can cause significant errors in the sound power spectra, which exhibit a sizable drop at moderate to high frequencies accompanied by a shift of the peaks to lower frequencies. Based on the above analysis, two possible ways to improve the acoustic power spectrum predictions can be considered. The first is to construct better SGS models to improve the LES accuracy for time correlations. The second is to remedy the temporal statistics of the Lighthill stress tensor in order to "recover" the contribution from the unresolved scales in LES to time correlations.

He, Guo-Wei↗
NASA NTRS1985

Subseasonal and interannual variations of tropical convection and the El Nino/Southern Oscillation

It was found that during the northern winter, fluctuation in tropical convection is dominated by eastware propagating cloud clusters from the Indian Ocean. Most of these clusters reach as far as the central Pacific. Longitude time sections of outgoing longwave radiation (OLR) anomaly along the equator show that there are about 3 to 4 such episodes each winter. Also, simultaneous OLR anomalies over the equatorial central Pacific (ECP) and the maritime continent (MC) are negatively correlated and their variations are dominated by a 40 to 60 day period, suggesting the presence of a standing wave phenomenon. Such combination of propagating and standing phenomena can be captured by the extended empirical orthogonal function (EEOF) analysis. The EEOF method combines the temporal as well as the spatial correlation information to produce eigenmodes which have space time dependence. A 5 day and a 10 day lag in addition to the simultaneous spatial correlation is used.

Lau, K. M.↗
DOE OSTI2021

Designing high-power, octave spanning entangled photon sources for quantum spectroscopy

Entangled photon spectroscopy is a nascent field that has important implications for measurement and imaging across chemical, biology, and materials fields. Entangled photon spectroscopy potentially offers improved spatial and temporal-frequency resolutions, increased cross sections for multiphoton and nonlinear measurements, and new abilities in inducing or measuring quantum correlations. A critical step in enabling entangled photon spectroscopies is the creation of high-flux entangled sources that can use conventional detectors as well as provide redundancy for the losses in realistic samples. Here, we report a periodically poled, chirped, lithium tantalate platform that generates entangled photon pairs with ~10 -7 efficiency. For a near watt level diode laser, this results in a near μW-level flux. The single photon per mode limit that is necessary to maintain non-classical photon behavior is still satisfied by distributing this power over up to an octave-spanning bandwidth. The spectral–temporal photon correlations are observed via a Michelson-type interferometer that measures the broadband Hong–Ou–Mandel two-photon interference. A coherence time of 245 fs for a 10 nm bandwidth in the collinear case and a coherence time of 62 fs for a 125 nm bandwidth in the non-collinear case are measured using a CW pump laser and, essentially, collecting the full photon cone. We outline in detail the numerical methods used for designing and tailoring the entangled photons source, such as changing center wavelength or bandwidth, with the ultimate aim of increasing the availability of high-flux UV–Vis entangled photon sources in the optical spectroscopy community.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗