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At least 73 records · Page 4

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NASA NTRSDate not supplied

Identifying Indicators of Harmful Algal Blooms in Coastal Atacama Using Satellite Image Processing Techniques to Improve Industry and Authority Response

Over the past decade, Chile has experienced a significant rise in both the frequency and intensity of coastal Harmful Algal Blooms (HABs). These bloom events are a growing concern for the Atacama Region especially, with potential impacts on human health, aquaculture, and the environment. HABs are caused by an excess proliferation of microalgae, with certain algae species commonly found in Chilean HABs capable of producing toxins which can poison fish, toxify shellfish, and cause illness or death when ingested by humans. Collaborating with the Ministry of Health of Chile, Centro de Información de Recursos Naturales, the University of Atacama, and the Embassy of Chile’s Agricultural Office, this study identifies potential indicators of HABs in the Coastal Atacama Region using NASA Earth observations. Satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard NASA’s Aqua satellite was used for the identification of chlorophyll-a which provides direct estimates of the algae pigment’s concentration in water bodies. In addition, the spatiotemporal patterns of several other parameters were examined to identify correlations with occurrences of HABs, including sea surface temperature and normalized fluorescence line height using Aqua MODIS, Normalized Difference Turbidity Index (NDTI) using Landsat 8, and ocean wind speed measurements from the NOAA Joint Polar Satellite System. Analysis of these patterns revealed hotspots of high chlorophyll-a concentrations from the years 2014 to 2022, the most major of which occurring in the coastal zones of Chañaral, Bahía Inglesa, and Huasco. Also, through the use of a similarity search conducted, the seasonal variation of these indicators was mapped to highlight areas with great likelihood of experiencing HABs in the future. The results can guide future efforts for in-situ water sampling, toxin analysis and assessment, and HAB monitoring and prevention.

Cody O'Ferrall↗

DOE OSTI2020

Unveiling defect-mediated carrier dynamics in monolayer semiconductors by spatiotemporal microwave imaging

The optoelectronic properties of atomically thin transition-metal dichalcogenides are strongly correlated with the presence of defects in the materials, which are not necessarily detrimental for certain applications. For instance, defects can lead to an enhanced photoconduction, a complicated process involving charge generation and recombination in the time domain and carrier transport in the spatial domain. Here, we report the simultaneous spatial and temporal photoconductivity imaging in two types of WS 2 monolayers by laser-illuminated microwave impedance microscopy. The diffusion length and carrier lifetime were directly extracted from the spatial profile and temporal relaxation of microwave signals, respectively. Time-resolved experiments indicate that the critical process for photoexcited carriers is the escape of holes from trap states, which prolongs the apparent lifetime of mobile electrons in the conduction band. As a result, counterintuitively, the long-lived photoconductivity signal is higher in chemical-vapor deposited (CVD) samples than exfoliated monolayers due to the presence of traps that inhibits recombination. Overall, our work reveals the intrinsic time and length scales of electrical response to photoexcitation in van der Waals materials, which is essential for their applications in optoelectronic devices.

42 ENGINEERING↗

DOE OSTI2022

MSASGCN : Multi-Head Self-Attention Spatiotemporal Graph Convolutional Network for Traffic Flow Forecasting

Traffic flow forecasting is an essential task of an intelligent transportation system (ITS), closely related to intelligent transportation management and resource scheduling. Dynamic spatial-temporal dependencies in traffic data make traffic flow forecasting to be a challenging task. Most existing research cannot model dynamic spatial and temporal correlations to achieve well-forecasting performance. The multi-head self-attention mechanism is a valuable method to capture dynamic spatial-temporal correlations, and combining it with graph convolutional networks is a promising solution. Therefore, we propose a multi-head self-attention spatiotemporal graph convolutional network (MSASGCN) model. It can effectively capture local correlations and potential global correlations of spatial structures, can handle dynamic evolution of the road network, and, in the time dimension, can effectively capture dynamic temporal correlations. Experiments on two real datasets verify the stability of our proposed model, obtaining a better prediction performance than the baseline algorithms. The correlation metrics get significantly reduced compared with traditional time series prediction methods and deep learning methods without using graph neural networks, according to MAE and RMSE results. Compared with advanced traffic flow forecasting methods, our model also has a performance improvement and a more stable prediction performance. We also discuss some problems and challenges in traffic forecasting.

Cao, Yang↗

NASA NTRS1992

Periodic and chaotic spatiotemporal states in a phase-conjugate resonator using a photorefractive BaTiO3 phase-conjugate mirror

The dynamics of an externally pumped phase-conjugate resonator that uses four-wave mixing in BaTi03 is investigated experimentally. The emergence of spatiotemporal instabilities as the degree of transverse confinement is varied by changing the Fresnel number is described in detail. Local intensity time series show that relaxing the transverse confinement leads the system from a stationary state to periodic, then to quasi-periodic, motions and finally to chaotic behavior. In some regions of parameter space two frequencies are identified in the power spectra of the time series, indicating a route to chaos following the Ruelle-Takens-Newhouse scheme. Wave-front topological defects are identified by interferometry. As the system's confinement is varied, the phase-defect density and the spatial correlation index are found to follow similar trends, indicating that the observed spatiotemporal dynamics may indeed be an example of defect-mediated turbulence.

Liu, Siuying R.↗

DOE OSTI2025

Applying Gaussian Process Machine Learning and Modern Probabilistic Programming to Satellite Data to Infer CO 2 Emissions

Satellite data provides essential insights into the spatiotemporal distribution of CO 2 concentrations. However, many atmospheric inverse models fail to adequately incorporate the spatial and temporal correlations inherent in satellite observations and often lack rigorous methods for estimating parameters like spatial length scales. We introduce an inference model that processes the spatiotemporal covariance in satellite data and estimates hyperparameters such as covariance length scales. Our approach uses the Gaussian process (GP) machine learning (ML) and modern probabilistic programming languages (PPLs) to perform atmospheric inversions of emissions from satellite data. We develop a GP ML inversion system based on modern PPLs and the GEOS-Chem chemical transport model, simulating atmospheric CO 2 concentrations corresponding to the Orbiting Carbon Observatory-2/3 (OCO-2/3) data for July 2020. In our supervised learning framework, we treat the GEOS-Chem simulated data set as the target, with predictors derived by scaling the target with sector-specific factors hidden from the GP machine. Our results show that the GP model, combined with GPU-enabled PPLs, effectively retrieves true emission scaling factors and infers noise levels concealed within the data. This suggests that our method could be applied over larger areas with more complex covariance structures, enabling comprehensive analysis of the spatiotemporal patterns observed in OCO-2/3 and similar satellite data sets.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Enhanced imaging of electronic hot spots using quantum squeezed light

Detecting electronic hot spots is important for understanding the heat dissipation and thermal management of electronic and semiconductor devices. Optical thermoreflective imaging is being used to perform precise temporal and spatial imaging of heat on wires and semiconductor materials. We apply quantum squeezed light to perform thermoreflective imaging on micro-wires, surpassing the shot-noise limit of classical approaches. We obtain a far-field temperature sensing accuracy of 42 mK after 50 ms of averaging and show that a 256×256 pixel image can be constructed with such sensitivity in 10 min. We can further obtain single-shot temperature sensing of 1.6 K after only 10 μs of averaging, enabling a dynamical study of heat dissipation. Not only do the quantum images provide accurate spatiotemporal information about heat distribution but also the measure of quantum correlation provides additional information, inaccessible by classical techniques, which can lead to a better understanding of the dynamics. We apply the technique to both aluminum and niobium microwires and discuss the applications of the technique in studying electron dynamics at low temperatures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Revealing complex subsurface dynamics with continuous seismic monitoring: Observations using distributed acoustic sensing and surface orbital vibrators during hydraulic fracturing

Understanding hydraulic fracturing is crucial to improving the stimulation of unconventional reservoirs and increasing fluid production. This study develops a novel seismic monitoring technology using distributed acoustic sensing (DAS) and surface orbital vibrators (SOV) to capture fracture seismic response and mechanical properties at high temporal intervals. We analyze continuous time-lapse vertical seismic profiling (VSP) data acquired every hour during the first nine days of treatment of an unconventional reservoir in the Austin Chalk/Eagle Field Laboratory. The VSP data contain clear seismic signals scattered from the activated fractures. The spatiotemporal changes of the fracture reflectivity revealed by the SOV/DAS data correlate well with the observations of fracture locations inferred from low-frequency DAS data. These results capture the fracture opening and closure processes, as well as highlight potential prestage activations of the fractures due to hydraulic connectivity with preexisting fracture systems. Therefore, analysis of the presented data set provides a unique opportunity to understand fracture initiation and subsequent evolution, not only in the context of unconventional resources but also in enhanced geothermal systems.

Correa, Julia↗

DOE OSTI2021

Spatiotemporal mapping of mesoscopic liquid dynamics

The study of liquid dynamics at mesoscopic scales is still strewn with difficulty due to limitations in theory and experiment. Historically, significant attention has been given to the analysis of space-time correlation functions and their frequency-Fourier transforms at a few discrete wave numbers. The massive computing power afforded by modern high performance computing clusters and the advent of a wide-angle neutron spin-echo spectrometer, however, have unlocked a more intuitive and fruitful approach to this problem. Using molecular dynamics simulations, here we demonstrate the benefits of spatiotemporally mapping intermediate scattering functions on a dense grid of correlation times and wave numbers. Four model systems are investigated: a Lennard-Jones liquid, a coarse-grained bead-spring polymer, a molten sodium chloride, and a poly(ethylene oxide) melt. We show that the spatiotemporal mapping approach is particularly useful for elucidating the mesoscopic dynamics in these liquids, where several underlying mechanisms, such as molecular relaxations, hydrodynamic modes, and nonhydrodynamic excitations, are potentially at play. Therefore, compared to the traditional method, direct visualization of density space-time correlation functions on two-dimensional color maps permits appraisals of complicated dynamical behavior at mesoscales in a global manner. For example, the scaling relations between space and time for different types of molecular motions can be straightforwardly identified on these plots, without any model-dependent analysis. Additionally, we show how theoretical ideas regarding collective mesoscopic dynamics, such as the classical hydrodynamic theory, the convolution approximation, and a recently proposed phenomenological model, can be discussed in terms of the global features of spatiotemporal maps of intermediate scattering functions. The new perspective offered by the spatiotemporal mapping method should prove useful for the study of liquid dynamics in general.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS2019

Disentangling the Drivers of the Summertime Ozone‐Temperature Relationship Over the United States

Summertime surface-level ozone (O3 ) is known to vary with temperature, but the relative roles of different processes responsible for causing the O3 -temperature relationship are not well quantified. In this study we use simulations of NASA's Global Modeling Initiative (GMI) chemical transport model (CTM) to isolate and assess the relative impact of atmospheric transport, chemistry, and emissions on O3 variability, events, and its correlation with temperature. Using observations from CASTNet in the contiguous United States, we show that the GMI CTM reproduces the spatiotemporal variability of O3 and the O3 -temperature relationship during the summer. We primarily focus on the total change in O3 due to a change in temperature (d O3 /d T). In regions with strong positive correlations between temperature and O3 such as the Northeast, Great Lakes, and Great Plains, temperature's association with transport primarily drives d O3 /d T with smaller contributions from temperature-dependent chemistry and anthropogenic emissions. There are regions, however, with near-zero correlation between temperature and O3 , and our findings suggest that transport is still an important driver of O3 variability in these regions, albeit not correlated with temperature. Transport is not directly dependent on temperature but rather is linked through an indirect association, and it is therefore important to understand the exact mechanisms that link transport to O3 and how these mechanisms will change in a warming world.

ozone, temperature, CTM↗

DOE OSTI2021

An artificial spiking quantum neuron

Abstract Artificial spiking neural networks have found applications in areas where the temporal nature of activation offers an advantage, such as time series prediction and signal processing. To improve their efficiency, spiking architectures often run on custom-designed neuromorphic hardware, but, despite their attractive properties, these implementations have been limited to digital systems. We describe an artificial quantum spiking neuron that relies on the dynamical evolution of two easy to implement Hamiltonians and subsequent local measurements. The architecture allows exploiting complex amplitudes and back-action from measurements to influence the input. This approach to learning protocols is advantageous in the case where the input and output of the system are both quantum states. We demonstrate this through the classification of Bell pairs which can be seen as a certification protocol. Stacking the introduced elementary building blocks into larger networks combines the spatiotemporal features of a spiking neural network with the non-local quantum correlations across the graph.

Kristensen, Lasse Bjørn (ORCID:0000000239398170)↗

NASA NTRSDate not supplied

Improving Geolocation Accuracy of the Advanced Meteorological Imager on the GEO-KOMPSAT-2A

GeoNEX is a collaborative project led by scientists from NASA and many other international institutes to generate Earth monitoring products using data streams from the latest geostationary (GEO) sensors. Its consistent processing and common gridding systems can produce research-quality data products from GEO sensors and leverage GEO-GEO or GEO-LEO (low earth orbit) synergistic uses. Currently, GeoNEX has produced and disseminated L1G (geometrically corrected Level 1 products) from GOES 16/17 ABIs and Himawari-8 AHI, but a new Korean geostationary sensor (Advanced Meteorological Imager, AMI) onboard Geo-KOMPSAT-2A covering a large proportion of Asia and all of Oceania is in development. Our recent efforts on assessing geolocation accuracy in ABI and AHI suggest a nontrivial residual exists in both level 1B data with varying spatiotemporal patterns. The findings urge us to prioritize identifying and correcting geolocation residuals of AMI to generate accurate and consistent GeoNEX top-of-atmosphere (TOA) reflectance products and following processing chains. Here we implement a phase correlation correction approach to a visible band (0.64 μm, 500 m) using landmarks prepared from finer scale digital terrain models. We characterize spatiotemporal patterns (e.g., diurnal & daily) of geolocation residuals of AMI before and after correction. We then assess stability of datasets and quantify impact of unexpected geolocation errors on terrestrial monitoring. The geolocation corrected AMI data are further compared with GeoNEX AHI L1G products which are able to create unique stereo-type observations with AMI through leveraging the similarities of spectral bands and the sun-target-sensor geometry. Further, we discuss challenges in utilizing the GEO-GEO (e.g., AMI & AHI) satellite data for potential applications.

Geostationary Satellites↗

NASA NTRS1992

Separability of spatiotemporal spectra of image sequences

The spatiotemporal power spectrum was calculated of 14 image sequences in order to determine the degree to which the spectra are separable in space and time, and to assess the validity of the commonly used exponential correlation model found in the literature. The spectrum was expanded by a Singular Value Decomposition into a sum of separable terms and an index was defined of spatiotemporal separability as the fraction of the signal energy that can be represented by the first (largest) separable term. All spectra were found to be highly separable with an index of separability above 0.98. The power spectra of the sequences were well fit by a separable model. The power spectrum model corresponds to a product of exponential autocorrelation functions separable in space and time.

Eckert, Michael P.↗

DOE OSTI2023

Operando methods: A new era of electrochemistry

One of the grand challenges facing electrochemistry is to directly resolve the complex nature of (electro)catalyst active sites and capture real-time “movies” of reaction dynamics, i.e. “watching chemistry in action”. The need for such fundamental understanding has stimulated the development of operando/in situ methods, which have greatly enhanced our ability to identify activity descriptors of electrocatalysts and establish structure–property relationships of energy materials. Here, this review summarizes the frontiers of operando electrochemical liquid-cell scanning transmission electron microscopy and correlative synchrotron X-ray methods, which are complementary tools to comprehensively investigate reaction dynamics across multiple spatiotemporal scales. In an effort to encourage greater adoption of advanced operando methods by the general electrochemistry community, this review points out the need to benchmark electrochemistry in confined and heterogenous liquid environment with minimal beam-induced damage. We anticipate that multimodal operando methods will become indispensable for understanding interfacial reaction mechanisms for the broad chemistry and energy materials communities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2021

Validation of IASI Satellite Ammonia Observations at the Pixel Scale Using In Situ Vertical Profiles

Satellite ammonia (NH 3 ) observations provide unprecedented insights into NH3 emissions, spatiotemporal variabilities and trends, but validation with in situ measurements remains lacking. Here, total columns from the Infrared Atmospheric Sounding Interferometer (IASI) were intercompared to boundary layer NH 3 profiles derived from aircraft- and surface-based measurements primarily in Colorado, USA, in the summer of 2014. IASI-NH 3 version 3 near real-time data set compared well to in situ derived columns (windows ±15 km around centroid, ±1 h around overpass time) with a correlation of 0.58, a slope of 0.78 ± 0.14 and an intercept of 2.1 × 10 15 ±1.5 × 10 15 molecules cm −2 . Agreement degrades at larger spatiotemporal windows, consistent with the short atmospheric lifetime of NH 3 . We also examined IASI version 3R data, which relies on temperature retrievals from the ERA Reanalysis, and a third product generated using aircraft-measured temperature profiles. The overall agreement improves slightly for both cases, and neither is biased within their combined measurement errors. Thus, spatiotemporal averaging of IASI over large windows can be used to reduce retrieval noise. Nonetheless, sampling artifacts of airborne NH 3 instruments result in significant uncertainties of the in situ-derived columns. For example, large validation differences exist between ascent and descent profiles, and the assumptions of the free tropospheric NH 3 profiles used above the aircraft ceiling significantly impact the validation. Because short-lived species like NH 3 largely reside within the boundary layer with complex vertical structures, more comprehensive validation is needed across a wide range of environments. More accurate and widespread in situ NH 3 data sets are therefore required for improved validations of satellite products.

Ammonia↗

DOE OSTI2021

Upregulation of PPPDE1 contributes to anorectal malformations via the mitochondrial apoptosis pathway during hindgut development in rats

Congenital anorectal malformations (ARMs) are among the most prominent deformities of the gastrointestinal tract; however, their precise aetiology remains obscure. Immunohistochemistry demonstrated that, in the ARM group, the PPPDE1-positive cells were widely distributed in the hindgut epithelial tissue from GD13 to GD16. Immunofluorescence revealed that most TUNEL-, Bax-, and Cytochrome C (Cyt C)-positive cells overlapped with PPPDE1-positive cells in the urorectal septum (URS). Western blotting and quantitative real-time RT-PCR revealed that PPPDE1 levels were significantly higher in the ARM group from GD13 to GD14 (p < 0.05). IEC-6 cells were transfected with PPPDE1 overexpression plasmid/NC (negative control) or si-PPPDE1/si-NC. Flow cytometry analysis and CCK-8 assay (used to detect apoptosis and proliferation, respectively), as well as western blotting, showed that the levels of PPPDE1 were positively correlated with the pro-apoptotic molecules Bax and Cyt C. Accordingly, aberrantly high expression of PPPDE1 caused a spatiotemporal imbalance in foetal rats with ARMs during hindgut development. Therefore, the upregulation of PPPDE1 may promote epithelial apoptosis and reduce proliferation in the hindgut via the mitochondrial apoptotic pathway. This could affect the fusion of the URS and cloacal membrane, ultimately inhibiting the hindgut development and resulting in ARMs.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

On-shot, high-intensity laser aberration measurements via ponderomotive electron ejection

We present a technique to assess the spatial aberration content of a focused multi-terawatt laser when fired at full power. This method leverages the direct detection of electrons ponderomotively accelerated from the focal volume formed in a low-pressure gaseous back-fill. Furthermore, our results show that the spatial distribution of emitted electrons exhibits distinct features correlated to the laser aberration type and magnitude. This work represents progress toward the complete and accurate in-situ spatiotemporal characterization of focused high-intensity lasers.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Deep learning-based spatiotemporal multi-event reconstruction for delay line detectors

Abstract Accurate observation of two or more particles within a very narrow time window has always been a challenge in modern physics. It creates the possibility of correlation experiments, such as the ground-breaking Hanbury Brown–Twiss experiment, leading to new physical insights. For low-energy electrons, one possibility is to use a Microchannel plate with subsequent delay lines for the readout of the incident particle hits, a setup called a Delay Line Detector. The spatial and temporal coordinates of more than one particle can be fully reconstructed outside a region called the dead radius. For interesting events, where two electrons are close in space and time, the determination of the individual positions of the electrons requires elaborate peak finding algorithms. While classical methods work well with single particle hits, they fail to identify and reconstruct events caused by multiple nearby particles. To address this challenge, we present a new spatiotemporal machine learning model to identify and reconstruct the position and time of such multi-hit particle signals. This model achieves a much better resolution for nearby particle hits compared to the classical approach, removing some of the artifacts and reducing the dead radius a factor of eight. We show that machine learning models can be effective in improving the spatiotemporal performance of delay line detectors.

Computer Science↗

DOE OSTI2023

Divergent urbanization-induced impacts on global surface urban heat island trends since 1980s

Urbanization experiences different speeds and forms under diverse development stages across the globe. However, urbanization-induced impacts on long-term surface urban heat island intensity (I s ) trends across global cities and the regulators of such impacts remain understudied. Here we estimate interannual trends in daytime I s (i.e., urban-rural differences in surface temperatures) across 511 major cities for 1985 –2020 using annual averages calculated by using reconstructed land surface temperature data derived from more than >250,000 Landsat thermal images. Our study reveals that the global mean I s growth rate is 0.156 °C/decade. We further examine I s change associated with per 1% impervious land growth (denoted as ß) in each city throughout the research period and during different periods. The global mean ß is 0.018 ± 0.025 °C/% (mean ± 1 standard deviation) for the whole period, with greater values in humid than in arid climates; and the ß may change during different periods, e.g., it has more than tripled when urban impervious land exceeds 30%, indicating the spatiotemporally divergent impacts of urbanization on Is trends across global cities. The spatial variations in ß across global cities are well correlated with rural vegetation abundance and precipitation but not with urban population. Among these three factors, rural vegetation abundance possesses the greatest standardized regression coefficient of partial least-squares model, signifying the critical role of biome background in regulating ß. Furthermore, the finding implies that future urbanization over densely vegetated regions should be more carefully and strategically planned due to the greater urbanization-induced surface warming effect.

54 ENVIRONMENTAL SCIENCES↗