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At least 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Future Projections of EURO‐CORDEX Raw and Bias‐Corrected Daily Maximum Wind Speeds Over Scandinavia

Twenty historical and future Coordinated Regional Climate Downscaling Experiment ensemble for Europe simulations are bias corrected to investigate the future changes in the daily maximum wind speed over Scandinavia. We use quantile mapping to adjust the wind for the historical period (1985–2014) and quantile delta mapping for two future periods (2041–2070, 2071–2100, RCP8.5) with the 3-km spatial resolution NORA3 hindcast as the reference data set. Decomposing the variance, we find that most of the inter-model spread in the bias and the response to climate change is due to the regional climate model over land and mainly to the general climate model over sea. On average, the mean daily maximum wind speed is projected to increase everywhere except along the western coast of Norway and Denmark. In summer, we see an opposite sign over Sweden and Finland. The Norwegian Sea experiences weaker mean and high wind whereas the Baltic Sea experiences a strengthening. Bias correction influences the amplitude of the response, not the response pattern. Wind speed distributions can have different shapes in the future, with, for example, a flattening of the distribution off the coast of Norway with more frequent weak and very strong winds. Apart from a few locations in Norway, we find an increase in the number of days in the local highest historical wind category. Overall, summer exhibits opposite signals compared to the three other seasons. At country scale, the sign of the change in the mean and 98th percentile varies greatly depending on the region and among the simulations, especially for Norway.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Quantifying Seasonal and Diurnal Cycles of Solar‐Induced Fluorescence With a Novel Hyperspectral Imager

Abstract Solar‐induced fluorescence (SIF) is a proxy of ecosystem photosynthesis that often scales linearly with gross primary productivity (GPP) at the canopy scale. However, the mechanistic relationship between GPP and SIF is still uncertain, especially at smaller temporal and spatial scales. We deployed a ultra‐hyperspectral imager over two grassland sites in California throughout a soil moisture dry down. The imager has high spatial resolution that limits mixed pixels, enabling differentiation between plants and leaves within one scene. We find that imager SIF correlates well with diurnal changes in leaf‐level physiology and gross primary productivity under well‐watered conditions. These relationships deteriorate throughout the dry down event. Our results demonstrate an advancement in SIF imaging with new possibilities in remotely sensing plant canopies from the leaf to the ecosystem. These data can be used to resolve outstanding questions regarding SIF's meaning and usefulness in terrestrial ecosystem monitoring.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Assessment of Storm-Associated Precipitation and Its Extremes Using Observational Data Sets and Climate Model Short-Range Hindcasts

Heavy precipitation, often associated with weather phenomena such as tropical cyclones, extratropical cyclones (ETCs), atmospheric rivers (ARs), and mesoscale convective systems (MCSs), can cause significant socio-economic loss. Here, in this study, we apply atmospheric feature trackers to quantify the contributions of these storm types in observational data sets and climate model short-range hindcasts. We generate a global hourly storm data set at 0.25° spatial resolution covering 2006–2020, based on the tracking results from TempestExtremes and Python FLEXible object TRacKeR. Our analyses show that these four storm types account for 67% of global annual mean precipitation and 82% of top 1% precipitation extremes, with MCSs mainly over the tropics, and ARs and ETCs over the midlatitudes. The percentage of precipitation contributions from these storms also show strong seasonality over many geographical locations. We further apply the tracking results to the Energy Exascale Earth System Model (E3SM) short-range hindcasts and evaluate how well these storms are simulated. The evaluation show that E3SM, with ∼1° resolution, significantly underestimates storm-associated precipitation totals and extremes, especially for MCSs in the tropics. Our analysis also suggests that model fails to capture the correct mean diurnal phases and amplitude of MCS precipitation. This phenomenon-based approach provides a better understanding of precipitation characteristics and can lead to enhanced model evaluation by revealing underlying problems in model physics related to precipitation processes associated with the heavy-precipitating storms.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Temporal Error Correlations in a Terrestrial Carbon Cycle Model Derived by Comparison to Carbon Dioxide Eddy Covariance Flux Tower Measurements

Abstract Atmospheric CO 2 flux inversions require as input an estimate of spatial and temporal correlations of errors in their estimate of the prior mean. Some previous studies have used the differences in CO 2 daily average flux estimates produced by terrestrial carbon cycle models and eddy covariance measurements to constrain the flux error correlations. Since inversions are starting to resolve the daily cycle, we set out to examine the correlations at sub‐daily time scales, as well as the correlations across years. To this end, we examine the autocorrelations in the difference between net ecosystem‐atmosphere exchange measurements from 75 AmeriFlux towers and temporally downscaled high‐spatial‐resolution flux estimates from the Carnegie‐Ames‐Stanford Approach (CASA) terrestrial carbon cycle model. We find that the daily cycle is prominent in these hourly autocorrelations and that these autocorrelations persist across years. We propose a family of functions to model these temporal correlations in atmospheric inversions, and use cross validation to determine which of the correlation functions best fits autocorrelation data from towers not in the training set. Correlation functions with a component that attempts to model the daily cycle in the differences match correlations from other towers better than those without. Those models that reproduce the same correlation structures at 1‐year intervals while modulating the amplitudes of the correlations between those intervals improve the fit still further.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Future Climate Projections for South Florida: Improving the Accuracy of Air Temperature and Precipitation Extremes With a Hybrid Statistical Bias Correction Technique

Projecting future climate variables is essential for comprehending the potential impacts on hydroclimatic hazards like floods and droughts. Evaluating these impacts is challenging due to the coarse spatial resolution of global climate models (GCMs); therefore, bias correction is widely used. Here, we applied two statistical methods—standard empirical quantile mapping (EQM) and a hybrid approach, EQM with linear correction (EQM-LIN)—to bias correct precipitation and air temperature simulated by nine GCMs. We used historical observations from 20 weather stations across South Florida to project future climate under three shared socioeconomic pathways (SSPs). Compared to the EQM, the hybrid EQM-LIN method improved R 2 of daily quantiles by up to 30% over the historical period and improved MAE up to 70% in months that contain most extreme values. Projected extreme precipitation at the weather stations showed that, compared to the EQM-LIN, the EQM method underestimates the high quantiles by up to 26% in SSP585. The projected changes in annual maximum precipitation from historical period (1985–2014) to near future (2040–2069) and far future (2070–2100) were between 2% and 16% across the study area. Projected future precipitation suggested a slight decrease during summer but an increase in fall. This, along with rising summer temperatures, suggested that South Florida can experience rapid oscillations from warmer summers and increased flooding in fall under future climate. Additionally, our comparative analyses with globally and nationally downscaled studies showed that such coarse scale studies do not represent the climatic extremes well, particularly for high quantile precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Projecting Large Fires in the Western US With an Interpretable and Accurate Hybrid Machine Learning Method

More frequent and widespread large fires are occurring in the western United States (US), yet reliable methods for predicting these fires, particularly with extended lead times and a high spatial resolution, remain challenging. In this study, we proposed an interpretable and accurate hybrid machine learning (ML) model, that explicitly represented the controls of fuel flammability, fuel availability, and human suppression effects on fires. The model demonstrated notable accuracy with a F 1 -score of 0.846 ± 0.012, surpassing process-driven fire danger indices and four commonly used ML models by up to 40% and 9%, respectively. More importantly, the ML model showed remarkably higher interpretability relative to other ML models. Specifically, by demystifying the “black box” of each ML model using the explainable AI techniques, we identified substantial structural differences across ML fire models, even among those with similar accuracy. The relationships between fires and their drivers, identified by our model, were aligned closer with established fire physical principles. The ML structural discrepancy led to diverse fire predictions and our model predictions exhibited greater consistency with actual fire occurrence. With the highly interpretable and accurate model, we revealed the strong compound effects from multiple climate variables related to evaporative demand, energy release component, temperature, and wind speed, on the dynamics of large fires and megafires in the western US. Our findings highlight the importance of assessing the structural integrity of models in addition to their accuracy. They also underscore the critical need to address the rise in compound climate extremes linked to large wildfires.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Influence of Atmospheric Slant Path on Geostationary Hyperspectral Infrared Sounder Radiance Simulations

Accurately simulating a geostationary hyperspectral infrared sounder is critical for quantitative applications. Traditional radiation simulations of such instruments often overlook the influence of slant observation geometry by using vertical profile assumption, leading to inadequate simulation accuracy. By using global atmospheric profiles with 1 km spatial resolution, the slant-path effects on brightness temperature simulations are quantified. Experiments indicate that the slant geometry has less impact on longwave brightness temperature simulations and has a substantial impact on middle-wave brightness temperature simulations. It may introduce 0.5 K (or more) uncertainty to brightness temperatures of water vapor absorption channels when the satellite zenith angle is greater than 45°. Considering the slant profile is recommended for quantitative applications of geostationary hyperspectral sounder data, such as sounding retrieval and data assimilation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Estimating Carbon Dioxide Emissions in Two California Cities Using Bayesian Inversion and Satellite Measurements

NASA's Orbiting Carbon Observatories (OCO-2 and OCO-3) provide measurements of column-averaged carbon dioxide concentrations (XCO 2 ) with sufficient spatial resolution and precision to constrain bottom-up estimates of CO 2 fluxes at regional scales. We use Bayesian inversion methods assimilating satellite retrievals to improve estimates of CO 2 fluxes in the South Coast Air Basin (SoCAB) which surrounds Los Angeles, and in the San Francisco Bay Area Air Basin (SFBA). We study 2020 to understand the impact of the COVID-19 lockdowns and an active wildfire season. Our results indicated that a 50% (30%) reduction in CO 2 emissions relative to 2015 during the COVID-19 lockdown period was consistent with OCO measurements for SFBA (SoCAB). We find that posterior wildfire emissions differed significantly from the prior at the scale of individual wildfires, though with large uncertainties, and that wildfire emissions in SFBA are significant, attributing 72% of the region's CO 2 emissions during August 2020 to wildfires.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

DASEventNet: AI‐Based Microseismic Detection on Distributed Acoustic Sensing Data From the Utah FORGE Well 16A (78)‐32 Hydraulic Stimulation

Abstract Distributed acoustic sensing (DAS) has emerged as a promising seismic technology for monitoring microearthquakes (MEQs) with high spatial resolution. Efficient algorithms are needed for processing large DAS data volumes. This study introduces a deep learning (DL) model based on a Residual Convolutional Neural Network (ResNet) for detecting MEQs using DAS data, named as DASEventNet. The test data were collected from the Utah FORGE 16A (78)‐32 hydraulic stimulation experiments conducted in April 2022. The DASEventNet model achieves a remarkable accuracy of 100% when discriminating MEQs from noise in the raw test set of 260 examples. Surprisingly, the model identified weak MEQ signatures that have been manually categorized as noise. The decision‐making process with the model is decoded by the classic activation map, which illuminates learning features of the DASEventNet model. These features provide clear illustrations of weak MEQs and varied noise types. Finally, we apply the trained model to the entire period (∼7 days) of continuous DAS recordings and find that it discovers >5,700 new MEQs, previously unregistered in the public Silixa DAS catalog. The DASEventNet model significantly outperforms the traditional seismic method Short‐Term Average/Long‐Term Average (STA/LTA), which detected only 1,307 MEQs. The DASEventNet detection threshold is M w −1.80 compared to the minimum magnitude of M w −1.14 detected by STA/LTA. The spatiotemporal distribution of the newly identified MEQs defines an extensive stimulation zone and more accurately characterizes fracture geometry. Our results highlight the potential of DL for long‐term, real‐time microseismic monitoring that can improve enhanced geothermal systems and other activities that include subsurface hydraulic fracturing.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Remote Sensing Improves Multi‐Hazard Flooding and Extreme Heat Detection by Fivefold Over Current Estimates

The co‐occurrence of multiple hazards is of growing concern globally as the frequency and magnitude of extreme climate events increases. Despite studies examining the spatial distribution of such events, there has been little work in examining if all relevant life threatening and damaging hazards are captured in existing hazard databases and by common hazard metrics. For example, local/regional flash flooding events are seldom captured by optical satellite instruments and are subsequently excluded from global hazard databases. Similarly, the heat hazard definitions most frequently used in multi‐hazard studies inherently fail to capture events that are life‐threatening but climatologically within an expected range. Our goal is to determine the potential for increasing multi‐hazard event detection capabilities by inferring additional hazard footprints from widely accessible satellite data. We use daily precipitation and temperature satellite data to develop an open‐source framework that infers additional hazard footprints that are not included in traditional methods. With the state of Texas as our study area, we detected 2.5 times as many flood hazards, equivalent to $320 million in property and crop damages. Furthermore, our expanded heat hazard definition increases the impacted area by 56.6%, equivalent to 91.5 million km 2 over an 18 year period. Increasing hazard detection capabilities and expanding existing definitions of hazards using daily satellite data increases the temporal and spatial resolutions at which multi‐hazard events are detected. Having more complete data sets of all relevant hazard extents improves our ability to track global trends and more accurately determine the magnitude of hazard exposure inequities.

equity↗
DOE OSTI2026

Neural Network‐Based Methods for Ocean Surface Wave Measurement Using Submarine Distributed Acoustic Sensing (DAS)

Two new data-driven models for estimating ocean surface waves from distributed acoustic sensing (DAS) submarine cable strain rate are developed using supervised machine learning on a 10-day data set collected offshore of Oliktok Point, Alaska. The new models were trained on target data from seafloor pressure moorings at three sites spaced evenly along 27.1 km of cable and were benchmarked against an empirical transfer function method previously used to estimate waves from DAS. A model which uses convolutional neural networks to transform 2-km frequency-wavenumber strain spectra to seafloor pressure spectra outperforms the benchmark in wave height prediction (RMSE of 0.15 vs. 0.41 m) and period prediction (0.29 vs. 0.37 s) when evaluated on a held-out test data set. When applied to a DAS data set collected on the same cable 2 years prior, the CNN-based model maintained similar significant wave height performance (RMSE = 0.23 m) relative to available satellite altimetry data. A two-hidden-layer, fully connected neural network which transforms 1-D strain spectra to seafloor pressure spectra also outperforms the benchmark in wave height prediction (RMSE of 0.19 vs. 0.41 m), but does not generalize as well to the prior data. Regression-based machine learning is useful for estimating waves from DAS data when the pressure-strain relationship varies temporally and spatially across different wave conditions. Models can be applied to DAS data to measure waves with higher spatial resolution and longer temporal coverage than traditional methods, which often measure waves only at a single point.

Davis, Jacob R. [Univ. of Washington, Seattle, WA ↗
DOE OSTI2020

Observation of the molecular response to light upon photoexcitation

Abstract When a molecule interacts with light, its electrons can absorb energy from the electromagnetic field by rapidly rearranging their positions. This constitutes the first step of photochemical and photophysical processes that include primary events in human vision and photosynthesis. Here, we report the direct measurement of the initial redistribution of electron density when the molecule 1,3-cyclohexadiene (CHD) is optically excited. Our experiments exploit the intense, ultrashort hard x-ray pulses of the Linac Coherent Light Source (LCLS) to map the change in electron density using ultrafast x-ray scattering. The nature of the excited electronic state is identified with excellent spatial resolution and in good agreement with theoretical predictions. The excited state electron density distributions are thus amenable to direct experimental observation.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Two-photon excited deep-red and near-infrared emissive organic co-crystals

Abstract Two-photon excited near-infrared fluorescence materials have garnered considerable attention because of their superior optical penetration, higher spatial resolution, and lower optical scattering compared with other optical materials. Herein, a convenient and efficient supramolecular approach is used to synthesize a two-photon excited near-infrared emissive co-crystalline material. A naphthalenediimide-based triangular macrocycle and coronene form selectively two co-crystals. The triangle-shaped co-crystal emits deep-red fluorescence, while the quadrangle-shaped co-crystal displays deep-red and near-infrared emission centered on 668 nm, which represents a 162 nm red-shift compared with its precursors. Benefiting from intermolecular charge transfer interactions, the two co-crystals possess higher calculated two-photon absorption cross-sections than those of their individual constituents. Their two-photon absorption bands reach into the NIR-II region of the electromagnetic spectrum. The quadrangle-shaped co-crystal constitutes a unique material that exhibits two-photon absorption and near-infrared emission simultaneously. This co-crystallization strategy holds considerable promise for the future design and synthesis of more advanced optical materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Organo–organic and organo–mineral interfaces in soil at the nanometer scale

The capacity of soil as a carbon (C) sink is mediated by interactions between organic matter and mineral phases. However, previously proposed layered accumulation of organic matter within aggregate organo–mineral microstructures has not yet been confirmed by direct visualization at the necessary nanometer-scale spatial resolution. Here, we identify disordered micrometer-size organic phases rather than previously reported ordered gradients in C functional groups. Using cryo-electron microscopy with electron energy loss spectroscopy (EELS), we show organo–organic interfaces in contrast to exclusively organo–mineral interfaces. Single-digit nanometer-size layers of C forms were detected at the organo–organic interface, showing alkyl C and nitrogen (N) enrichment (by 4 and 7%, respectively). At the organo–mineral interface, 88% (72–92%) and 33% (16–53%) enrichment of N and oxidized C, respectively, indicate different stabilization processes than at organo–organic interfaces. However, N enrichment at both interface types points towards the importance of N-rich residues for greater C sequestration.

58 GEOSCIENCES↗
DOE OSTI2021

Distilling nanoscale heterogeneity of amorphous silicon using tip-enhanced Raman spectroscopy (TERS) via multiresolution manifold learning

Abstract Accurately identifying the local structural heterogeneity of complex, disordered amorphous materials such as amorphous silicon is crucial for accelerating technology development. However, short-range atomic ordering quantification and nanoscale spatial resolution over a large area on a-Si have remained major challenges and practically unexplored. We resolve phonon vibrational modes of a-Si at a lateral resolution of <60 nm by tip-enhanced Raman spectroscopy. To project the high dimensional TERS imaging to a two-dimensional manifold space and categorize amorphous silicon structure, we developed a multiresolution manifold learning algorithm. It allows for quantifying average Si-Si distortion angle and the strain free energy at nanoscale without a human-specified physical threshold. The multiresolution feature of the multiresolution manifold learning allows for distilling local defects of ultra-low abundance (< 0.3%), presenting a new Raman mode at finer resolution grids. This work promises a general paradigm of resolving nanoscale structural heterogeneity and updating domain knowledge for highly disordered materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Probing subwavelength in-plane anisotropy with antenna-assisted infrared nano-spectroscopy

Infrared nano-spectroscopy based on scattering-type scanning near-field optical microscopy (s-SNOM) is commonly employed to probe the vibrational fingerprints of materials at the nanometer length scale. However, due to the elongated and axisymmetric tip shank, s-SNOM is less sensitive to the in-plane sample anisotropy in general. In this article, we report an easy-to-implement method to probe the in-plane dielectric responses of materials with the assistance of a metallic disk micro-antenna. As a proof-of-concept demonstration, we investigate here the in-plane phonon responses of two prototypical samples, i.e. in (100) sapphire and x-cut lithium niobate (LiNbO 3 ). In particular, the sapphire in-plane vibrations between 350 cm–1 to 800 cm–1 that correspond to LO phonon modes along the crystal b- and c-axis are determined with a spatial resolution of < λ/10, without needing any fitting parameters. In LiNbO 3 , we identify the in-plane orientation of its optical axis via the phonon modes, demonstrating that our method can be applied without prior knowledge of the crystal orientation. Furthermore, our method can be elegantly adapted to retrieve the in-plane anisotropic response of a broad range of materials, i.e. subwavelength microcrystals, van-der-Waals materials, or topological insulators.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Real-time dynamics and structures of supported subnanometer catalysts via multiscale simulations

Understanding the performance of subnanometer catalysts and how catalyst treatment and exposure to spectroscopic probe molecules change the structure requires accurate structure determination under working conditions. Experiments lack simultaneous temporal and spatial resolution and could alter the structure, and similar challenges hinder first-principles calculations from answering these questions. Here, we introduce a multiscale modeling framework to follow the evolution of subnanometer clusters at experimentally relevant time scales. We demonstrate its feasibility on Pd adsorbed on CeO 2 (111) at various catalyst loadings, temperatures, and exposures to CO. We show that sintering occurs in seconds even at room temperature and is mainly driven by free energy reduction. It leads to a kinetically (far from equilibrium) frozen ensemble of quasi-two-dimensional structures that CO chemisorption and infrared experiments probe. CO adsorption makes structures flatter and smaller. High temperatures drive very rapid sintering toward larger, stable/metastable equilibrium structures, where CO induces secondary structure changes only.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Dark matter from axion strings with adaptive mesh refinement

Abstract Axions are hypothetical particles that may explain the observed dark matter density and the non-observation of a neutron electric dipole moment. An increasing number of axion laboratory searches are underway worldwide, but these efforts are made difficult by the fact that the axion mass is largely unconstrained. If the axion is generated after inflation there is a unique mass that gives rise to the observed dark matter abundance; due to nonlinearities and topological defects known as strings, computing this mass accurately has been a challenge for four decades. Recent works, making use of large static lattice simulations, have led to largely disparate predictions for the axion mass, spanning the range from 25 microelectronvolts to over 500 microelectronvolts. In this work we show that adaptive mesh refinement simulations are better suited for axion cosmology than the previously-used static lattice simulations because only the string cores require high spatial resolution. Using dedicated adaptive mesh refinement simulations we obtain an over three order of magnitude leap in dynamic range and provide evidence that axion strings radiate their energy with a scale-invariant spectrum, to within ~5% precision, leading to a mass prediction in the range (40,180) microelectronvolts.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗