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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Performance of Cloud 3D Solvers in Ice Cloud Shortwave Radiation Closure Over the Equatorial Western Pacific Ocean

Abstract For retrieving cloud optical properties from satellite images or computing these properties from climate model output, computationally efficient treatments of cloud horizontal inhomogeneity include the Monte Carlo Independent Column Approximation (McICA) and the Tripleclouds method. Computationally efficient treatment of cloud horizontal radiation exchanges includes the SPeedy Algorithm for Radiative TrAnsfer through CloUd Sides (SPARTACUS). As a test to derive properties from satellite images, we collocate Moderate Resolution Imaging Spectroradiometer (MODIS) cloud retrievals with near‐nadir Cloud and the Earth's Radiant Energy System (CERES) footprints in July 2008 over an equatorial western Pacific Ocean region to compare the performance of the McICA, Tripleclouds, and SPARTACUS solvers to the conventional plane‐parallel homogeneous (PPH) treatment. PPH overestimates cloud albedo, and the three solvers effectively reduce overestimation with root mean square error of shortwave upwelling irradiance decreasing between 15.72 and 18.53 W m −2 , or about 22%–25%. Although cloud top variability does not get fed into the simulations, all three solvers also reduce the effect of cloud top variability on cloud albedo. Entrapment (energy reflected downward from clouds) and horizontal radiation transfer have opposite effects on the SPARTACUS cloud albedo simulation. The net effect depends on the cloud vertical extent, the unawareness of which limits the performance of the SPARTACUS solver.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Maritime Aerosol and CCN Profiles Derived From Ship₋Based Measurements Over Eastern North Pacific During MAGIC

Atmospheric aerosols are widely recognized to give rise to a substantial radiative forcing to the climate by scattering and absorbing radiation and by modifying the microphysical, lifetime, and radiative properties of clouds. During the Marine ARM GPCI Investigation of Clouds (MAGIC) over the Eastern North Pacific (ENP), the ship-based measured cloud condensation nuclei (CCN) concentration at 0.2% supersaturation (N CCN,0.2 ) and condensation nuclei concentration (N CN ) had mean values of 116.7 and 219.4 cm ⁻3 , with the highest concentrations found closest to LA due to an increase in aerosol sources. Moving westward, both N CCN,0.2 and N CN gradually decreased until stabilizing near 100 cm ⁻3 and 200 cm ⁻3 , respectively. Using the methods proposed by Ghan and Collins (2004), https://doi.org/10.1175/1520-0426(2004)021<0387:uoisdt>2.0.co;2 and Ghan et al. (2006), https://doi.org/10.1029/2004jd005752, N CCN,0.2 profiles are retrieved using the surface measured N CCN,0.2 as a constraint. For coupled conditions, correlations between the retrieved N CCN,0.2 profiles and cloud-droplet number concentration (N C ) increase from 0.26 at the surface to 0.38 near cloud base, particularly true for non-drizzling clouds. Although the correlations are lower than expected, the percentage increase (46.2%) is encouraging. Finally, the relationships between cloud breakup (CB) and the stratocumulus to cumulus transition (SCT) with environmental conditions and associated aerosols are also studied. The decreased N CN trend east of CB is mainly caused by precipitation scavenging, while the increased N CN trend west of CB is strongly associated with the increased surface wind speed and fewer drizzle events. A further study is needed using high-resolution models to simulate these events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Using Dark Fiber and Distributed Acoustic Sensing to Characterize a Geothermal System in the Imperial Valley, Southern California

The Imperial Valley, CA, is a tectonically active transtensional basin located south of the Salton Sea; the area hosts numerous geothermal fields, including significant hidden hydrothermal resources without surface manifestations. Development of inexpensive, rugged, and highly sensitive exploration techniques for undiscovered geothermal systems is critical for accelerating geothermal power deployment as well as unlocking a low-carbon energy future. We present a case study utilizing distributed acoustic sensing (DAS) and ambient noise interferometry for geothermal reservoir imaging, utilizing unlit fiber-optic telecommunication infrastructure (dark fiber). The study exploits two days of passive DAS data acquired in early November 2020 over a ~28-km section of fiber from Calipatria, CA to Imperial, CA. We apply ambient noise interferometry to retrieve coherent signals from DAS records and develop a bin stacking technique to attenuate the effects from persistent localized noise sources and to enhance retrieval of coherent surface waves. As a result, we are able to obtain high-resolution two-dimensional (2D) S wave velocity ($V_s$) structure to 3 km depth, based on joint inversion of both the fundamental and higher overtones. We observe a previously unmapped high $V_s$ and low $V_p$/$V_s$ ratio feature beneath the Brawley geothermal system, which we interpret to be a zone of hydrothermal mineralization and lower porosity. This interpretation is consistent with a host of other measurements including surface heat flow, gravity anomalies, and available borehole wireline data. These results demonstrate the potential utility of DAS deployed on dark fiber for geothermal system exploration and characterization in the appropriate geological settings.

Vp/Vs imaging↗
DOE OSTI2022

Peak Rain Rate Sensitivity to Observed Cloud Condensation Nuclei and Turbulence in Continental Warm Shallow Clouds During CACTI

Abstract Warm clouds strongly affect Earth's energy budget but remain imperfectly represented in climate models, partly due to the complexity and covariability of relevant processes influencing warm rain. This work presents a detailed analysis of different factors affecting rain rate peak intensity (RR) in continental warm clouds. Clouds were identified with vertically pointing radar and lidar observations and categorized via a temperature‐based cloud type classification algorithm from which warm clouds were isolated. Observations and retrievals of liquid water path (LWP), cloud condensation nuclei concentration (N CCN ), cloud depth, and cloud duration of more than 3,000 separate warm clouds sampled during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign are analyzed in this work. Multiple linear regression (MLR) and random forest (RF) models are applied to assess the relative impact of these variables on RR. Overall, RR tends to increase as cloud depth, LWP, and cloud duration increase, or N CCN decreases. Cloud depth affects RR the most while N CCN impacts it the least. When considering over 170 warm clouds observed at least 1 hr in which in‐cloud turbulence is retrieved, the effect of N CCN on RR remains most likely suppressive, but it is not significant at a 75% level for MLR and is highly uncertain for RF. The impact of in‐cloud turbulence depends on the moment and location it is sampled. Cloud base turbulence around the time of RR suppresses RR, while cloud top turbulence effects are inconclusive. Possible difficulties in isolating robust CCN and turbulence effects on RR are discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Impact of Seasonal Snow-Cover Change on the Observed and Simulated State of the Atmospheric Boundary Layer in a High-Altitude Mountain Valley

The structure and evolution of the atmospheric boundary layer (ABL) under clear-sky fair weather conditions over mountainous terrain is dominated by the diurnal cycle of the surface energy balance and thus strongly depends on surface snow cover. We use data from three passive ground-based infrared spectrometers deployed in the East River Valley in Colorado's Rocky Mountains to investigate the response of the thermal ABL structure to changes in surface energy balance during the seasonal transition from low to high snow cover. Temperature profiles were retrieved from the infrared radiances using the optimal estimation physical retrieval Tropospheric Remotely Observed Profiling via Optimal Estimation. A nocturnal surface inversion formed in the valley during clear-sky days, which was subsequently mixed out during daytime with the development of a convective boundary layer when snow cover was low. Over high snow cover, a very shallow convective boundary layer formed, above which the inversion persisted through the daytime hours. We compare these observations to NOAA's operational High-Resolution-Rapid-Refresh model and find large warm biases on clear-sky days resulting from the model's inability to form strong nocturnal inversions and to maintain the stable stratification in the valley during daytime when there was snow on the ground. We suggest several factors contributing to the large model errors. These are (a) the inability of the model to represent well-developed thermally driven flows likely due to the too coarse horizontal grid spacing (3 km), (b) too much convective mixing during daytime, and (c) too strong vertical coupling between the valley atmosphere and the free troposphere.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Evaluating the Representations of Atmospheric Rivers and Their Associated Precipitation in Reanalyses With Satellite Observations

Atmospheric rivers (ARs) are filaments of enhanced horizontal moisture transport in the atmosphere. Due to their prominent role in the meridional moisture transport and regional weather extremes, ARs have been studied extensively in recent years. Yet, the representations of ARs and their associated precipitation on a global scale remains largely unknown. In this study, we developed an AR detection algorithm specifically for satellite observations using moisture and the geostrophic winds derived from 3D geopotential height field from the combined retrievals of the Atmospheric Infrared Sounder and the Advanced Microwave Sounding Unit on NASA Aqua satellite. This algorithm enables us to develop the first global AR catalog based solely on satellite observations. The satellite–based AR catalog is then combined with the satellite–based precipitation (Integrated Muti–SatellitE Retrievals for GPM) to evaluate the representations of ARs and AR–induced precipitation in reanalysis products. Here our results show that the spreads in AR frequency and AR length distribution are generally small across data sets, while the spread in AR width is relatively larger. Reanalysis products are found to consistently underestimate both mean and extreme AR–related precipitation. However, all reanalyses tend to precipitate too often under AR conditions, especially over low latitude regions. This finding is consistent with the “drizzling” bias which has plagued generations of climate models. Overall, the findings of this study can help to improve the representations of ARs and associated precipitation in reanalyses and climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Boundary Layer Structures Over the Northwest Atlantic Derived From Airborne High Spectral Resolution Lidar and Dropsonde Measurements During the ACTIVATE Campaign

The Planetary Boundary Layer height (PBLH) is essential for studying PBL and ocean-atmosphere interactions. Marine PBL is usually defined to include a mixed layer (ML) and a capping inversion layer. The ML height (MLH) estimated from the measurements of aerosol backscatter by a lidar was usually compared with PBLH determined from radiosondes/dropsondes in the past, as the PBLH is usually similar to MLH in nature. However, PBLH can be much greater than MLH for decoupled PBL. Here, in this study, we evaluate the retrieved MLH from an airborne lidar (HSRL-2) by utilizing 506 co-located dropsondes during the ACTIVATE field campaign over the Northwest Atlantic from 2020 to 2022. First, we define and determine the MLH and PBLH from the temperature and humidity profiles of each dropsonde, and find that the MLH values from HSRL-2 and dropsondes agree well with each other, with a coefficient of determination of 0.66 and median difference of 18 m. In contrast, the HSRL-2 MLH data do not correspond to dropsonde-derived PBLH, with a median difference of -47 m. Therefore, we modify the current operational and automated HSRL-2 wavelet-based algorithm for PBLH retrieval, decreasing the median difference significantly to -8 m. Further data analysis indicates that these conclusions remain the same for cases with higher or lower cloud fractions, and for decoupled PBLs. These results demonstrate the potential of using HSRL-2 aerosol backscatter data to estimate both marine MLH and PBLH and suggest that lidar-derived MLH should be compared with radiosonde/dropsonde-determined MLH (not PBLH) in general.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Incorporating an Interactive Fire Plume-Rise Model in the DOE's Energy Exascale Earth System Model Version 1 (E3SMv1) and Examining Aerosol Radiative Effect

The vertical distribution of biomass burning aerosol (BBA) is important in regulating their impacts on weather and climate. The plume-rise process affects the injection height of BBA and interacts with the air parcel lifting and cloud processes. However, these processes are not represented in most global climate models. In this study, we replaced the fixed vertical profiles of monthly BBA emissions in the Department of Energy's Energy Exascale Earth System Model version 1 (E3SMv1) with an interactive fire plume-rise model. The vertical distribution of BBA emissions was calculated as a function of ambient thermodynamic conditions from the host E3SMv1, with distributions of fire sizes and sensible heat fluxes derived from the observations. The maximum fire radiative power (FRP) technique was used to determine the fire size. Scaling-FRP technique is used to calculate the wildfire heat release. Daily BBA emission, superimposed with a fire diurnal cycle retrieved from the satellite observation, was included in model simulations. The model shows improved agreement with satellite retrievals and in situ measurement during the National Oceanic and Atmospheric Administration Wildfire Experiment for Cloud chemistry, Aerosol absorption, and Nitrogen campaign. The model-observation comparison demonstrates the importance of the plume-rise model and fire diurnal cycle assumption in determining the BBA fields. We also find that E3SMv1 with new features produces a larger carbonaceous aerosol burden, leading to 0.13 W m –2 warming at the top of atmosphere compared to the default E3SMv1. This highlights the importance of accurately representing the BBA injection height and indicates a no-linear nature in the BBA-induced radiative effect.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A Comprehensive Northern Hemisphere Particle Microphysics Data Set From the Precipitation Imaging Package

Microphysical observations of precipitating particles are critical data sources for numerical weather prediction models and remote sensing retrieval algorithms. However, obtaining coherent data sets of particle microphysics is challenging as they are often unindexed, distributed across disparate institutions, and have not undergone a uniform quality control process. This work introduces a unified, comprehensive Northern Hemisphere particle microphysical data set from the National Aeronautics and Space Administration precipitation imaging package (PIP), accessible in a standardized data format and stored in a centralized, public repository. Data is collected from 10 measurement sites spanning 34° latitude (37°N–71°N) over 10 years (2014–2023), which comprise a set of 1,070,000 precipitating minutes. The provided data set includes measurements of a suite of microphysical attributes for both rain and snow, including distributions of particle size, vertical velocity, and effective density, along with higher-order products including an approximation of volume-weighted equivalent particle densities, liquid equivalent snowfall, and rainfall rate estimates. The data underwent a rigorous standardization and quality assurance process to filter out erroneous observations to produce a self-describing, scalable, and achievable data set. Case study analyses demonstrate the capabilities of the data set in identifying physical processes like precipitation phase-changes at high temporal resolution. Bulk precipitation characteristics from a multi-site intercomparison also highlight distinct microphysical properties unique to each location. This curated PIP data set is a robust database of high-quality particle microphysical observations for constraining future precipitation retrieval algorithms, and offers new insights toward better understanding regional and seasonal differences in bulk precipitation characteristics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Comparisons of the v11.1 Orbiting Carbon Observatory‐2 (OCO‐2) X CO2 Measurements With GGG2020 TCCON

The Orbiting Carbon Observatory 2 (OCO-2) is NASA's first Earth observation satellite mission dedicated to studying the sources and sinks of carbon dioxide (CO 2 ) on a global scale. The observations of reflected sunlight are inverted in a retrieval algorithm to produce estimates of the dry air mole-fractions of CO 2 (X CO2 ). The OCO-2 Level 2 data release, version 11.1 (v11.1) retrievals from the Atmospheric Carbon Observations from Space (ACOS) algorithm, includes significant improvements in the X CO2 data product compared to older OCO-2 data versions. This work compares the v11.1 X CO2 from OCO-2 against X CO2 estimates collected from a global ground-based network known as the Total Carbon Column Observing Network (TCCON), OCO-2's primary validation source. The OCO-2 project provides a version of the Level 2 data product, called “lite” files that include calibrated and bias-corrected XCO2 values, accessible together with all OCO-2 data products through the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). This work shows that OCO-2 X CO2 observations made between September 2014 and December 2023, after quality filtering and the application of an averaging kernel correction, agree well with coincident TCCON data for all OCO-2 observational modes of land (nadir, glint, target) and ocean (glint). The aggregated, bias-corrected, and quality-filtered absolute average bias values are less than or equal to 0.20 parts per million (ppm) globally for all OCO-2 observation modes, where the biases do not indicate a statistically significant time dependence. The land nadir/glint mode has the lowest bias value of −0.03 ± 0.85 ppm.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Remote Sensing of Live Fuel Moisture for Wildfires Using SMAP Satellite Observations

Live Fuel Moisture (LFM) is a critical parameter for wildfire risk assessment, traditionally measured by labor-intensive field sampling. However, sampled LFM data are influenced by site-specific factors, such as local vegetation types and plant traits, and are often collected retrospectively after wildfire events, making it difficult to obtain pre-fire data for predictive applications. Here, we evaluate the relationship between LFM and Vegetation Water Content (VWC) and Soil Moisture (SM) retrieved from SMAP L-band brightness temperature using the Maximum Entropy Production (MEP) approach. The MEP-retrieved VWC exhibited strong correlation with in situ measurements of LFM ( r > 0.6) in the Western U.S. The integration of high-resolution vegetation coverage data enhances the detection of sub-grid vegetation heterogeneity. This study demonstrates the operational potential of remote sensing derived VWC as a scalable proxy of LFM, supporting its application in regional assessment of wildfire risk.

Cho, Kyeungwoo [Georgia Institute of Technology, A↗
DOE OSTI2026

High-Resolution LiDAR Observations for Coupled Effects of Dry-Air Entrainment and Haze-Cloud Interactions on Cloud Vertical Structure

This study demonstrates the high-resolution profiling of cloud microphysics in a laboratory chamber using Time-Correlated Single Photon Counting (TCSPC) LiDAR. We present a novel retrieval method to derive vertical extinction (𝜎) profiles, constrained by in situ measurements, to diagnose responses to dry-air entrainment. In clean clouds, the LiDAR signals and retrieved 𝜎 remain relatively uniform, with entrainment effects confined to the upper layer. In contrast, polluted clouds exhibit strong vertical variability and a transition to a water-vapor-limited state. Entrainment significantly enhances the haze number concentration (𝑁ℎ), particularly near the bottom, creating highly height-dependent extinction profiles for polluted clouds. Our results highlight the capability of high-resolution LiDAR in capturing fine-scale vertical inhomogeneities. This approach provides a robust framework for quantifying how aerosol loading modulates entrainment sensitivity, offering new insights into the transition between buffered and water-vapor-limited regimes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

AI-enabled Lorentz microscopy for quantitative imaging of nanoscale magnetic spin textures

The manipulation and control of nanoscale magnetic spin textures are of rising interest as they are potential foundational units in next-generation computing paradigms. Achieving this requires a quantitative understanding of the spin texture behavior under external stimuli using in situ experiments. Lorentz transmission electron microscopy (LTEM) enables real-space imaging of spin textures at the nanoscale, but quantitative characterization of in situ data is extremely challenging. Here, we present an AI-enabled phase-retrieval method based on integrating a generative deep image prior with an image formation forward model for LTEM. Our approach uses a single out-of-focus image for phase retrieval and achieves significantly higher accuracy and robustness to noise compared to existing methods. Furthermore, our method is capable of isolating sample heterogeneities from magnetic contrast, as shown by application to simulated and experimental data. This approach allows quantitative phase reconstruction of in situ data and can also enable near real-time quantitative magnetic imaging.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Optical emissivity dataset of multi-material heterogeneous designs generated with automated figure extraction

Optical device design is typically an iterative optimization process based on a good initial guess from prior reports. Optical properties databases are useful in this process but difficult to compile because their parsing requires finding relevant papers and manually converting graphical emissivity curves to data tables. Here, we present two contributions: one is a dataset of thermal emissivity records with design-related parameters, and the other is a software tool for automated colored curve data extraction from scientific plots. We manually collected 64 papers with 176 figures reporting thermal emissivity and automatically retrieved 153 colored curve data records. The automated figure analysis software pipeline uses Faster R-CNN for axes and legend object detection, EasyOCR for axes numbering recognition, and k-means clustering for colored curve retrieval. Additionally, we manually extracted geometry, materials, and method information from the text to add necessary metadata to each emissivity curve. Finally, we analyzed the dataset to determine the dominant classes of emissivity curves and determine the underlying design parameters leading to a type of emissivity profile.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

3D magnetic imaging using electron vortex beam microscopy

Abstract Electron vortex beams are free-electron waves that carry orbital angular momentum. There has been growing theoretical and experimental interest in the use of electron vortex beams as a tool for the investigation of magnetic materials. However, due to the complex wavefront of the propagating waves, a deeper understanding of the interaction of electron vortex beams and the magnetic sample is needed. Here we calculate the magnetic phase shift that an electron vortex beam obtains upon transmitting through a magnetic sample. We show that this magnetic phase shift is influenced by the out-of-plane magnetization, which is a unique characteristic of incident electron vortex beams and is proportional to their orbital angular momentum. Finally, we develop a phase retrieval methodology to retrieve the out-of-plane component of magnetization. Based on our theory, we discuss suitable experimental conditions that would enable this imaging capability for magnetic materials and further extend to non-magnetic chiral materials.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Laser-induced electron diffraction of the ultrafast umbrella motion in ammonia

Visualizing molecular transformations in real-time requires a structural retrieval method with Ångström spatial and femtosecond temporal atomic resolution. Imaging of hydrogen-containing molecules additionally requires an imaging method sensitive to the atomic positions of hydrogen nuclei, with most methods possessing relatively low sensitivity to hydrogen scattering. Laser-induced electron diffraction (LIED) is a table-top technique that can image ultrafast structural changes of gas-phase polyatomic molecules with sub-Ångström and femtosecond spatiotemporal resolution together with relatively high sensitivity to hydrogen scattering. Here, we image the umbrella motion of an isolated ammonia molecule (NH3) following its strong-field ionization. Upon ionization of a neutral ammonia molecule, the ammonia cation (NH3+) undergoes an ultrafast geometrical transformation from a pyramidal (ΦHNH=107°) to planar (ΦHNH=120°) structure in approximately 8 femtoseconds. Using LIED, we retrieve a near-planar (ΦHNH=117 ± 5°) field-dressed NH3+ molecular structure 7.8−9.8 femtoseconds after ionization. Our measured field-dressed NH3+ structure is in excellent agreement with our calculated equilibrium field-dressed structure using quantum chemical ab initio calculations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

AI-enabled high-resolution scanning coherent diffraction imaging

Ptychographic imaging is a powerful means of imaging beyond the resolution limits of typical x-ray optics. Capturing recovered images from raw ptychographic data, however, requires the solution of an inverse problem, namely, phase retrieval. Phase retrieval algorithms are computationally expensive, which precludes real-time imaging. In this work, we propose PtychoNN, an approach to solve the ptychography data inversion problem based on a deep convolutional neural network. We demonstrate how the proposed method can be used to predict real-space structure and phase at each scan point solely from the corresponding far-field diffraction data. Our results demonstrate the practical application of machine learning to recover high fidelity amplitude and phase contrast images of a real sample hundreds of times faster than current ptychography reconstruction packages. Furthermore, by overcoming the constraints of iterative model-based methods, we can significantly relax sampling constraints on data acquisition while still producing an excellent image of the sample. Besides drastically accelerating acquisition and analysis, this capability has profound implications for the imaging of dose sensitive, dynamic, and extremely voluminous samples.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Inversion of coherent surface scattering images via deep learning network

Coherent surface scattering imaging is a powerful tool for imaging a surface/interface of a thin nanostructure deposited on an opaque substrate. A mathematical conversion of an object image from a scattering pattern is essential for coherent surface scattering imaging to visualize structures of specimens. It has been achieved by using phase retrieval algorithms requiring oversampling in scattering patterns and employing alternating projection approaches. It is a computationally challenging and time-consuming process. In this paper, we demonstrate CSSI-NN, which is a deep learning neural network model to predict images of objects from scattering intensities in coherent surface scattering imaging. This model allowing for the instant outcome from scattering patterns would be tremendously beneficial not only for effective experiments but also for data analysis of phase retrieval.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗