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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Mapping Vegetation at Species Level with High-Resolution Multispectral and Lidar Data Over a Large Spatial Area: A Case Study with Kudzu

Mapping vegetation species is critical to facilitate related quantitative assessment, and mapping invasive plants is important to enhance monitoring and management activities. Integrating high-resolution multispectral remote-sensing (RS) images and lidar (light detection and ranging) point clouds can provide robust features for vegetation mapping. However, using multiple sources of high-resolution RS data for vegetation mapping on a large spatial scale can be both computationally and sampling intensive. Here, we designed a two-step classification workflow to potentially decrease computational cost and sampling effort and to increase classification accuracy by integrating multispectral and lidar data in order to derive spectral, textural, and structural features for mapping target vegetation species. We used this workflow to classify kudzu, an aggressive invasive vine, in the entire Knox County (1362 km2) of Tennessee (U.S.). Object-based image analysis was conducted in the workflow. The first-step classification used 320 kudzu samples and extensive, coarsely labeled samples (based on national land cover) to generate an overprediction map of kudzu using random forest (RF). For the second step, 350 samples were randomly extracted from the overpredicted kudzu and labeled manually for the final prediction using RF and support vector machine (SVM). Computationally intensive features were only used for the second-step classification. SVM had constantly better accuracy than RF, and the producer’s accuracy, user’s accuracy, and Kappa for the SVM model on kudzu were 0.94, 0.96, and 0.90, respectively. SVM predicted 1010 kudzu patches covering 1.29 km2 in Knox County. We found the sample size of kudzu used for algorithm training impacted the accuracy and number of kudzu predicted. The proposed workflow could also improve sampling efficiency and specificity. Our workflow had much higher accuracy than the traditional method conducted in this research, and could be easily implemented to map kudzu in other regions as well as map other vegetation species.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2006

Simulation of Urban Heat Island Mitigation Strategies in Atlanta, GA Using High-Resolution Land Use/Land Cover Data Set to Enhance Meteorological Modeling

The specification of land use/land cover (LULC) and associated land surface parameters in meteorological models at all scales has a major influence on modeled surface energy fluxes and boundary layer states. In urban areas, accurate representation of the land surface may be even more important than in undeveloped regions due to the large heterogeneity within the urban area. Deficiencies in the characterization of the land surface related to the spatial or temporal resolution of the data, the number of LULC classes defined, the accuracy with which they are defined, or the degree of heterogeneity of the land surface properties within each class may degrade the performance of the models. In this study, an experiment was conducted to test a new high-resolution LULC data set for meteorological simulations for the Atlanta, Georgia metropolitan area using a mesoscale meteorological model and to evaluate the effects of urban heat island (UHI) mitigation strategies on modeled meteorology for 2030. Simulation results showed that use of the new LULC data set reduced a major deficiency of the land use data used previously, specifically the poor representation of urban and suburban land use. Performance of the meteorological model improved substantially, with the overall daytime cold bias reduced by over 30%. UHI mitigation strategies were projected to offset much of a predicted urban warming between 2000 and 2030. In fact, for the urban core, the cooling due to UHI mitigation strategies was slightly greater than the warming associated with urbanization over this period. For the larger metropolitan area, cooling only partially offset the projected warming trend.

Crosson, William L.↗
DOE OSTI2024

Super Resolution for Renewable Energy Resource Data With Wind From Reanalysis Data (Sup3rWind) and Application to Ukraine [Slides]

In this work we present a novel deep learning-based downscaling method, using generative adversarial networks (GANs), for generating high-resolution wind resource data from ECMWF Reanalysis v5 data (ERA5). We show that by training a GAN model on ERA5, as opposed to coarsened high-resolution data, we achieve results that are competitive with conventional dynamical downscaling. This GAN-based downscaling method additionally reduces computational costs over dynamical downscaling by two orders of magnitude. All GANs are trained on data sampled from CONUS, selected to provide a diverse sampling of terrain conditions, and validated on observational data along with data held out from training. This cross-validation shows low error and high correlations with observations and excellent agreement with hold out data across physical distributions. Our approach is finally used to downscale 30km hourly ERA5 to 2-km 5-minute wind data, for January 2000 through December 2023, at multiple hub heights, over Ukraine, Moldova, and part of Romania. Comparisons against observational data from Meteorological Assimilation Data Ingest System (MADIS) and multiple wind farms show the same level of performance as for CONUS validation. This 24 year data record is the first member of the "super resolution for renewable energy resource data with wind from reanalysis data" dataset (Sup3rWind).

17 WIND ENERGY↗
NASA NTRS1994

An assessment of image reconstruction from balloon-borne and the IRAS data

Angular resolution and structural information from the far-infrared mapping of astronomical sources (Galactic star forming regions, spiral galaxies, etc.) made using the TIFR 1 m balloon-borne telescope and the IRAS have been compared. The effective wavelengths of the TIFR two-band photometer are 58 and 150 microns. From IRAS, the survey COADD data, additional observations (AO's) made with the survey detectors with different Macros (DPS, DSD, DPM), as well as the chopped photometric channel (CPC) data have been considered here. The observed signals have been processed using different deconvolution strategies, either based on a maximum entropy method (MEM) developed at TIFR or the HiRes package developed at IPAC. Relative merits of each of these, under different conditions of signal to noise ratio, are highlighted. The following sources have been selected for illustration: Carina complex, W31 region, IRAS 10361-5830 (all Galactic), M101 and M81 (extragalactic). The main conclusions are: far-infrared maps from MEM deconvolution of balloon-borne data have the best angular resolution; MEM deconvolution of IRAS AO's gives resolution comparable to HiRes but with less amount of computation, though the dynamic range in MEM maps is less than in HiRes maps.

Ghosh, S. K.↗
NASA NTRS1994

Implications of high-spatial-resolution thermal infrared (Termoskan) data for Mars landing site selection

Thermal infrared observations of Mars from spacecraft provide physical information about the upper thermal skin depth of the surface, which is on the order of a few centimeters in depth and thus very significant for lander site selection. The Termoskan instrument onboard the Soviet Phobos '88 spacecraft acquired the highest spatial-resolution thermal infrared data obtained for Mars, ranging in resolution from 300 m to 3 km per pixel. It simultaneously obtained broadband reflected solar flux data. Although the 6 deg N - 30 deg S Termoskan coverage only slightly overlaps the nominal Mars Pathfinder target range, the implications of Termoskan data for that overlap region and the extrapolations that can be made to other regions give important clues for optimal landing site selection.

Betts, Bruce H.↗
NASA NTRS2011

Analysing the Advantages of High Temporal Resolution Geostationary MSG SEVIRI Data Compared to Polar Operational Environmental Satellite Data for Land Surface Monitoring in Africa

Since 1972, satellite remote sensing of the environment has been dominated by polar-orbiting sensors providing useful data for monitoring the earth s natural resources. However their observation and monitoring capacity are inhibited by daily to monthly looks for any given ground surface which often is obscured by frequent and persistent cloud cover creating large gaps in time series measurements. The launch of the Meteosat Second Generation (MSG) satellite into geostationary orbit has opened new opportunities for land surface monitoring. The Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on-board MSG with an imaging capability every 15 minutes which is substantially greater than any temporal resolution that can be obtained from existing polar operational environmental satellites (POES) systems currently in use for environmental monitoring. Different areas of the African continent were affected by droughts and floods in 2008 caused by periods of abnormally low and high rainfall, respectively. Based on the effectiveness of monitoring these events from Earth Observation (EO) data the current analyses show that the new generation of geostationary remote sensing data can provide higher temporal resolution cloud-free (less than 5 days) measurements of the environment as compared to existing POES systems. SEVIRI MSG 5-day continental scale composites will enable rapid assessment of environmental conditions and improved early warning of disasters for the African continent such as flooding or droughts. The high temporal resolution geostationary data will complement existing higher spatial resolution polar-orbiting satellite data for various dynamic environmental and natural resource applications of terrestrial ecosystems.

Fensholt, R.↗
NASA NTRS2005

The NASA Short-term Prediction and Research Transition (SPoRT) Center: A Research to Operations Test Bed

Over the last three years, NASA/MSFC scientists have embarked on an effort to transition unique NASA EOS data/products and research technology to selected NWSEOs in the southeast U.S. This activity, called the Short-term Prediction and - Research Transition (SPoRT) program, supports the NASA Science Mission Directorate and its Earth-Sun System Mission to develop a scientific understanding of the Earth System and its response to natural or human-induced changes that will enable improved prediction capability for climate, weather, and natural hazards. The overarching question related to weather prediction is "How well can weather forecasting duration and reliability be improved by new space-based observations, data assimilation, and modeling?" The transition activity has included the real-time delivery of MODIS data and products to several NWS Forecast Offices. Local NWS FOs have used the MODIS data to complement the coarse resolution GOES data for a number of applications. Specialized products have also been developed and made available to local and remote offices for their weather applications. Data from &e Lightning Mapping Array (LMA) network has been used in severe storm forecasts at several offices in the region. At the regional scale and forecast horizons from 0-1 day, the next generation of high-resolution mesoscale forecast and data assimilation models have been used to provide local offices with unique weather forecasts not otherwise available. The continued use of near red-time infusion of NASA science products into high-resolution mesoscale forecast and decision-making models can be expected to improve the model initialization as well as short-term forecasts. A current focus of SPoRT is to expand collaborations to include contributions from the assimilation of AMSR-E data in the ADASIARPS forecast system (OU), inclusion of MODIS SSTs and AIRS thermodynamic profiles in the WRF, and to extend the distribution of real-time MODIS and AMSR-E data and products to the Florida coastal WFOs. A SPoRT Test bed, together with input from other interagency and university partners, will provide a means and a process to effectively transition ESE observations and technology to NWS operations and decision makers at both the globdnational and regional scales. The transition of emerging experimental products into operations through the SPoRT infrastructure will allow NASA to foster and accelerate the progress of this Science Mission Directorate research strategy over the coming years.

Jedlovec, Gary J.↗
NASA NTRS1992

High resolution gravity models combining terrestrial and satellite data

Spherical harmonic expansions to degree 360 have been developed that combine satellite potential coefficient information, terrestrial gravity data, satellite altimeter information as a direct tracking data type and topographic information. These models define improved representations of the Earth's gravitational potential beyond that available from just satellite or terrestrial data. The development of the degree 360 models, however, does not imply a uniform accuracy in the determination of the gravity field as numerous geographic areas are devoid of terrestrial data or the resolution of such data is limited to, for example, 100 km. This paper will consider theoretical and numerical questions related to the combination of the various data types. Various models of the combination process are discussed with a discussion of various correction terms for the different models. Various sources of gravity data will be described. The new OSU91 360 model will be discussed with comparisons made to previous 360 models and to other potential coefficient models that are complete to degree 50. Future directions in high degree potential coefficient models will be discussed.

Rapp, Richard H.↗
DOE OSTI2025

Estimating soybean yields from high-temporal-resolution multi-source data using deep learning

Accurate and timely crop yield prediction is crucial for ensuring food security and maintaining stable agricultural markets. In recent years, there has been a surge in interest in leveraging high-temporal-resolution, multi-source data for effective crop growth monitoring and yield estimation. A notable challenge arises from the difficulty in capturing the intricate interactions between variables across different time steps within these high-temporal-resolution time series datasets. This complexity hinders the reliable extraction of yield information from voluminous and often noisy datasets, especially during periods of extreme weather events. Here, in this study, we propose an Attention and Graph Isomorphism Network-enhanced Bi-directional Long Short-Term Memory network (AGB-LSTM) for estimating county-level soybean yield in the United States. This model integrates a diverse set of remote sensing data, including Near-Infrared Reflectance of Vegetation (NIRv), Sun-Induced chlorophyll Fluorescence (SIF), and Gross Primary Productivity (GPP), along with environmental covariates. The AGB-LSTM effectively leverages information related to crop yield from high-temporal-resolution time series data (5-days), achieving an accuracy of R²= 0.67 and rRMSE = 14.46%. This approach significantly outperforms traditional machine learning methods such as Random Forest (RF) (R²= 0.52, rRMSE = 17.36%) and Bi-LSTM (R²= 0.58, rRMSE = 16.17%). Sensitivity experiments with different time steps and ranges demonstrated that our model could accurately and stably predict yields 1 to 2 months before harvest. Moreover, data with a finer temporal resolution consistently improved prediction performance, resulting in an approximately 20% increase in and an approximately 20% decrease in rRMSE compared to using monthly composites. We also evaluated the robustness of the model under extreme climate events and observed strong performance (R²= 0.50, rRMSE = 21.32%). Finally, yield mapping for major soybean-producing regions in North America in 2023 revealed spatial patterns that closely matched USDA yield reports. Our findings suggest that the AGB-LSTM model is a promising and effective method for estimating yield and has notable potential for global crop yield forecasting.

Deep learning↗
DOE OSTI2025

Complexity Reduction Methods for Large-Scale Spatially Explicit Biofuels Network Design

The size and complexity of energy system optimization models have increased significantly in recent years, driven by the availability of high-resolution spatial data. We present complexity reduction and solution methods that enable us to efficiently represent high-resolution spatial data in the network design of large-scale energy systems. We aim to reduce the size and enhance the computational efficiency of network design models without sacrificing solution accuracy. Specifically, we first present how to aggregate highly granular data into larger resolutions without averaging out their specific properties through a composite-curve-based approach and then develop a method to linearly represent these curves. Second, we utilize a general clustering method to determine groups of geographically proximate biomass fields and establish a single transportation arc for all of them, reducing the number of transportation-related variables while maintaining an accurate representation of the system. Finally, we introduce a two-step algorithm that decomposes large-scale network design problems into two smaller, more manageable subproblems. We demonstrate the application of our methods using a case study of switchgrass-to-biofuels network design in the eight states of the U.S. Midwest, using realistic and highly explicit spatial data.

09 BIOMASS FUELS↗
NASA NTRS2003

An Overview of the Earth Observing System Moderate Resolution Imaging Spectroradiometer (MODIS) Data Products and Availability for Environmental Applications and Global Change Studies

The Moderate Resolution Imaging Spectroradiometer (MODIS) on the Earth Observing System (EOS) Terra Mission began to produce data in February 2000. The Terra MODIS is in a sun-synchronous orbit going north to south in the daylight portion of the orbit crossing the equator at about 1030 hours local time. The spacecraft, instrument, and data systems are performing well and are producing a wide variety of data products useful for scientific and applications studies in relatively consistent fashion extending from November 2000 to the present. Within the approximately 40 MODIS data products, several are new and represent powerful and exciting capabilities such the ability to provide observations over the globe of fire occurrences, microphysical properties of clouds and sun-stimulated fluorescence from phytoplankton in the surface waters of the ocean. The remainder of the MODIS products exceeds or, at a minimum, matches the capabilities of products from heritage sensors such as, for example, the Advanced Very High Resolution Radiometer (AVHRR). Efforts are underway to provide data sets for the greater Earth science community and to improve access to these products at the various Distributed Active Archive Centers (DAACs) or through Direct Broadcast (DB) stations. The EOS Aqua mission was launched successfully May 4,2002 with another MODIS on it. The Aqua spacecraft operates in a sun-synchronous orbit going south to north in the daylight portion of the orbit crossing the equator at approximately 1330 hours local time. Subsequently the Aqua MODIS observations will substantially add to the capabilities of the Terra MODIS for environmental applications and global change studies.

Salomonson, V. V.↗
NASA NTRS2002

An Overview of the Earth Observing System Moderate Resolution Imaging Spectroradiometer (MODIS) Data Products Status and Availability for Environmental Applications and Global Change Studies

The Moderate Resolution Imaging Spectroradiometer (MODIS) on the Earth Observing System (EOS) Terra Mission began to produce data in February 2000. The Terra MODIS is in a sun-synchronous orbit going north to south in the daylight portion of the orbit crossing the equator at about 1030 hours local time. The spacecraft, instrument, and data systems are performing well and are producing a wide variety of data products useful for scientific and applications studies in relatively consistent fashion extending from November 2000 to the present. Within the approximately 40 MODIS data products, several are new and represent powerful and exciting capabilities such the ability to provide observations over the globe of fire occurrences, microphysical properties of clouds and sun-stimulated fluorescence from phytoplankton in the surface waters of the ocean. The remainder of the MODIS products exceed or, at a minimum, match the capabilities of products from heritage sensors such as, for example, the Advanced Very High Resolution Radiometer (AVHRR). Efforts are underway to provide data sets for the greater Earth science community and to improve access to these products at the various Distributed Active Archive Centers (DAAC's) or through Direct Broadcast (DB) stations. The MODIS instrument on the EOS Aqua mission should also be expected to be in orbit and functioning in the Spring of 2002. The Aqua spacecraft will operate in a sun-synchronous orbit going south to north in the daylight portion of the orbit crossing the equator at approximately 1330 hours local time. Subsequently the Aqua MODIS observations will substantially add to the capabilities of the Terra MODIS for environmental applications and global change studies.

Salomonson, Vincent V.↗
NASA NTRS1993

High resolution depiction of atmospheric moisture, stability and surface temperature from combined MAMS and VAS radiances

In order to elucidate mesoscale variability of the earth-atmosphere system, aircraft-borne Multi-spectral Atmospheric Mapping Sensor (MAMS), 100-meter-resolution radiometric data, and geostationary-borne VISSR Atmospheric Sounder (VAS) 8-km-resolution radiometric data are used together in a physical retrieval method to produce 100-m-resolution depictions of atmospheric moisture, stability, and skin temperature. The VAS, with its IR-sounding capability, provides the vertical information to the retrieval while the MAMS, with its 100-m resolution, provides the horizontal information. The retrievals show mesoscale features, including a moist tongue intrusion and an urban heat island. Mesogamma-scale gradients are found to exceed mesobeta-scale gradients, and significant mesogamma-scale variability is not captured in current geostationary sounding data. It is suggested that improvements to the spatial resolution of operating sounding data will yield improved information on atmospheric and surface gradients, especially at the mesogamma scale.

Moeller, C. C.↗
DOE OSTI2023

Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC)

The Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC) data is a collection of 4km hourly wind, solar, temperature, humidity, and pressure fields for the contiguous United States under various climate change scenarios. Sup3rCC is downscaled Global Climate Model (GCM) data. The downscaling process was performed using a generative machine learning approach called sup3r: Super-Resolution for Renewable Energy Resource Data (linked below as "Sup3r GitHub Repo"). The data includes both historical and future weather years, although the historical years represent the historical climate, not the actual historical weather that we experienced. You cannot use Sup3rCC data to study historical weather events, although other sup3r datasets may be intended for this. The Sup3rCC data is intended to help researchers study the impact of climate change on energy systems with high levels of wind and solar capacity. Please note that all climate change data is only a representation of the possible future climate and contains significant uncertainty. Analysis of multiple climate change scenarios and multiple climate models can help quantify this uncertainty.

Array↗
NASA NTRS1994

A statistical analysis of the dependency of closure assumptions in cumulus parameterization on the horizontal resolution

Simulated data from the UCLA cumulus ensemble model are used to investigate the quasi-universal validity of closure assumptions used in existing cumulus parameterizations. A closure assumption is quasi-universally valid if it is sensitive neither to convective cloud regimes nor to horizontal resolutions of large-scale/mesoscale models. The dependency of three types of closure assumptions, as classified by Arakawa and Chen, on the horizontal resolution is addressed in this study. Type I is the constraint on the coupling of the time tendencies of large-scale temperature and water vapor mixing ratio. Type II is the constraint on the coupling of cumulus heating and cumulus drying. Type III is a direct constraint on the intensity of a cumulus ensemble. The macroscopic behavior of simulated cumulus convection is first compared with the observed behavior in view of Type I and Type II closure assumptions using 'quick-look' and canonical correlation analyses. It is found that they are statistically similar to each other. The three types of closure assumptions are further examined with simulated data averaged over selected subdomain sizes ranging from 64 to 512 km. It is found that the dependency of Type I and Type II closure assumptions on the horizontal resolution is very weak and that Type III closure assumption is somewhat dependent upon the horizontal resolution. The influences of convective and mesoscale processes on the closure assumptions are also addressed by comparing the structures of canonical components with the corresponding vertical profiles in the convective and stratiform regions of cumulus ensembles analyzed directly from simulated data. The implication of these results for cumulus parameterization is discussed.

Xu, Kuan-Man↗
NASA NTRS1987

Methods for extracting aerodynamic accelerations from Orbiter High Resolution Accelerometer Package flight data

This report presents a process for extracting the aerodynamic accelerations of the Shuttle Orbiter Vehicle from the High Resolution Accelerometer Package (HiRAP) flight data during reentry. The methods for obtaining low-level aerodynamic accelerations, principally in the rarefied flow regime, are applied to 10 Orbiter flights. The extraction process is presented using data obtained from Space Transportation System Flight 32 (Mission 61-C) as a typical example. This process involves correcting the HiRAP measurements for the effects of temperature bias and instrument offset from the Orbiter center of gravity, and removing acceleration data during times they are affected by thruster firings. The corrected data are then made continuous and smooth and are further enhanced by refining the temperature bias correction and removing effects of the auxiliary power unit actuation. The resulting data are the current best estimate of the Orbiter aerodynamic accelerations during reentry and will be used for further analyses of the Orbiter aerodynamics and the upper atmosphere characteristics.

Thompson, J. M.↗
DOE OSTI2024

Experimental methods to test photoionized plasma models in emission

The expanding foil photoionized plasma platform was developed on the Z-machine at Sandia National Laboratories to produce terrestrial photoionized plasmas at some of the same conditions as those observed in astrophysical black hole accretion disks. The specific scientific goals of the platform are motivated by long-standing astrophysical puzzles related to accreting black hole systems. Ultimately, the experimental goal is to inform the supersolar Fe abundance problem by testing photoionized plasma models in emission with at-parameter laboratory data. High quality high resolution emission data with absolute intensity calibration from a laboratory photoionized plasma have never been collected prior to the experiments described here, providing broader motivation. A robust test of the model predictions requires the successful production of a laboratory photoionized plasma along with measurements of the supporting model inputs including the radiation drive spectrum used to create the photoionized plasma, the electron density, and the temperature. The measured absolute spectral radiance in emission collected from the independently diagnosed plasma can be compared against model calculations performed using the measured inputs. In this paper, we focus on the experimental platform and the model inputs, specifically detailing the methodology used to measure the plasma conditions. The moderate electron temperature of ∼41 eV ± 15 eV and electron density of ∼1e19 ± 1.6e18 e−/cm3 and the ∼3e12 W/cm2 x-ray irradiance at the sample confirm that photoionization dominates the plasma ionization and spectra.

Cho, P. B. (ORCID:0000000291632905)↗
DOE OSTI2025

Evaluating probabilistic deep learning methods for uncertainty quantification of temperature downscaling

Deep learning (DL) has emerged as a promising tool for downscaling coarse-resolution climate data to high-resolution outputs, enabling improved regional climate predictions. A critical aspect of DL-based downscaling is the incorporation of uncertainty quantification (UQ), which enhances the interpretability and reliability of predictions—key factors for climate risk assessment and decision-making. This study develops a DL model to downscale 2 m temperature across the contiguous United States using reanalysis datasets. We systematically evaluate three epistemic UQ methods—deep ensembles (DEns), Monte Carlo dropout (MCD), and Flipout—based on their probabilistic accuracy, downscaling performance, sensitivity to geographical features, and computational efficiency. Results indicate that MCD generally outperforms Flipout and DEns in terms of calibration and downscaling accuracy. However, DEns demonstrate lower calibration errors in coastal regions, indicating its higher confidence within these areas. Flipout, in contrast, is more sensitive to elevation gradients and exhibits higher calibration errors in mountainous regions. Hence, the choice of UQ method for this task depends on the specific requirements of the application. For applications that prioritize overall calibration, downscaling accuracy, and computational efficiency, MCD is a strong candidate. These findings highlight the importance of selecting UQ methods based on application-specific requirements, such as geographical context and computational constraints. By addressing the trade-offs between UQ methods, this study provides actionable insights for improving the reliability, scalability, and utility of DL-based downscaling in climate science.

Environmental sciences↗