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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Spatial and temporal filtering technique for processing lidar photocount data

Shot noise places a practical limit on the spatial and temporal resolution of lidar photocount data. A 2-D signal-processing technique that utilizes spatial and temporal filtering to reduce shot noise and increase resolution is described. The technique is applied to sodium lidar data collected during the fall of 1979 over Urbana, Illinois. Temporal filtering is shown to enhance the spatial resolution of the sodium profiles significantly by reducing shot noise by more than 10 dB. The signal-processing technique is applicable to a wide variety of lidar data.

Gardner, C. S.↗
NASA NTRS2000

Saskatchewan Forest Fire Control Centre Surface Meteorological Data

The Saskatchewan Forest Fire Control Centre (SFFCC) provided surface meteorological data to BOREAS from its archive. This data set contains hourly surface meteorological data from 18 of the Meteorological stations located across Saskatchewan. Included in these data are parameters of date, time, temperature, relative humidity, wind direction, wind speed, and precipitation. Temporally, the data cover the period of May through September of 1994 and 1995. The data are provided in comma-delimited ASCII files, and are classified as AFM-Staff data. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

Hall, Forrest G.↗
NASA NTRS2013

Application of Spectral Analysis Techniques in the Intercomparison of Aerosol Data: 1. an EOF Approach to the Spatial-Temporal Variability of Aerosol Optical Depth Using Multiple Remote Sensing Data Sets

Many remote sensing techniques and passive sensors have been developed to measure global aerosol properties. While instantaneous comparisons between pixel-level data often reveal quantitative differences, here we use Empirical Orthogonal Function (EOF) analysis, also known as Principal Component Analysis, to demonstrate that satellite-derived aerosol optical depth (AOD) data sets exhibit essentially the same spatial and temporal variability and are thus suitable for large-scale studies. Analysis results show that the first four EOF modes of AOD account for the bulk of the variance and agree well across the four data sets used in this study (i.e., Aqua MODIS, Terra MODIS, MISR, and SeaWiFS). Only SeaWiFS data over land have slightly different EOF patterns. Globally, the first two EOF modes show annual cycles and are mainly related to Sahara dust in the northern hemisphere and biomass burning in the southern hemisphere, respectively. After removing the mean seasonal cycle from the data, major aerosol sources, including biomass burning in South America and dust in West Africa, are revealed in the dominant modes due to the different interannual variability of aerosol emissions. The enhancement of biomass burning associated with El Niño over Indonesia and central South America is also captured with the EOF technique.

variability↗
NASA NTRS2015

Structural Dynamics of Tropical Moist Forest Gaps

Gap phase dynamics are the dominant mode of forest turnover in tropical forests. However, gap processes are infrequently studied at the landscape scale. Airborne lidar data offer detailed information on three-dimensional forest structure, providing a means to characterize fine-scale (1 m) processes in tropical forests over large areas. Lidar-based estimates of forest structure (top down) differ from traditional field measurements (bottom up), and necessitate clear-cut definitions unencumbered by the wisdom of a field observer. We offer a new definition of a forest gap that is driven by forest dynamics and consistent with precise ranging measurements from airborne lidar data and tall, multi-layered tropical forest structure. We used 1000 ha of multi-temporal lidar data (2008, 2012) at two sites, the Tapajos National Forest and Ducke Reserve, to study gap dynamics in the Brazilian Amazon. Here, we identified dynamic gaps as contiguous areas of significant growth, that correspond to areas greater than 10 sq m, with height less than 10 m. Applying the dynamic definition at both sites, we found over twice as much area in gap at Tapajos National Forest (4.8%) as compared to Ducke Reserve (2.0%). On average, gaps were smaller at Ducke Reserve and closed slightly more rapidly, with estimated height gains of 1.2 m y-1 versus 1.1 m y-1 at Tapajos. At the Tapajos site, height growth in gap centers was greater than the average height gain in gaps (1.3 m y-1 versus 1.1 m y-1). Rates of height growth between lidar acquisitions reflect the interplay between gap edge mortality, horizontal ingrowth and gap size at the two sites. We estimated that approximately 10% of gap area closed via horizontal ingrowth at Ducke Reserve as opposed to 6% at Tapajos National Forest. Height loss (interpreted as repeat damage and/or mortality) and horizontal ingrowth accounted for similar proportions of gap area at Ducke Reserve (13% and 10%, respectively). At Tapajos, height loss had a much stronger signal (23% versus 6%) within gaps. Both sites demonstrate limited gap contagiousness defined by an increase in the likelihood of mortality in the immediate vicinity ((is) approximately 6 m) of existing gaps.

Structural Dynamics of Tropical Moist Forest Gaps↗
NASA NTRS2000

BOREAS TE-6 NPP For The Tower Flux, Carbon Evaluation, and Auxiliary Sites

The BOREAS TE-6 team collected several data sets to examine the influence of vegetation, climate, and their interactions on the major carbon fluxes for boreal forest species. This data set contains estimates of the biomass produced by the plant species at the TF, CEV, and AUX sites in the SSA and NSA for a given year. Temporally, the data cover the years of 1985 to 1995. The plant biomass production (i.e., aboveground, belowground, understory, litterfall), spatial coverage, and temporal nature of measurements varied between the TF, CEV, and AUX sites as deemed necessary by BOREAS principal investigators. The data are stored in tabular ASCII files. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distrobuted Activity Archive Center (DAAC).

Hall, Forrest G.↗
NASA NTRS1987

Measurements of surface energy budgets in the rocky mountains of Colorado

The surface energy balance of a mountain valley and a mountain peak were continuously monitored for periods of 126 and 34 days, respectively. The data yielded temporally resolved, high-quality measurements of the radiative, soil, and total atmospheric turbulent heat fluxes. Comparative data for the two sites reveal the effects of terrain setting and surface cover in modulating the heat balance. A procedure is suggested for accommodating local advective affects due to variable terrain and surface cover to allow partitioning of the total turbulent flux into values for sensible and latent heat. This procedure relies on a stability-dependent coefficient of turbulent transfer which is locally calibrated to systematically minimize advection-related errors. Scaling functions for the transfer coeffient are based on the effective blackbody temperature of the surface. Consequently, the procedure offers an opportunity for developing reliable bulk representations of heat transfer at the surface for use with remote sensing data and with models of the soil and surface cover in complex terrain.

Sheaffer, John D.↗
NASA NTRS2017

Challenges in Obtaining and Visualizing Satellite Level 2 Data in GIS

Satellite data products are important for a wide variety of applications that can bring far-reaching benefits to the science community and the broader society. These benefits can best be achieved if the satellite data are well utilized and interpreted. Unfortunately, this is not always the case, despite the abundance and relative maturity of numerous satellite data products provided by NASA and other organizations. One way to help users better understand the satellite data is to provide data along with Images, including accurate pixel coverage area delineation, and science team recommended quality screening for individual geophysical parameters. However, there are challenges of visualizing remote sensed non-gridded products: (1) different geodetics of space-borne instruments (2) data often arranged in a long-track and a cross-track axes (3) spatially and temporally continuous data chunked into granule files: data for a portion (or all) of a satellite orbit (4) no general rule of resampling or interpolations to a grid (5) geophysical retrieval only based on pixel center location without shape information. In this presentation, we will unravel a new Goddard Earth Sciences Data and Information Services Center (GES DISC) Level 2 (L2) visualization on-demand service. The service's front end provides various visualization and data accessing capabilities, such as overlay and swipe of multiply variables and subset and download of data in different formats. The backend of the service consists of Open Geospatial Consortium (OGC) standard-compliant Web Mapping Service (WMS) and Web Coverage Service. The infrastructure allows inclusion of outside data sources served in OGC compliant protocols and allows other interoperable clients, such as ArcGIS clients, to connect to our L2 WCS/WMS.

GI↗
NASA NTRSDate not supplied

MISR Browse data (MISBR_V5)

A MISR Browse Tool is available to allow easy access to ellipsoid-derived, true-color images for each camera reduced to 2.2 km resolution. The MISR red, green and blue bands are used to create the true-color image in JPEG format. The image is intentionally clipped and gamma-stretched to make cloud, ocean and land features visible. The user may find the Browse Tool helpful before ordering data. The tool's purpose is to search and view images in the on-line user interface. However, the browse product can be ordered separately. The Browse Tool page has Orbit-to-Date and Lat/Lon-to-Path/Block conversion tools to help identify particular MISR geographic locations and data files. [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=2.2 km; Longitude_Resolution=2.2 km; Horizontal_Resolution_Range=1 km - < 10 km or approximately .01 degree - < .09 degree; Temporal_Resolution=about 15 orbits/day; Temporal_Resolution_Range=Daily - < Weekly].

EOSDIS↗
NASA NTRS2021

Augmenting Landsat time series with Harmonized Landsat Sentinel-2 data products: Assessment of spectral correspondence

An increase in the temporal revisit of satellite data is often sought to increase the likelihood of obtaining cloud- and shadow-free observations as well as to improve mapping of rapidly- or seasonally-changing features. Currently, as a tandem, Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and −8 Operational Land Imager (OLI) provide an acquisition opportunity on an 8-day revisit interval. Sentinel-2A and -2B MultiSpectral Instrument (MSI), with a wider swath, have a 5-day revisit interval at the equator. Due to robust pre- and post-launch cross-calibration, it has been possible for NASA to produce the Harmonized Landsat Sentinel-2 (HLS) data product from Landsat-8 OLI and Sentinel-2 MSI: L30 and S30, respectively. Knowledge of the agreement of HLS outputs (especially S30) with historic Landsat surface reflectance products will inform the ability to integrate historic time-series information with new and more frequent measures as delivered by HLS. In this research, we control for acquisition date and data source to cross-compare the HLS data (L30, S30) with established Landsat-8 OLI surface-reflectance measures as delivered by the USGS (hereafter BAP, Best Available Pixel). S30 and L30 were found to have high agreement (R = 0.87–0.96) for spectral channels and an r = 0.99 for Normalized Burn Ratio (NBR) with low relative root-mean-square difference values (1.7%–3.3%). Agreement between L30 and BAP was lower, with R values ranging from 0.85 to 0.92 for spectral channels and R = 0.94 for NBR. S30 and BAP had the lowest agreement, with R values ranging from 0.71 to 0.85 for spectral channels and r = 0.90 for NBR. Comparisons indicated a stronger agreement at latitudes above 55° N. Some dependency between spectral agreement and land cover was found, with stronger correspondence for non-vegetated cover types. The level of agreement between S30 and BAP reported herein would enable integration of HLS outputs with historic Landsat data. The resulting increased temporal frequency of data allows for improvements to current cloud screening practices and increases data density and the likelihood of temporal proximity to target date for pixel compositing approaches. Furthermore, additional within-year observations will enable change products with a higher temporal fidelity and allow for the incorporation of phenological trends into land cover classification algorithms.

Michael A. Wulder↗
NASA NTRSDate not supplied

Data Science and the Knowledge Discovery Adventure

This talk will cover the important steps involved in the data science and knowledge discovery process: • Initial fact gathering (interview domain experts, review reports, articles, state-of-the-art) • Identify the problem (prediction, classification, statistical analysis, etc.) • Survey supporting data sources • Understand the data (numerical, categorical, text, sampling rate, data quality issues, etc.) • Selecting relevant features and sources • Acquire the data (set up agreements with the data stewards, APIs to download, etc.) • Merge data sources (temporal, spatial, common key, other ontologies...) • Feature Engineering (non linear domain knowledge or physics-based relationships) • Build data processing pipeline (may need to tap into data stream, develop parallel processing algorithm, federated learning etc.) • Build model and test (tune hyper-parameters, cross validation.) • Analyze/Validate results (do the results make sense. Does it answer the original question). • Deploy/Publish (Monitor and assess benefits)

Data science↗
NASA NTRS1986

Planning for the utilization of the PCDS in studying the interaction of clouds (ISCCP-C data) and the Earth radiation budget (ERBE data)

The Pilot Climate Data System (PCDS) affords an opportunity to analyze data from different but highly complementary data sets. Two of these highly complementary data sets supported by the PCDS are the International Satellite Cloud Climatology Project (ISCCP) and the Earth Radiation Budget Experiment (ERBE). Both data set sponsors are aware of the utility of one data set to the other, and both projects utilize gridded data on a 2.5 deg by 2.5 deg grid. The ISCCP data have been collected since July 1983, and the NOAA-9 data for ERBE have been collected for more than a year. Therefore, there is a good chance to use these temporally overlapping data sets to investigate hypothesized relationships. Changes in cloudiness affect both cloud albedo feedback (shortwave) and the greenhouse effect (longwave). The relative importance of the effects of clouds on albedo versus outgoing longwave radiation (OLR) in determining the radiation balance has long been a matter of controversy. Now, however, changes in cloud amount as observed by the ISCCPO can be correlated to corresponding changes in the albedo and changes in the OLR from ERBE. Monthly means can be utilized in all instances.

Jacobowitz, H.↗
NASA NTRS2023

First results from the Solar Orbiter Heavy Ion Sensor

Aims . Solar Orbiter launched in February 2020 with the goal of revealing the connections between the Sun’s interior, atmosphere, and the heliosphere. The Solar Orbiter Heavy Ion Sensor (HIS) is a time-of-flight ion mass spectrometer dedicated to measuring heavy ions in the solar wind. Methods . We present an overview of the first measurements of heavy ion composition from HIS, reviewing the methods used to transform the spectra obtained on board into scientific data products and examining two solar wind case studies as well as the statistical properties of the heavy ion composition observed by HIS. We also carried out a comparison with prior measurements of heavy ions at L1. Results . The HIS data set provides the first mass- and charge-resolved heavy ion measurements in the inner heliosphere. Conclusions . These high temporal resolution data have the potential to transform our understanding of the connections between the solar wind and its origin at the Sun, as well as the interaction between the solar wind and the environment around planets, comets, and in the interstellar medium.

solar wind↗
DOE OSTI2025

Multimodal super-resolution: discovering hidden physics and its application to fusion plasmas

Understanding complex physical systems often requires integrating data from multiple diagnostics, each with limited resolution or coverage. We present a machine learning framework that reconstructs synthetic high-temporal-resolution data for a target diagnostic using information from other diagnostics, without direct target measurements during the inference. This multimodal super-resolution technique improves diagnostic robustness and enables monitoring even in case of measurement failures or degradation. Applied to fusion plasmas, our method targets edge-localized modes (ELMs), which can damage plasma-facing materials. By reconstructing super-resolution Thomson Scattering data from complementary diagnostics, we uncover fine-scale plasma dynamics and validate the role of resonant magnetic perturbations (RMPs) in ELM suppression through magnetic island formation. The approach provides new observation supporting the plasma profile flattening due to these islands. Our results demonstrate the framework’s ability to generate high-fidelity synthetic diagnostics, offering a powerful tool for ELM control development in future reactors like ITER. The approach is broadly transferable to other domains facing sparse, incomplete, or degraded diagnostic data, opening new avenues for discovery.

Jalalvand, Azarakhsh [Princeton Univ., NJ (United ↗
NASA NTRS2000

BOREAS TGB-5 Biogenic Soil Emissions of NO and N2O

The BOReal Ecosystem-Atmosphere Study Trace Gas Biogeochemistry (BOREAS TGB)-5 team made several measurements of trace gas concentrations and fluxes at various NSA sites. This data set contains biogenic soil emissions of nitric oxide and nitrous oxide that were measured over a wide range of spatial and temporal site parameters. Since very little is known about biogenic soil emissions of nitric oxide and nitrous oxide from the boreal forest, the goal of the measurements was to characterize the biogenic soil fluxes of nitric oxide and nitrous oxide from black spruce and jack pine areas in the boreal forest. The diurnal variation and monthly variation of the emissions was examined as well as the impact of wetting through natural or artificial means. Temporally, the data cover mid-August 1993, June to August 1994, and mid-July 1995. The data are provided in tabular ASCII files. The data files are available on a CD-ROM (see document number 20010000884).

Levine, J. S.↗
DOE OSTI2026

Data-Driven Modeling and Correction of Vehicle Dynamics

We develop a data-driven framework for learning and correcting nonautonomous vehicle dynamics. Physics-based vehicle models are often simplified for tractability and therefore exhibit inherent model-form uncertainty, motivating the need for data-driven correction. Moreover, nonautonomous dynamics are governed by time-dependent control inputs, which pose challenges in learning predictive models directly from temporal snapshot data. To address these, we reformulate the vehicle dynamics via a local parameterization of the time-dependent inputs, yielding a modified system composed ofa sequence of local parametric dynamical systems. Here, we approximate these parametric systems using two complementary approaches. First, we employ the dimension reduction and interpolation in parameter space (DRIPS) methodology to construct efficient linear surrogate models, equipped with lifted observable spaces and manifold-based operator interpolation. This enables data-efficient learning of vehicle models whose dynamics admit accurate linear representations in the lifted spaces. Second, for more strongly nonlinear systems, we employ flow map learning (FML), a deep neural network (DNN) approach that approximates the parametric evolution map without requiring special treatment of nonlinearities. We further extend FML with a transfer-learning-based model correction procedure, enabling the correction of misspecified prior models using only a sparse set of high-fidelity or experimental measurements, without assuming a prescribed form for the correction term. Through a suite of numerical experiments on unicycle, simplified bicycle, and slip-based bicycle models, we demonstrate that DRIPS offers robust and highly data-efficient learning of nonautonomous vehicle dynamics, while FML provides expressive nonlinear modeling and effective correction of model-form errors under severe data scarcity.

data-driven modeling↗
NASA NTRS2010

Phenological Parameters Estimation Tool

The Phenological Parameters Estimation Tool (PPET) is a set of algorithms implemented in MATLAB that estimates key vegetative phenological parameters. For a given year, the PPET software package takes in temporally processed vegetation index data (3D spatio-temporal arrays) generated by the time series product tool (TSPT) and outputs spatial grids (2D arrays) of vegetation phenological parameters. As a precursor to PPET, the TSPT uses quality information for each pixel of each date to remove bad or suspect data, and then interpolates and digitally fills data voids in the time series to produce a continuous, smoothed vegetation index product. During processing, the TSPT displays NDVI (Normalized Difference Vegetation Index) time series plots and images from the temporally processed pixels. Both the TSPT and PPET currently use moderate resolution imaging spectroradiometer (MODIS) satellite multispectral data as a default, but each software package is modifiable and could be used with any high-temporal-rate remote sensing data collection system that is capable of producing vegetation indices. Raw MODIS data from the Aqua and Terra satellites is processed using the TSPT to generate a filtered time series data product. The PPET then uses the TSPT output to generate phenological parameters for desired locations. PPET output data tiles are mosaicked into a Conterminous United States (CONUS) data layer using ERDAS IMAGINE, or equivalent software package. Mosaics of the vegetation phenology data products are then reprojected to the desired map projection using ERDAS IMAGINE

McKellip, Rodney D.↗
NASA NTRS2000

BOREAS ECMWF 6-Hour Analysis and Forecast Data

In cooperation with BOREAS atmospheric research efforts, the ECMWF agreed to provide BOREAS with a customized subset of its 6-hourly forecast data. This data set contains parameters from three ECMWF data products in GRIB format: Surface and Diagnostic Fields, Supplemental Fields, and Extension Data. Sample software and information are provided to assist in reading the data files. Temporally, the atmospheric parameters are available for the four main synoptic hours of 00, 06, 12, and 18 UTC from 1994 to 1996. Spatially, the data are stored in a 0.5- by 0.5-degree latitude/longitude grid. To cover the entire BOREAS study area, the grid extends from 48 to 62 degrees latitude and -92 to -114 degrees longitude. The data are stored in binary data representation known as FM 92 GRIB. Due to the complexity of the content and format of this data set, users are advised to read Sections 6, 7, 8, and 14 before using data. Based on agreements between BOREAS and ECMWF, users may legally obtain and use these data only by having a set of the BOREAS CD-ROMs that contain the data. Possession or use of these data under any other circumstance is prohibited. See Sections 11.3 and 20.4 for details.

Viterbo, Pedro↗
NASA NTRS2016

Features of the Upgraded Imaging for Hypersonic Experimental Aeroheating Testing (IHEAT) Software

The Imaging for Hypersonic Experimental Aeroheating Testing (IHEAT) software is used at the NASA Langley Research Center to analyze global aeroheating data on wind tunnel models tested in the Langley Aerothermodynamics Laboratory. One-dimensional, semi-infinite heating data derived from IHEAT are used in the design of thermal protection systems for hypersonic vehicles that are exposed to severe aeroheating loads, such as reentry vehicles during descent and landing procedures. This software program originally was written in the PV-WAVE(Registered Trademark) programming language to analyze phosphor thermography data from the two-color, relative-intensity system developed at Langley. To increase the efficiency, functionality, and reliability of IHEAT, the program was migrated to MATLAB(Registered Trademark) syntax and compiled as a stand-alone executable file labeled version 4.0. New features of IHEAT 4.0 include the options to perform diagnostic checks of the accuracy of the acquired data during a wind tunnel test, to extract data along a specified multi-segment line following a feature such as a leading edge or a streamline, and to batch process all of the temporal frame data from a wind tunnel run. Results from IHEAT 4.0 were compared on a pixel level to the output images from the legacy software to validate the program. The absolute differences between the heat transfer data output from the two programs were on the order of 10(exp -5) to 10(exp -7). IHEAT 4.0 replaces the PV-WAVE(Registered Trademark) version as the production software for aeroheating experiments conducted in the hypersonic facilities at NASA Langley.

Mason, Michelle L.↗