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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Large Language Model for Validation, Optical Calibration, and Learning (VOCAL) Distributed Temperature Sensing Interface

Distributed temperature sensing (DTS) using fiber optic sensors (FOS) offers a promising method for temperature measurements in advanced reactors, such as sodium fast reactors and molten salt cooled reactors. To support the calibration and validation of DTS measurements, Argonne National Laboratory developed the Validation, Optical Calibration, and Learning (VOCAL) software package. This report describes the integration of a local large language model (LLM) with a retrieval-augmented generation (RAG) system into the VOCAL interface to serve as an interactive user assistant. The LLM framework enhances the VOCAL platform’s accessibility to users by explaining interface components, clarifying inputs and outputs, and answering user queries dynamically in real-time. The accuracy of the LLM assistant performance was evaluated with 20 queries regarding the interface and its parameters using experimental data from the Thermal Hydraulic Experimental Test Article (THETA) facility. Results demonstrate that the LLM achieved a 95% accuracy rate, with a BERTScore of 0.8816 and SBERT value of 0.7417. Furthermore, validation of the RAG system within the LLM framework showed optimal accuracy with k-values between 1 and 2 using the k-refinement convergence test. The prompt perturbation analysis demonstrated good initial consistency for the RAG system, exhibiting the highest accuracy under punctuation variations and the greatest sensitivity under query reordering. Notably, the model’s errors were limited to data retrieval failures rather than factual hallucinations, reinforcing its baseline reliability. The integration of LLM provides a highly accurate, userfriendly enhancement to the VOCAL platform without disrupting its core computational capabilities for FOS calibration and validation.

Hong, Evan↗
NASA NTRS2014

Field Results from Three Campaigns to Validate the Performance of the Miniaturized Laser Heterodyne Radiometer (Mini‐LHR) for Measuring Carbon Dioxide and Methane in the Atmospheric Column

In a collaboration between NASA GSFC and GWU, a low-cost, surface instrument is being developed that can continuously monitor key carbon cycle gases in the atmospheric column: carbon dioxide (CO2) and methane (CH4). The instrument is based on a miniaturized, laser heterodyne radiometer (LHR) using near infrared (NIR) telecom lasers. Despite relatively weak absorption line strengths in this spectral region, spectrallyresolved atmospheric column absorptions for these two molecules fall in the range of 60-80% and thus sensitive and precise measurements of column concentrations are possible. In the last year, the instrument was deployed for field measurements at Park Falls, Wisconsin; Castle Airport near Atwater, California; and at the NOAA Mauna Loa Observatory in Hawaii. For each subsequent campaign, improvement in the figures of merit for the instrument has been observed. In the latest work the absorbance noise is approaching 0.002 optical density (OD) noise on a 1.8 OD signal. An overview of the measurement campaigns and the data retrieval algorithm for the calculation of column concentrations will be presented. For light transmission through the atmosphere, it is necessary to account for variation of pressure, temperature, composition, and refractive index through the atmosphere that are all functions of latitude, longitude, time of day, altitude, etc. For temperature, pressure, and humidity profiles with altitude we use the Modern-Era Retrospective Analysis for Research and Applications (MERRA) data. Spectral simulation is accomplished by integrating short-path segments along the trajectory using the SpecSyn spectral simulation suite developed at GW. Column concentrations are extracted by minimizing residuals between observed and modeled spectrum using the Nelder-Mead simplex algorithm. We will also present an assessment of uncertainty in the reported concentrations from assumptions made in the meteorological data, LHR instrument and tracker noise, and radio frequency bandwidth and describe additional future goals in instrument development and deployment target

meteorological data↗
NASA NTRS2014

Mars' Water Vapor Mapping by the SPICAM IR Spectrometer: Five Martian Years of Observations

The SPICAM IR instrument on the Mars Express mission continuously observes the water vapor in the martian atmosphere starting from 2004 in the 1.38-micrometer spectral band. The water vapor column abundance is retrieved from nadir observations to characterize its spatial, seasonal and interannual variations. A reference set of SPICAM water vapor column abundances (zonally averaged) covering the time period from 2004 to 2013 (martian years 27-31) is available for a grid of 2 deg Ls 2 latitude, along with an average reference map of water vapor abundance combining all the martian years of Mars Express observations. Compared to the previous data retrieval by Fedorova et al. (Fedorova, A., Korablev, O., Bertaux, J.L., Rodin, A., Kiselev, A., Perrier, S. [2006]. J. Geophys. Res. 111, E09S08) the new processing algorithm includes many improvements concerning the calibration and assumed parameters. A major improvement is the account for aerosol scattering based on dust and water ice cloud optical depths measured by THEMIS/Mars Odyssey (Smith, M.D. [2009]. Icarus 202, 444-452). The account for multiple scattering by aerosol particles increases the retrieved water vapor amount by approximately 10% in polar areas during summer, and up to 60-70% for large solar zenith angles. The sensitivity of the results to aerosol properties, surface albedo, solar spectrum, and water vapor vertical distribution has also been studied. The retrieved water vapor reveals nominal annual cycle with maximum abundance of about 60-70 pr. lm for the Northern summer and approximately 20 pr. lm for the Southern summer. The annual average amount of water has been estimated to be of 10-20 pr. micrometers, in agreement with other measurements. From year to year the seasonal cycle of water vapor abundance is very stable. An observed decrease during the MY 28 global dust storm cannot be fully attributed to the masking effect of dust, and indicates a real decrease of water amount near or above the surface. No evidence of diurnal variation of column water vapor amount was found, even though the 1.38-micrometer measurements are sensitive to the few lowermost kilometers above the surface.

Mars water vapor↗
NASA NTRS1989

Initial operating capability for the hypercluster parallel-processing test bed

The NASA Lewis Research Center is investigating the benefits of parallel processing to applications in computational fluid and structural mechanics. To aid this investigation, NASA Lewis is developing the Hypercluster, a multi-architecture, parallel-processing test bed. The initial operating capability (IOC) being developed for the Hypercluster is described. The IOC will provide a user with a programming/operating environment that is interactive, responsive, and easy to use. The IOC effort includes the development of the Hypercluster Operating System (HYCLOPS). HYCLOPS runs in conjunction with a vendor-supplied disk operating system on a Front-End Processor (FEP) to provide interactive, run-time operations such as program loading, execution, memory editing, and data retrieval. Run-time libraries, that augment the FEP FORTRAN libraries, are being developed to support parallel and vector processing on the Hypercluster. Special utilities are being provided to enable passage of information about application programs and their mapping to the operating system. Communications between the FEP and the Hypercluster are being handled by dedicated processors, each running a Message-Passing Kernel, (MPK). A shared-memory interface allows rapid data exchange between HYCLOPS and the communications processors. Input/output handlers are built into the HYCLOPS-MPK interface, eliminating the need for the user to supply separate I/O support programs on the FEP.

Cole, Gary L.↗
NASA NTRS1996

Data Archival and Retrieval Enhancement (DARE) Metadata Modeling and Its User Interface

The Defense Nuclear Agency (DNA) has acquired terabytes of valuable data which need to be archived and effectively distributed to the entire nuclear weapons effects community and others...This paper describes the DARE (Data Archival and Retrieval Enhancement) metadata model and explains how it is used as a source for generating HyperText Markup Language (HTML)or Standard Generalized Markup Language (SGML) documents for access through web browsers such as Netscape.

The Defense Nuclear Agency DNA DARE Data Archival ↗
NASA NTRS1993

The UoSAT-5 solar cell experiment: First year in orbit

The results for the first year in orbit of the DRA solar cell experiment flying on the Surrey University UoSAT-5 satellite are described. Several problems were identified with the measured data, which are discussed along with the techniques used to remove or minimize the effect of the problems. After 1 year in orbit the majority of the cells flying on the experiment have undergone little or no degradation. The exception to this are all the ITO/InP cells, supplied by two different manufacturers, they are showing more degradation than the GaAs cells. This result is unexpected and currently unexplainable. It will be necessary to retrieve data from the experiment for several years to obtain the best results due to the relatively benign radiation environment.

Goodbody, C.↗
NASA NTRS1992

Database activity in the Italian Astronet: DIRA 2

The development and utilization of informational archives and databases started, in the Italian Astronet Project, in the middle of 1983. In that year, a small group of astronomers and some more technical people met together in an Astronet working group, with a common, painful experience in managing astronomical catalogues and archives with computers. Nowadays, some years later, some software packages and the contents of both, a relative general database and several local databases represent the work and the effort of the group. The systems have been conceived and developed keeping in mind the original goal of the group: to allow the single atronomer to make a free use of original data. The main package (DIRA) was rewritten, after some years of use, to fully take advantage of the several suggestions of the astronomer that used it and gathered experiences in the astronomical catalog's management. A more technical goal was to install the whole system, born and developed in the vms environment, on unix and unix-like systems. This new version, DIRA2, has a new user interface, a query language with SQL style commands supporting numerical and character functions also and a set of commands to create new catalogues from existing data. The graphics commands are also more powerful with respect to the previous version. DIRA (and DIRA2 of course) philosophy and design are very simple and proved to be very appreciated by astronomers, namely, to normalize and homogenize, at minimum, astronomical catalogues, to collect satisfactory astronomical documentation on their contents and, finally, to allow an astronomical approach to the dialogue with the database. DIRA2 is currently used in most Italian astronomical institutes to retrieve data from a still growing database of about 140 well documented and controlled astronomical catalogues, for the identification of objects and the preparation of a 'medium size' survey, in astrometry and in the creation of new catalogues.

Benacchio, L.↗
NASA NTRS2019

TPSAS-NF1676L-32493-DND

The Committee on Earth Observation Satellites (CEOS) System Engineering Office (SEO) has supported the Open Data Cube (ODC) initiative to provide a data architecture solution that has value to its global users and increases the impact of EO satellite data. ODC is an open-source platform for processing satellite data. We have developed software products and tools around the core ODC that would help users perform machine learning on EO satellite data. The recent United Nations (UN) Sustainable Development Agenda provides a shared blueprint for peace and prosperity for people and for the planet, considering our current situation and helping to create a plan. The core of this agenda is a set of seventeen Sustainable Development Goals (SDGs), which represent an urgent call for action by all countries - both developed and developing - in a global partnership. The CEOS SEO team has recently developed and released a set of innovative Jupyter notebooks addressing UN SDGs 6.6.1 (spatial extents of water-related ecosystems), 11.3.1 (ratio of land consumption rate to population growth rate), and 15.3.1 (proportion of land that is degraded over total land area). These notebooks empower users by providing features that will assist with streamlining analysis ready data retrieval, processing, and visualization. We have recently incorporated several machine learning techniques in these notebooks. In this paper, we present the lessons learned from our experience on classifying land using supervised and unsupervised machine learning techniques using ODC framework for UN SDGs. We identify the current limitations of ODC to seamlessly support machine learning techniques. We propose features that would help machine learning, specifically within the ODC framework. We propose a thematic indexing/loading of data for both unsupervised learning as well as data annotation/labeling pipeline. Currently, ODC supports machine learning by separating data-management from the analysis process. It works as a mechanism to load cubes of data. ODC does not natively support features that are vital in machine learning such as validation splits, fair/balanced sampling, establishing load size constraints, etc. We believe that our proposed features will empower users by providing features that bring machine learning techniques closed to ODC. Enhancements to ODC to better accommodate machine learning techniques can assist in fulfilling UN SDGs such as 6.3.2, 6.4.2, 6.6.1, 11.3.1, 14.1.1, 15.1.1, 15.3.1, and 15.4.2.

Syed R Rizvi↗
NASA NTRS2010

The Effect of Incident Light Polarization on Vegetation Bidirectional Reflectance Factor

The Laboratory-based Bidirectional Reflectance Factor (BRF) polarization study of vegetation is presented in this paper. The BRF was measured using a short-arc Xenon lamp/monochromator assembly producing an incoherent, tunable light source with a well-defined spectral bandpass at visible and near-infrared wavelengths of interest at 470 nm and 870 nm and coherent light source at 1.656 microns. All vegetation samples were measured using P and S linearly polarized incident light over a range of incident and scatter angles. By comparing these results, we quantitatively examine how the BRF of the samples depends on the polarization of the incident light. The differences are significant, depend strongly on the incident and scatter angles, and can be as high as 120% at 67 deg incident and 470nm. The global nature of Earth's processes requires consistent long-term calibration of all instruments involved in data retrieval. The BRF defines the reflection characteristics of Earth surface. It provides the reflectance of a target in a specific direction as a function of illumination and viewing geometry. The BRF is a function of wavelength and reflects the structural and optical properties of the surface. Various space and airborne radiometric and imaging remote sensing instruments are used in the remote sensing characterization of vegetation canopies and soils, oceans, or especially large pollution sources. The satellite data is validated through comparison with airborne, ground-based and laboratory-based data in an effort to fully understand the vegetation canopy reflectance, The Sun's light is assumed to be unpolarized at the top of the atmosphere; however it becomes polarized to some degree due to atmospheric effects by the time it reaches the vegetation canopy. Although there are numerous atmospheric correction models, laboratory data is needed for model verification and improvement.

Georgiev, Georgi T.↗
NASA NTRSDate not supplied

Data Cube Application Algorithms For The United Nations Sustainable Development Goals (UN-SDGS)

In 2015, all United Nations (UN) Member States adopted the 2030 Agenda for Sustainable Development. The Agenda provides a shared blueprint for peace and prosperity for people and for the planet, considering our current situation and helping to create a plan. The core of this agenda is a set of seventeen Sustainable Development Goals (SDGs), which represent an urgent call for action by all countries -both developed and developing - in a global partnership. The Committee on Earth Observation Satellites (CEOS) Systems Engineering Office (SEO) team has recently developed and released a set of innovative notebooks addressing UN SDGs 6.6.1 (spatial extents of water-related ecosystems), 11.3.1 (ratio of land consumption rate to population growth rate), and 15.3.1 (proportion of land that is degraded over total land area). These notebooks empower users by providing features that will assist with streamlining analysis ready data retrieval, processing, and visualization. The main contributions in this paper are: (1) briefly describing the framework of the UN SDG notebooks, (2) enumerating the notebooks’ salient features, and (3) discussing current limitations and proposing approaches to overcome these limitations.

Open Data Cube↗
NASA NTRS2025

On the Characterization and Mitigation of Noise in Space-borne Microwave Sounding Instruments

Space-borne microwave sounding instruments have become vital data sources for weather prediction and climate change studies. Among the various radiometer configurations, the total power microwave radiometer is particularly appealing for current and future operational satellites due to its superior sensitivity and simple design. However, its performance is vulnerable to degradation caused by receiver gain fluctuations, electronic 1/f noise, and other time varying receiver characteristics. For Numerical Weather Prediction (NWP) users, 1/f noise introduces inter-channel correlations, complicating the assimilation of affected observations and reducing their accuracy. Addressing this noise issue in ground data processing system is essential to enhance the utility of microwave sounding data. This paper focuses on the characterization and mitigation of noise in current and future microwave sounding instruments, with particular emphasis on the impact of 1/f noise. Various methods are applied to quantitatively characterize noise features in both frequency and time domains. Additionally, the influence of calibration parameters on 1/f noise are analyzed. Based on these findings, we propose a mitigation algorithm for reducing noise during the on-orbit calibration of microwave sounding instruments, aiming to improve the quality of retrieved data for operational use.

calibration↗
NASA NTRS2020

Evaluation and Validation of a High Spatial Resolution Satellite Soil Moisture Product over the Continental United States

The soil moisture (SM) data retrieved from the Soil Moisture Active and Passive (SMAP) satellite are available at a 9 km grid spacing since April 2015. This product can provide valuable information for research and applications in hydrology and other related fields. However, the resolution may be too coarse for applications at catchment or field scale. In this study, an established downscaling methodology, which had a major modification regarding its application on the SMAP 33 km domain, was implemented to develop a 1 km soil moisture product based on the SMAP 9 km data. The algorithm proposed here is based on the thermal inertia principle and developed by modeling the relationship between surface temperature difference and SM for different Normalized Difference Vegetation Index (NDVI) classes. The model functions were established and tuned using data from the NASA’s Land Information System (LIS) North America Land Data Assimilation System (NLDAS) and remotely sensed VISible/InfRared (VIS/IR) reflectance data from Long Term Data Record (LTDR) AVHRR (Advanced Very High Resolution Radiometer) for the growing season months of April-September 1981–2018. These were then implemented using the MODIS (Moderate Resolution Imaging Spectroradiometer) data over the Continental United States (CONUS) domain. Validation activities were carried out using in situ measurements distributed through the International Soil Moisture Network (ISMN). The validation results computed using the 1 km SM data showed that the R2, unbiased RMSE (root mean square error) and bias were improved relative to the 9 km SMAP product by 0.045, 0.018m3/m3 and 0.001m3/m3, respectively. The 1 km SM also exhibited a strong time-series autocorrelation. Further accuracy assessment analyses indicated that precipitation might contribute to the uncertainties in both the 9 km SMAP and 1 km downscaled SMAP SM products.

SMAP↗
DOE OSTI2025

NANT Site - ASSIST / Thermodynamic retrievals TROPoe / Derived Data

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous radiances observed with an Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST, Rochette et al. 2009) operated by NOAA Physical Sciences Laboratory on Nantucket Island for WFIP3. The spectral bands used in the retrieval are in the wavenumber range from 612 - 905.4 cm-1 and are specified in Turner and Löhnert (2021). Additional input data in TROPoe are cloud base height from a collocated ceilometer operated by NOAA GML and temperature, water vapor mixing ratio, and pressure from a sensor attached to the ASSIST housing. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at Upton, NY.

17 WIND ENERGY↗
NASA NTRS2013

Wind Retrieval Algorithms for the IWRAP and HIWRAP Airborne Doppler Radars with Applications to Hurricanes

Algorithms for the retrieval of atmospheric winds in precipitating systems from downward-pointing, conically-scanning airborne Doppler radars are presented. The focus in the paper is on two radars: the Imaging Wind and Rain Airborne Profiler(IWRAP) and the High-altitude IWRAP (HIWRAP). The IWRAP is a dual-frequency (Cand Ku band), multi-beam (incidence angles of 30 50) system that flies on the NOAAWP-3D aircraft at altitudes of 2-4 km. The HIWRAP is a dual-frequency (Ku and Kaband), dual-beam (incidence angles of 30 and 40) system that flies on the NASA Global Hawk aircraft at altitudes of 18-20 km. Retrievals of the three Cartesian wind components over the entire radar sampling volume are described, which can be determined using either a traditional least squares or variational solution procedure. The random errors in the retrievals are evaluated using both an error propagation analysis and a numerical simulation of a hurricane. These analyses show that the vertical and along-track wind errors have strong across-track dependence with values of 0.25 m s-1 at nadir to 2.0 m s-1 and 1.0 m s-1 at the swath edges, respectively. The across-track wind errors also have across-track structure and are on average, 3.0 3.5 m s-1 or 10 of the hurricane wind speed. For typical rotated figure four flight patterns through hurricanes, the zonal and meridional wind speed errors are 2 3 m s-1.Examples of measured data retrievals from IWRAP during an eyewall replacement cycle in Hurricane Isabel (2003) and from HIWRAP during the development of Tropical Storm Matthew (2010) are shown.

atmospheric winds↗
DOE OSTI2025

NANT Site - Microwave Radiometer Thermodynamic Retrievals TROPoe / Derived Data

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous brightness temperature measurements at 35 channels observed with a microwave radiometer MP3000A operated by NOAA Physical Sciences Laboratory on Nantucket Island for WFIP3. Additional input data in TROPoe are cloud base height from a collocated ceilometer operated by NOAA GML and temperature, water vapor mixing ratio, and pressure from a sensor attached to the MWR housing. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at Upton, NY.

17 WIND ENERGY↗
DOE OSTI2025

BLOC Site - ASSIST Thermodynamic retrievals TROPoe / Derived Data

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous radiances observed with an Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST, Rochette et al. 2009) operated by NOAA Physical Sciences Laboratory on Block Island for WFIP3. The spectral bands used in the retrieval are in the wavenumber range from 612 - 905.4 cm-1 and are specified in Turner and Löhnert (2021). Radiances are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height from a collocated ceilometer operated by NOAA GML and temperature, water vapor mixing ratio, and pressure from a sensor attached to the ASSIST housing. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at Upton, NY.

17 WIND ENERGY↗
DOE OSTI2025

GIScience in the era of Artificial Intelligence: a research agenda towards Autonomous GIS

The advent of generative AI exemplified by large language models (LLMs) opens new ways to represent and compute geographic information and transcends the process of geographic knowledge production, driving geographic information systems (GIS) towards autonomous GIS. Leveraging LLMs as the decision core, autonomous GIS can independently generate and execute geoprocessing workflows to perform spatial analysis. In this vision paper, we further elaborate on the concept of autonomous GIS and present a conceptual framework that defines its five autonomous goals, five levels of autonomy, five core functions, and three operational scales. We demonstrate how autonomous GIS could perform geospatial data retrieval, spatial analysis, and map making with four proof-of-concept GIS agents. We conclude by identifying critical challenges and future research directions, including fine-tuning and self-growing decision-cores, autonomous modelling, and examining the societal and practical implications of autonomous GIS. By establishing the groundwork for a paradigm shift in GIScience, this paper envisions a future where GIS moves beyond traditional workflows to autonomously reason, derive, innovate, and advance geospatial solutions to pressing global challenges. Meanwhile, we emphasize that as we design and deploy increasingly intelligent geospatial systems, we carry a responsibility to ensure they are developed in socially responsible ways, serve the public good, and support the continued value of human geographic insight in an AI-augmented future.

Autonomous GI↗