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Results for “GEOPHYSICAL SATELLITE”

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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

An initial analysis of the data from the Polar Orbiting Geophysical (POGS) Satellite

The Polar Orbiting Geophysical Satellite (POGS) was launched in 1990 to measure the geomagnetic field. POGS data from selected magnetically quiet days was chosen, quality checked and deleted where thought to be erroneous. A time and position correction was applied. The resulting data was fit to a degree 13 spherical harmonic model. Evaluation of the quality of the data indicates that it is sufficient for definition of the low degree (approximately less than 8) portion of the geomagnetic field. Further correction of the data time and position may improve this quality.

Langel, R. A.↗
NASA NTRS1993

Improved thermal force modeling for GPS satellites

Geophysical applications of the Global Positioning System (GPS) require the capability to estimate and propagate satellite orbits with high precision. An accurate model of all the forces acting on a satellite is an essential part of achieving high orbit accuracy. Methods of analyzing the perturbation due to thermal radiation and determining its effects on the long-term orbital behavior of GPS satellites are presented. The thermal imbalance force, a nongravitational orbit perturbation previously considered negligible, is the focus of this article. The earth's shadowing of a satellite in orbit causes periodic changes in the satellite's thermal environment. Simulations show that neglecting thermal imbalance in the satellite force model gives orbit error larger than ten meters over several days for eclipsing satellites. This orbit mismodeling can limit accuracy in orbit determination and in estimation of baselines used for geophysical applications.

Vigue, Y.↗
DOE OSTI2024

Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are identifying hidden geothermal resources in the USA and designing profitable enhanced geothermal systems (EGS). Many non-obvious processes and parameters could characterize geothermal resources and could control the ultimate energy potential of geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize geothermal resources, but this data is sparse and multi-scale. This has hindered attempts to leverage the datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) give promise to overcome these issues. Modern ML methods and tools can (1) analyze large datasets, (2) assimilate model ensembles that include a multitude of inputs and outputs, (3) process sparse datasets, (4) perform transfer learning between sites with different data quality, (5) extract hidden geothermal signatures from field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. In this work, we implement ML-based geothermal exploration and an enhanced geothermal systems (EGS) design tool to achieve the above goals. Our exploration tool is GeoThermalCloud (GTC) EGS design tool is GeoDT-ML. GTC (github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. It enables the identification of critical measurements needed to identify geothermal resource signatures. GeoDT-ML (github.com/SmartTensors/GeoThermalCloud.jl/tree/master/) adds coupling to GeoDT (https://github.com/GeoDesignTool/GeoDT.git) for stochastic EGS design optimization and performance prediction. GeoDT-ML leverages recent advances in deep learning and high-performance computing. Contributors to this effort include LANL, PNNL, Google, Stanford, and Julia Computing.

15 GEOTHERMAL ENERGY↗
NASA NTRS1991

Geophysical applications of satellite altimetry

Publications related to geophysical applications of Seasat and Geosat altimetry are reviewed for the period 1987-1990. Problems discussed include geoid and gravity errors, regional geoid heights and gravity anomalies, local gravity field/flexure, plate tectonics, and gridded geoid heights/gravity anomalies.

Sandwell, David T.↗
NASA NTRS2012

Observing Climate with Satellites - Are We on Thin Ice?

The Earth s climate is determined by irradiance from the Sun and properties of the atmosphere, oceans, and land that determine the reflection, absorption, and emission of energy within our atmosphere and at the Earth s surface. Since the 1970s, Earth-viewing satellites have complimented non-satellite geophysical observations with consistent, quantitative, and spatially-continuous measurements that have led to an unprecedented understanding of the Earth s climate system. I will describe the Earth s climate system as elaborated by satellite and in situ observations, review arguments against global warming, and show the convergence of evidence for human-caused warming of our planet.

Tucker, Compton↗
NASA NTRS1979

A rocket ozonesonde for geophysical research and satellite intercomparison

The in-situ rocketsonde for ozone profile measurements developed and flown for geophysical research and satellite comparison is reviewed. The measurement principle involves the chemiluminescence caused by ambient ozone striking a detector and passive pumping as a means of sampling the atmosphere as the sonde descends through the atmosphere on a parachute. The sonde is flown on a meteorological sounding rocket, and flight data are telemetered via the standard meteorological GMD ground receiving system. The payload operation, sensor performance, and calibration procedures simulating flight conditions are described. An error analysis indicated an absolute accuracy of about 12 percent and a precision of about 8 percent. These are combined to give a measurement error of 14 percent.

Hilsenrath, E.↗