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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 55 records · Page 3
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NASA NTRS1983

Equatorial long waves in geostationary satellite observations and in a multichannel sea surface temperature analysis

Geostationary satellite observations of a zonally oriented sea surface temperature front in the eastern equatorial Pacific were made between 1975 and 1981. Long waves appeared along the front mainly during the summer and fall, except during 1976, the year of an El Nino. The waves have averaged periods of 25 days and wavelengths of 1000 km. At the end of 1981, the long waves also were detected in a new sea surface temperature analysis based on multichannel infrared measurements from a polar-orbiting satellite. This quantitative analysis may improve the ability to resolve low-frequency equatorial wave motions from satellite observations.

Legeckis, R.↗
NASA NTRS1978

Geomagnetic pulsations observed simultaneously on three geostationary satellites

Simultaneous observations of magnetic pulsations have been made by three geostationary satellites carrying similar magnetometers and acting as an azimuthal array. Autospectral and cross-spectral analysis yields coherence and phase differences between the pulsations at the satellite positions. The majority of the data fit the Kelvin-Helmholtz generation mechanism. The azimuthal wave number changes sign near noon and corresponds to propagation away from noon. Usually, the wave number is less than 10 deg per degree of longitude. Later in the afternoon, however, strong pulsations are observed with low coherence, implying large wave numbers. This suggests an instability driven by a gradient in the intensity of energetic protons, which may be expected at this local time. The data also suggest that some of the observed phase difference between the azimuthal components is due to small separations in magnetic shell, whereas this is not the case for the radial components. This implies a localized field-line resonance.

Hughes, W. J.↗
NASA NTRS2019

First Provisional Land Surface Reflectance Product from Geostationary Satellite Himawari-8 AHI

A provisional surface reflectance (SR) product from the Advanced Himawari Imager (AHI) on-board the new generation geostationary satellite (Himawari-8) covering the period between July 2015 and December 2018 is made available to the scientific community. The Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm is used in conjunction with time series Himawari-8 AHI observations to generate 1-km gridded and tiled land SR every 10 minutes during day time. This Himawari-8 AHI SR product includes retrieved atmospheric properties (e.g., aerosol optical depth at 0.47μm and 0.51μm), spectral surface reflectance (AHI bands 1–6), parameters of the RTLS BRDF model, and quality assurance flags. Product evaluation shows that Himawari-8 AHI data on average yielded 35% more cloud-free, valid pixels in a single day when compared to available data from the low earth orbit (LEO) satellites Terra/Aqua with MODIS sensor. Comparisons of Himawari-8 AHI SR against corresponding MODIS SR products (MCD19A1) over a variety of land cover types with the similar viewing geometry show high consistency between them, with correlation coefficients (r) being 0.94 and 0.99 for red and NIR bands, respectively. The high-frequency geostationary data are expected to facilitate studies of ecosystems on daily to diurnal time scales, complementing observations from networks such as the FLUXNET.

Himawari-8 AHI↗
NASA NTRS1985

Station keeping of geostationary satellites by electric propulsion

As various types of perturbations tend to drive a geostationary satellite away from its prescribed position, occasional orbit corrections have to be carried out by means of a suitable propulsion system. In future geostationary missions, low thrust electric propulsion is likely to be applied for station keeping because of considerable mass savings. In this paper a station keeping strategy for electric propulsion systems is developed. Both the unconstrained case and the case where thrust operation constraints are present are considered and tested by computer simulation of a realistic example.

Eckstein, M. C.↗
NASA NTRS1979

An application of a geostationary satellite rain estimation technique to an extratropical area

Previous studies in which geostationary-satellite images were used for estimating precipitations dealt primarily with tropical clouds. In the present study, an attempt is made to evaluate the applicability of geostationary images and techniques in the Montreal area, characterized by a variety of weather situations and variables (different types of cloud, moisture and temperature profiles, and rain rates). The large differences between the Montreal and tropical studies are shown to be reflected by Simpson and Wiggert's (1969) cloud model.

Wylie, D. P.↗
NASA NTRS1983

Comparison of winter-nocturnal geostationary satellite infrared-surface temperature with shelter-height temperature in Florida

Geostationary satellite surface temperatures derived from a Visible and Infrared Spin Scan Radiometer (VISSR) sensor (10.5 to 12.6 microns) were compared with 1.5-m air temperatures collected by a thermocouple on a traversing vehicle along rural highway transects in Florida, and with two fixed thermographs located in rural and agricultural areas. Statistical comparisons between satellite and 1.5-m observations yielded a mean correlation coefficient of 0.87 and an average sample standard deviation from regression of 1.57 C during clear nights for four winters (1978-1981). The satellite-temperature image of Lake Okeechobee was compared with its geographic outline for areal image registration. Manual overlays of temporal images were repeatable to within one pixel. Satellite-sensed water temperature of Lake Okeechobee was used as an indicator of satellite radiometer repeatability and stability.

Chen, E.↗
NASA NTRS1993

Possible Near-IR Channels for Remote Sensing Precipitable Water Vapor from Geostationary Satellite Platforms

Remote sensing of tropospheric water vapor profiles from current geostationary weather satellites are made using a few broadband infrared (IR) channels in the 6 to 13- micrometers region. Uncertainties greater than 20 percent exist in derived water vapor values just above the surface from the IR emission measurements. In this paper, we propose three near-IR channels, one within the 0.94-micrometer water vapor band absorption region, and the other two in nearby atmospheric windows, for remote sensing of precipitable water vapor over land areas, excluding lakes and rivers, during daytime from future geostationary satellite platforms. The physical principles are as follows. The reflectance of most surface targets varies approximately linearly with wavelength near 1 micrometer. The solar radiation on the sun-surface-sensor ray path is attenuated by atmospheric water vapor...

Green, R. O.↗
NASA NTRS2019

GEONEX Data Products: Geostationary Satellite Derived Land Surface and Atmospheric Public Data Products

The latest generation of geostationary satellites carry sensors such as the Advanced Baseline Imager (GOES-16/17) and the Advanced Himawari Imager (Himawari-8/9) that closely mimic the spatial and spectral characteristics of MODIS and VIIRS, useful for monitoring land surface conditions. The NASA Earth Exchange (NEX) team at Ames Research Center has embarked on a collaborative effort among scientists from NASA and NOAA exploring the feasibility of producing operational land surface products similar to those from MODIS/VIIRS. The team built a processing pipeline called GEONEX that is capable of converting raw geostationary data into routine products of Fires, surface reflectances, vegetation indices, LAI/FPAR, ET and GPP/NPP using algorithms adapted from both NASA/EOS and NOAA/GOES-R programs. The GEONEX pipeline will begin to produce provisional data products to be consumed by external collaborators and the academic community. In order to better inform and introduce the GEONEX products to the science community, the provisional products shall be distributed from the NAS data portal, located at data.nas.nasa.gov, and simple webpage at www.nasa.gov/geonex has been deployed, which describes any algorithms used in deriving the products, user manuals and data file information. We will also update the status of the data processing, on the website and provide links to the latest datasets, and use a geonex mailing list, using lists.nasa.gov.

Wang, Weile↗
NASA NTRS1997

A geostationary satellite system for mobile multimedia applications using portable, aeronautical and mobile terminals

A geostationary satellite system for mobile multimedia services via portable, aeronautical and mobile terminals was developed within the framework of the Advanced Communications Technology Service (ACTS) programs. The architecture of the system developed under the 'satellite extremely high frequency communications for multimedia mobile services (SECOMS)/ACTS broadband aeronautical terminal experiment' (ABATE) project is presented. The system will be composed of a Ka band system component, and an extremely high frequency band component. The major characteristics of the space segment, the ground control station and the portable, aeronautical and mobile user terminals are outlined.

Losquadro, G.↗
NASA NTRSDate not supplied

GeoNEX-ML: A Machine Learning System for Geostationary Satellite Imagery

Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. Further, a large majority of archived datasets generated by scientific processing is never used. This motivates the development of an efficient machine learning system for end-to-end processing of multi-level satellite datasets, from level 1 top of atmosphere observations to user friendly environmental variables of interest. Using current generation geostationary satellites GOES-16/17 (NOAA/NASA), Himawari-8/9 (JAXA), and GK-2A (Korea), we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow in a high performance computing environment. We use these tools to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Geostationary satellites↗
NASA NTRS1981

Methodologies for initialization experiments with NWP mesoscale model and geostationary satellite data

An algorithm is developed for the initialization of a mesoscale NWP model with geostationary satellite data and conventional data. The algorithm is a blend of the four-dimensional assimilation technique (Charney, Jastrow and Halem, 1969) for large-scale NWP models and the variational techniques used in cloud models (Gal-Chen, 1978) and large-scale models (Sasaki, 1969). The model's generated winds are combined with rawinsonde measurements.

Gal-Chen, T.↗
NASA NTRS1993

ESA personal communications and digital audio broadcasting systems based on non-geostationary satellites

Personal Communications and Digital Audio Broadcasting are two new services that the European Space Agency (ESA) is investigating for future European and Global Mobile Satellite systems. ESA is active in promoting these services in their various mission options including non-geostationary and geostationary satellite systems. A Medium Altitude Global Satellite System (MAGSS) for global personal communications at L and S-band, and a Multiregional Highly inclined Elliptical Orbit (M-HEO) system for multiregional digital audio broadcasting at L-band are described. Both systems are being investigated by ESA in the context of future programs, such as Archimedes, which are intended to demonstrate the new services and to develop the technology for future non-geostationary mobile communication and broadcasting satellites.

Logalbo, P.↗
NASA NTRS1993

Normalization and calibration of geostationary satellite radiances for the International Satellite Cloud Climatology Project

Procedures are described for normalizing the radiometric calibration of image radiances obtained from geostationary weather satellites that contributed data to the International Satellite Cloud Climatology Project. The key step is comparison of coincident and collocated measurements made by each satellite and the concurrent AVHRR on the 'afternoon' NOAA polar-orbiting weather satellite at the same viewing geometry. The results of this comparison allow transfer of the AVHRR absolute calibration, which has been established over the whole series, to the radiometers on the geostationary satellites. Results are given for Meteosat-2, 3, and 4, for GOES-5, 6, and 7, for GMS-2, 3, and 4 and for Insat-1B. The relative stability of the calibrations of these radiance data is estimated to be within +/- 3 percent; the uncertainty of the absolute calibrations is estimated to be less than 10 percent. The remaining uncertainties are at least two times smaller than for the original radiance data.

Desormeaux, Yves↗
NASA NTRSDate not supplied

A Novel Machine Learning Method for Surface PM2.5 Estimations from Geostationary Satellites

Particulate matter (PM) with a diameter of less or equal to 2.5 μm, known as PM , affects human health as it penetrates the respiratory system. The Environmental Protection Agency (EPA) measures the atmospheric concentration of PM using air quality monitors stationed throughout the Continental United States (CONUS). Such measurements are points on a spatial domain and therefore, might not be representative of the air quality at nearby areas considering that the composition of the atmosphere is highly variable from place to place. Satellite based AOD permits a spatially uniform means of estimating PM and new geostationary satellites provide high temporal and spatial resolution estimation of AOD. However, the concentration of PM is non-linearly dependent on other atmospheric parameters that include relative humidity, temperature, and height of the planetary boundary layer. This information may be estimated at similar spatial and temporal resolutions as AOD from numerical modeling such as from the National Oceanic and Atmospheric Administration’s (NOAA) High Resolution Rapid Refresh (HRRR) model which resolves near real-time atmospheric conditions over the CONUS. The estimation of PM concentration is a multi-parametric problem that considers the effect of temporal dependencies among the different parameters. Deep learning approaches are appropriate for such complex estimation problems as they intrinsically capture relations among multiple non-linear parameters. This study compares deep-learning methods to traditional regression analysis to demonstrate the capabilities of these methods in predicting PM2.5 concentrations. Additionally, a novel ensemble learning approach is employed to identify scientific processes that could further improve the estimation of PM concentration. Utilizing Long Short-Term Memory (LSTM) neural networks, which are suitable for multivariate time series estimation problems as they are capable of learning long-term dependencies, individual models are created for each EPA station and trained on the aforementioned dataset collocated over each station. Individual station models are merged if the model's performance is improved by reducing the root mean squared error (RMSE) metric. This ensemble training method ultimately reduces the RMSE value. Evaluation of these results provide insights into physical processes and related observable parameters that may contribute to PM concentrations. Identified parameters evaluated to be statistically different between the merged and unmerged models are expected to improve overall performance. These new parameters are then utilized for reevaluation of the deep learning methods with an extreme gradient boosting model with an RMSE of 5.5 providing the best results.

George Priftis↗