Search NASA⌕ Search

SEARCH · Search NASA

Results for “Geostationary satellites”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

Clear-sky radiative characteristics of South America derived from geostationary satellite data

Estimates of clear-sky albedo and equivalent blackbody temperatures derived from the Geostationary Operational Environment Satellite are presented. Regional clear-sky albedo over South America at noon ranged from 10 to 22 percent during November 1978 and February 1979, mean clear-sky albedo over the continent remained fairly constant at about 12.4 percent. A slight seasonal decrease in albedo found in a number of large river valleys is attributed to seasonal flooding. The average clear-sky albedo varied diurnally by factors of 2.0 and 2.3 from the value at noon over desert and vegetated areas, respectively. The mean regional clear-sky equivalent blackbody temperature ranged from 283 to 295 K.

Minnis, P.↗
NASA NTRSDate not supplied

Deriving Broadband Radiances from Geostationary Satellite Visible Radiance Channels for Use in the CERES SYN1deg Product

The NASA CERES project was designed to monitor the Earth’s radiation budget, both at the regional and global scale. The CERES SSF1deg single satellite product provides TOA flux observations from Aqua, Terra, NPP, and NOAA-20. The SYN1deg product incorporates observations from Terra, Aqua, and geostationary (GEO) satellites. The GEO observations are included in the SYN1deg product to infer the regional diurnal cycle between CERES observations. GEO satellites throughout the CERES record have different narrowband spectral response functions and do not directly observe radiances over the broadband spectra of CERES instruments. Hence the emphasis of this study is improving the process of deriving broadband radiances from the myriad GEO visible channel observations. We present a new method for converting the narrowband radiances observed from GEO operational satellites. This study relies on radiative transfer model hyper-spectral radiances for many cloud and surface conditions under all angular conditions observed by GEO sensors. First, the hyper-spectral radiances from a radiative transfer model must be convolved with the spectral response function of the visible imager for each GEO sensor in the CERES record. For each angular bin and surface type, the clear-sky and multiple optical depth and phase cloudy-sky narrowband and broadband reflectance pairs are linearly regressed. This study found that, no matter how the scene conditions are stratified, for example the all-sky, clear-sky, ice or water clouds, they all provide similar linear regressions. This includes ocean glint conditions, where the clear-sky reflectance exceeds cloudy conditions and yet the relationship was found to be linear in the given angular bin. This infers that no GEO retrieved cloud information is necessary in the narrowband to broadband conversion process, which relies only on visible channel reflectance. Over land, most of the GEO visible channels do not observe the NIR vegetation reflectance. We intend to use land adjustment factors developed using the TOA reflectance difference between the real-time MODIS .86um and the .64um channel. We validate this approach by using coincident observational data between CERES and MODIS aboard Aqua and Terra as well as GEO and CERES observations .

CERES↗
NASA NTRS2006

Air Quality Science and Regulatory Efforts Require Geostationary Satellite Measurements

Air quality scientists and regulatory agencies would benefit from the high spatial and temporal resolution trace gas and aerosol data that could be provided by instruments on a geostationary platform. More detailed time-resolved data from a geostationary platform could be used in tracking regional transport and in evaluating mesoscale air quality model performance in terms of photochemical evolution throughout the day. The diurnal cycle of photochemical pollutants is currently missing from the data provided by the current generation of atmospheric chemistry satellites which provide only one measurement per day. Often peak surface ozone mixing ratios are reached much earlier in the day during major regional pollution episodes than during local episodes due to downward mixing of ozone that had been transported above the boundary layer overnight. The regional air quality models often do not simulate this downward mixing well enough and underestimate surface ozone in regional episodes. Having high time-resolution geostationary data will make it possible to determine the magnitude of this lower-and mid-tropospheric transport that contributes to peak eight-hour average ozone and 24-hour average PM2.5 concentrations. We will show ozone and PM(sub 2.5) episodes from the CMAQ model and suggest ways in which geostationary satellite data would improve air quality forecasting. Current regulatory modeling is typically being performed at 12 km horizontal resolution. State and regional air quality regulators in regions with complex topography and/or land-sea breezes are anxious to move to 4-km or finer resolution simulations. Geostationary data at these or finer resolutions will be useful in evaluating such models.

Pickering, Kenneth E.↗
NASA NTRS1983

Diurnal and seasonal variations of clouds from geostationary satellite data

Hourly 8-km visible (0.55-0.75 micron) and infrared (10.5-12.5 microns) data from the eastern Geostationary Operational Environmental Satellite were analyzed with a hybrid bispectral threshold method to yield total, low, middle, and high level cloud fractions and cloud radiative parameters for 250 x 250 sq km regions. These data were analyzed for the area between 45 deg N and 45 deg S, and longitudes 30 deg W and 125 deg W for the autumn of 1978 and the winter of 1979. Preliminary results for winter are presented in the present study. It is shown that diurnal variations of clouds may reveal diurnal variability in regional and large-scale circulation patterns. These diurnal cloud cycles may also have substantial effects on regional and large-scale radiation budgets and, therefore, influence circulation patterns at various spatial scales.

Minnis, P.↗
NASA NTRS1994

Geostationary satellite positioning by DLR/GSOC operations and management methods

Starting with a short description of the GSOC (German Space Operations Center) and its role within the wider framework of the research institute DLR, this paper provides a review of the geostationary telecommunications satellites positioned by the GSOC. The paper then proceeds to describe the evolution of the operations and management structures and methods which have been effectively used to accomplish these missions.

Brittinger, Peter↗
NASA NTRS2019

Probability Index of Low Stratus and Fog at Dawn using Dual Geostationary Satellite Observations from COMS and FY-2D near the Korean Peninsula

We developed a new remote sensing method for detecting low stratus and fog (LSF) at dawn in terms of probability index (PI) of LSF from simultaneous stereo observations of two geostationary-orbit satellites; the Korean Communication, Ocean, and Meteorological Satellite (COMS; 128.2°E); and the Chinese FengYun satellite (FY-2D; 86.5°E). The algorithm was validated near the Korean Peninsula between the months of April and August from April 2012 to June 2015, by using surface observations at 45 meteorological stations in South Korea. The optical features of LSF were estimated by using satellite retrievals and simulated data from the radiative transfer model. The PI was calculated using the combination of three satellite-observed variables: 1) the reflectance at 0.67 μm (R(0.67)) from COMS, and 2) the FY-2D R(0.67) minus the COMS R(0.67) (△R(0.67)) and (3) the FY-2D-COMS difference in the brightness temperature difference between 3.7 and 11.0 μm (ΔBTD(3.7-11)). The three variables, adopted from the top three probability of detection (POD) scores for their fog detection thresholds: △R(0.67) (0.82) > ΔBTD(3.7-11) (0.73) > R(0.67) (0.70) > BTD(3.7-11) (0.51). The LSF PI for this algorithm was significantly better in the two case studies compared to that using COMS only (i.e., R(0.67) or BTD(3.7-11)), so that this improvement was due to △R(0.67) and ΔBTD(3.7-11). Overall, PI in the LSF spatial distribution has the merits of a high detection rate, a specific probability display, and a low rate of seasonality and variability in detection accuracy. Therefore, PI would be useful for monitoring LSF in near-real-time, and to further its forecast ability, using next-generation satellites.

fog↗
NASA NTRS1993

HISPASAT launch and early operations phases: Computation and monitoring of geostationary satellite positioning

Since 1974, CNES, the French National Space Agency, has been involved in the geostationary launch and early operations phases (LEOP) of moving satellites from a transfer orbit delivered by a launcher to a geostationary point. During the operations and their preparation, the Flight Dynamics Center (FDC), part of CNES LEOP facilities, is in charge of the space mechanics aspects. What is noteworthy about the Spanish HISPASAT satellite positioning is that all the operations were performed on the customer's premises, and consequently the FDC was duplicated in Madrid, Spain. The first part of this paper is the FDC presentation: its role, its hardware configuration, and its space dynamics ground control system called MERCATOR. The second part of this paper details the preparation used by the FDC for the HISPASAT mission: hardware and software installation in Madrid, integration with the other entities, and technical and operational qualifications. The third part gives results concerning flight dynamics aspects and operational activities.

Brousse, Pascal↗
NASA NTRS1976

Three-axis attitude control for a geostationary satellite

The Applications Technology Satellite-6 was successfully launched into a geostationary orbit in May, 1974. Since that time, its attitude control subsystem has routinely demonstrated its ability to perform precision pointing and precision closed loop maneuvers. This paper describes the ATS-6 attitude control subsystem. The requirements imposed by the spacecraft mission objectives are outlined, and functional descriptions of the principal attitude control subsystem components and control laws are presented. The paper concludes with a summary comparison of in-orbit performance relative to its specified performance requirements.

Freeman, H. R.↗
NASA NTRS2004

GeoSTAR - A Microwave Sounder for Geostationary Satellites

GeoSTAR represents a new approach to microwave atmospheric sounding that is now under development. It has capabilities similar to sensors currently operating on low earth orbiting weather satellites but is intended for deployment in geostationary orbit - where it will complement future infrared sounders and enable all-weather temperature and humidity soundings and rain mapping. The required spatial resolution of 50 km or better dictates an aperture of 4 meters or more at a sounding frequency of 50 GHz, which is difficult to achieve with a real aperture system - this is the reason why it has until now not been possible to put a microwave sounder on a geostationary platform. GeoSTAR is instead based on a synthetic aperture imaging approach. Among the advantages of such a system are that there are no moving parts, and the size of the aperture is easily expandable to meet future needs. A ground based prototype of GeoSTAR is currently under development in an effort led by the Jet Propulsion Laboratory.

aperture synthesis↗
NASA NTRS2021

Temporal Interpolation of Geostationary Satellite Imagery With Optical Flow

Applications of satellite data in areas such as weather tracking and modeling, ecosystem monitoring, wildfire detection, and land-cover change are heavily dependent on the tradeoffs to spatial, spectral, and temporal resolutions of observations. In weather tracking, high-frequency temporal observations are critical and used to improve forecasts, study severe events, and extract atmospheric motion, among others. However, while the current generation of geostationary (GEO) satellites has hemispheric coverage at 10-15-min intervals, higher temporal frequency observations are ideal for studying mesoscale severe weather events. In this work, we present a novel application of deep learning-based optical flow to temporal upsampling of GEO satellite imagery. We apply this technique to 16 bands of the GOES-R/Advanced Baseline Imager mesoscale dataset to temporally enhance full-disk hemispheric snapshots of different spatial resolutions from 10 to 1 min. Experiments show the effectiveness of task-specific optical flow and multiscale blocks for interpolating high-frequency severe weather events relative to bilinear and global optical flow baselines. Finally, we demonstrate strong performance in capturing variability during convective precipitation events.

Image processing↗
NASA NTRS1986

On orbital allotments for geostationary satellites

The following satellite synthesis problem is addressed: communication satellites are to be allotted positions on the geostationary arc so that interference does not exceed a given acceptable level by enforcing conservative pairwise satellite separation. A desired location is specified for each satellite, and the objective is to minimize the sum of the deviations between the satellites' prescribed and desired locations. Two mixed integer programming models for the satellite synthesis problem are presented. Four solution strategies, branch-and-bound, Benders' decomposition, linear programming with restricted basis entry, and a switching heuristic, are used to find solutions to example synthesis problems. Computational results indicate the switching algorithm yields solutions of good quality in reasonable execution times when compared to the other solution methods. It is demonstrated that the switching algorithm can be applied to synthesis problems with the objective of minimizing the largest deviation between a prescribed location and the corresponding desired location. Furthermore, it is shown that the switching heuristic can use no conservative, location-dependent satellite separations in order to satisfy interference criteria.

Gonsalvez, David J. A.↗
NASA NTRSDate not supplied

Development and Application of NASA SPoRT’s DustTracker-AI Model for Real-Time Identification and Tracking of Dust in Geostationary Satellite Imagery

The NASA Short-term Prediction Research and Transition (SPoRT) Center developed the DustTracker-AI model for identifying and tracking dust in NASA/NOAA Geostationary Operational Environmental Satellite (GOES) imagery in a real-time framework. A training dataset consisting of day and night dust cases was gathered over the southwestern consisting of 115 distinct images and over a million dust pixels and 256 million no dust pixels. The dataset was separated into training (60%), testing (20%), and validation (20%). A simple random forest machine learning model was developed originally to overcome the problem of night-time dust detection and has been expanded to a comprehensive day/night model for dust identification and tracking. This physically-based machine-learning approach uses NASA/NOAA GOES-16 Advanced Baseline Imager infrared imagery as inputs to the model. The model probability of dust output achieves an Area-Under-Curve (AUC) of 0.97 with a standard deviation of 0.04 for dust cases. For images with dust present, the model correctly labels 85% of dust pixels for all dust images in the validation data set. In conjunction with developing the machine-learning model, the NASA Short-term Prediction Research and Transition Center (SPoRT) partnered with NOAA National Weather Service forecast offices to evaluate the model for utility in weather forecasting operations during the 2021 and 2023 late winter-spring seasons. Preliminary evaluation has indicated the majority of forecasters described the DustTracker-AI probabilities as having added confidence to interpreting the Dust RGB and other satellite products to objectively assess the dust extent and trends and increased the amount of time the dust plume could be tracked into the night as compared to use of the Dust RGB. More recently, SPoRT tested small scale events associated with thunderstorm outflow and burn scars to determine the model’s ability to capture local events. This presentation highlights design of the model, validation/evaluation of model performance, and example cases collected during end user product assessments.

Connor H Welch↗
NASA NTRSDate not supplied

Evaluating Meteorological Dust Events and Machine-Learning Based Dust Identification in Geostationary Satellite Imagery

NASA scientists in the Short-term Prediction Research and Transition Center (SPoRT) developed a physically-based machine learning approach to identify dust in satellite imagery with a focus on night-time dust detection (Berndt et al. 201; DustTracker-AI). NASA/NOAA Geostationary Environmental Operational Satellite-16 (GOES-16) imagery was used for training and model inputs. The training, testing and validation data set consists of 28 events in the Southwest United States, capturing dust and null events in the region from 2018-2020.With 83 distinct images and millions of pixels a random forest model was trained and validated, correctly labeling 85% of dust pixels.For the first time, the model was run in near-real time production during the spring of 2022 and dust probability visualizations were made available to NOAA National Weather Service (NWS) forecasters to assess its utility for dust forecasting. Results indicated the model helped increase the confidence in the presence of dust and enabled dust tracking for a longer period of time into the night-time hours. Forecaster assessment and running the model in near real-time allowed for the team to determine the types of events missed, captured, and false alarms. To gain additional context on model performance,the SPoRT team sought to gather more detailed information on the training database(e.g., meteorological characteristics and drivers). The goal of this project was to identify the meteorological drivers for the dust events and create a database which synthesized information from observations, forecaster discussions, and analyses pertaining to the dust events to understand the types of events currently used to train the model. A more detailed meteorological synopsis was created for each dust event in the training, testing, and validation datasets. Following the completion of the database and documentation, the classification details revealed that 88% of the dust events were synoptically driven while mesoscale events were less prevalent in model datasets. Meteorological conditions found such as mixing layer depth and wind velocity had mean values of 645mb and 21kt respectively.With conditions of deep mixed layers and moderate to strong surface winds a mesoscale thunderstorm outflow event was considered and subsequently added to the model training data set to test the impact of additional mesoscale training data. The model was retrained and then qualitatively tested on a sample thunderstorm outflow case that the original model was unable to identify. Preliminary results showed potential that the addition of more mesoscale events included in the training data could help to better identify indistinct and localized dust events.

Connor Welch↗
NASA NTRS2019

Homogenized Water Vapor Absorption Band Radiances From International Geostationary Satellites

In the past 20+ years, GEO Imagers with infrared 6.5‐μm bands have been observing the Earth's atmosphere, providing useful information of upper tropospheric moisture. Due to the instrumental differences and local viewing angles in GEO satellites, these observations are not consistent for generating climate data records (CDRs). In this study, a methodology has been developed to homogenize the 6.5‐μm radiances from the international GEO satellites, to generate a consistent CDR. Validations with Infrared Atmospheric Sounding Interferometer radiances from Metops for 2015–2017for seven GEO Imager sensors show that the GEO radiances are homogenized well with small standard deviation and biases of the differences (smaller for newer sensors), temporally stable radiometric accuracy, and weak angle dependency (even weaker for sensors with two water vapor bands). The homogenized 20+ years of consistent 6.5‐μm radiance CDR can be used to evaluate reanalysis and climate models, especially the diurnal variation of the model simulation.

Water Vapor↗
NASA NTRS1982

A method for diagnosing surface parameters using geostationary satellite imagery and a boundary-layer model

A method for diagnosing surface parameters on a regional scale via geosynchronous satellite imagery is presented. Moisture availability, thermal inertia, atmospheric heat flux, and total evaporation are determined from three infrared images obtained from the Geostationary Operational Environmental Satellite (GOES). Three GOES images (early morning, midafternoon, and night) are obtained from computer tape. Two temperature-difference images are then created. The boundary-layer model is run, and its output is inverted via cubic regression equations. The satellite imagery is efficiently converted into output-variable fields. All computations are executed on a PDP 11/34 minicomputer. Output fields can be produced within one hour of the availability of aligned satellite subimages of a target area.

Polansky, A. C.↗
NASA NTRS1972

A very high frequency radio interferometer for investigating ionospheric disturbances using geostationary satellites. Determination of changes in exospheric electron content by a comparison of group delay and Faraday rotation

The theory and development of a VHF correlation radio interferometer for investigating ionospheric disturbances are discussed. The system was developed to receive signals from the geostationary Applications Technology Satellites. Amplitude and phase variations of the signal passing through the ionosphere can be detected by this instrument. The system consists of two superheterodyne receivers separated by a distance known as the baseline of the system. Since the system is a phase sensitive instrument, the local oscillators of the two receivers must be phase coherent. This is accomplished by using phase-locked loops for generating the local oscillators. The two signals from the separate receivers are cross-correlated by multiplying the two signals together and then time averaging the result. The sensitivity of the instrument is increased by off-setting one of the local oscillators by a small amount.

Terry, R.↗
NASA NTRS1972

VHF/UHF technique for the determination of the columnar electron contents of the plasmasphere and of the protonosphere using geostationary satellite transmission: Observations during magnetic storms

Measurements of the total electron content of the plasmasphere up to geostationary heights were made using the beacon transmitters aboard the satellite ATS-3. The technique employed is a combination of the phase-path length difference and the Faraday rotation angle methods. Such a combination permits very accurate determination of the integration constant necessary to convert phase-path length difference data into information about the absolute value of the columnar content.

Almeida, O. G.↗
NASA NTRSDate not supplied

Application of Artificial Intelligence for Surface PM2.5 Estimations from Geostationary Satellite and Atmospheric Numerical Model Data

PM2.5, particulate matter (PM) with a diameter less than or equal to 2.5 μm, is emitted from anthropogenic fuel combustion and forest fires. Due to their small size, PM2.5 can penetrate into respiratory systems and cause or exacerbate serious illness. The US Environmental Protection Agency (EPA) regulates the levels of surface PM2.5 but surface monitoring has spatial and temporal limitations. The Aerosol Optical Depth (AOD) retrievals from the Geostationary Operational Environmental Satellite (GOES) missions and meteorological factors can be utilized as an alternative technique to estimate surface PM2.5 levels at a higher spatial and temporal resolution compared to surface monitors. Traditional estimation approaches rely on linear regression techniques and have limitations modeling the nonlinear relationship between the meteorological factors, AOD retrievals, and surface PM2.5. We compare different machine learning techniques and identify the best-suited model that can represent the nonlinearity between the factors affecting PM2.5 levels

Manisha Khatri↗