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NASA NTRS2006

Gamma-ray Observations with Swift and their Impact on the TeV Community

The Swift gamma-ray burst explorer was launched on Nov. 20, 2004 from Cape Canaveral, Florida. The first instrument onboard became fully operational less than a month later. Since that time the Burst Alert Telescope (BAT) on Swift has detected more than 150 gamma-ray bursts (GRBs), most of which have also been observed within two minutes by the Swift narrow-field instruments: the X-Ray Telescope (XRT) and the Ultra-Violet and Optical Telescope (UVOT). Swift trigger notices are distributed worldwide within seconds of the trigger through the Gamma-ray burst Coordinates Network (GCN) and a substantial fraction of GRBs have been followed up by ground and space-based telescopes, ranging in wavelength from radio to TeV. Results have included the first rapid localization of a short GRB and further validation of the theory that short and long bursts have different origins; detailed observations of the power-law decay of burst afterglows leading to an improved understanding of the fireball and afterglow models; and detection of the most distant GRB ever found. Swift is also a sensitive X-ray observatory with capabilities to monitor galactic and extragalactic transients on a daily basis, carry out the first all-sky hard X-ray survey since HEAO-1, and study in detail the spectra of X-ray transients. The talk will emphasize the connection between Swift/BAT GRB observations and source monitoring and TeV observations.

Krimm, Hans↗
NASA NTRS2007

Advances in Ground Transmitters for the NASA Deep Space Network

The Deep Space Network (DSN), managed by the Jet Propulsion Laboratory for NASA, is equipped with multiple microwave transmitters ranging in average radiated power from 200 W to 400 kW. The transmitters are used for routine or emergency communication with spacecraft, for navigation, and for radio science tasks. The latest advances in transmitter engineering were implemented in a new generation of 20-kW dual-band transmitters developed for the DSN 34-m beam waveguide antennas. Innovations include additional X-band communication capability for near Earth missions, new control algorithms, automated calibration, improved and expanded computerized monitoring and diagnostics, reduced cabling, and improved maintainability. The innovations were very beneficial for the DSN 'overload' during the Mars 2003/2004 missions and will benefit other missions throughout the next decade. This paper describes the current design of the new transmitters and possible future developments.

microwaves↗
NASA NTRS2014

How Well Do Satellite AOD Observations Represent the Spatial and Temporal Variability of PM2.5 Concentration for the United States?

Due to their extensive spatial coverage, satellite Aerosol Optical Depth (AOD) observations have been widely used to estimate and predict surface PM2.5 concentrations. While most previous studies have focused on establishing relationships between collocated, hourly or daily AOD and PM2.5 measurements, in this study, we instead focus on the comparison of the large-scale spatial and temporal variability between satellite AOD and PM2.5 using monthly mean measurements. A newly developed spectral analysis technique e Combined Maximum Covariance Analysis (CMCA) is applied to Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging Spectroradiometer (MISR), Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Ozone Monitoring Instrument (OMI) AOD datasets and Environmental Protection Agency (EPA) PM2.5 data, in order to extract and compare the dominant modes of variability. Results indicate that AOD and PM2.5 agree well in terms of interannual variability. An overall decrease is found in both AOD and PM2.5 across the United States, with the strongest signal over the eastern US. With respect to seasonality, good agreement is found only for Eastern US, while for Central and Western US, AOD and PM2.5 seasonal cycles are largely different or even reversed. These results are verified using Aerosol Robotic Network (AERONET) AOD observations and differences between satellite and AERONET are also examined. MODIS and MISR appear to have the best agreement with AERONET. In order to explain the disagreement between AOD and PM2.5 seasonality, we further use Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) extinction profile data to investigate the effect of two possible contributing factors, namely aerosol vertical distribution and cloud-free sampling. We find that seasonal changes in aerosol vertical distribution, due to the seasonally varying mixing height, is the primary cause for the AOD and PM2.5 seasonal discrepancy, in particular, the low AOD but high PM2.5 observed during the winter season for Central and Western US. In addition, cloud-free sampling by passive sensors also induces some bias in AOD seasonality, especially for the Western US, where the largest seasonal change in cloud fraction is found. The seasonal agreement between low level (below 500 m AGL), all sky CALIOP AOD and PM2.5 is significantly better than column AOD from MODIS, MISR, SeaWiFS and OMI. In particular, the correlation between low level, all sky AOD and PM2.5 seasonal cycles increases to above 0.7 for Central and Western US, as opposed to near zero or negative correlation for column, clear sky AOD. This result highlights the importance of accounting for the seasonally varying aerosol profiles and cloud-free sampling bias when using column AOD measurements to infer surface PM2.5 concentrations.

MODIS (radiometry)↗
NASA NTRS2019

Deep Learning and Uncertainty Quantification for Climate Resilience

Modeling and monitoring of earth’s processes through physical models and satellite observations at high resolutions is crucial for ensuring society’s ability to adapt to climate change. Deep learning (DL) has been shown to be a valuable tool for generating high resolution data, emulating physical models, and detecting weather patterns which can then be used to inform stakeholders and decision makers. However, both the data and model parameters contain substantial uncertainties that may alter users’ decisions. In this work we present two DL applications on high-resolution climate and satellite datasets using Bayesian neural networks to generate well calibrated uncertainty estimates.

Vandal, Thomas↗
NASA NTRSDate not supplied

In-Time Safety Assessment & Risk Prediction for Unmanned Aerial Systems

One of the critical challenges in emerging autonomous systems is timely mitigation of hazards encountered during operation which may not be known or accounted for at the time of design. Efficient execution of unmanned systems therefore demands a paradigm shift from scheduled periodic maintenance to predictive risk analysis that includes condition-based-monitoring, real-time reliability assessment and hazard mitigation. Particularly, the state-of-health parameters needs to be computed at the component level, unit level as well as the integrated system level. While in the former two levels, the physics of health propagation may be based on underlying electro-mechanical properties, system level prognostics often relies on data-driven models. Further, uncertainty from model, measurements and input sources should be accurately quantified to generate meaningful prediction results that can be fed into reliable decision making processes. Finally, the expected risk and time to failure has to be computed based on the current state-of-health of the overall system. This talk presents a conceptual design of such an in-time safety assurance approach for unmanned aerial vehicles (UAV) operating at low altitudes near and over populated areas. Typical in-flight hazard incidents include unplanned detour, proximity to obstacles, mid-flight component faults, limited battery life and poor quality of GPS measurements. Safety assessment therefore comprises trajectory generation and re-plan, battery RUL computation, distributed fault diagnostics and uncertainty management of predicted trajectory based on GPS measurement noise. The entire monitoring framework will be demonstrated on simulated as well as real UAV flight experiments conducted at the NASA Langley Research Center. This tutorial will therefore guide the audience through a step-by-step tracking of an autonomous system with focus on in-time risk prediction in the presence of unforeseen hazards and uncertain environment.

diagnostics↗
NASA NTRS1994

Ground-based intercomparisons of SBUV/2 flight instruments the world standard Dobson spectrophotometer 83 and overpass observations from Nimbus-7 TOMS and NOAA-11 SBUV/2

Total ozone data obtained during summers at Mauna Loa Observatory, Hawaii, with Dobson Spectrophotometer 83 are routinely compared with overpass total ozone data from the Total Ozone Mapping Spectrometer (TOMS) and the Solar Backscatter Ultraviolet (SBUV) spectrometer launched aboard the Nimbus 7 satellite in 1978. Results from the TOMS/Dobson instrument comparisons through 1990 have been presented by McPeters and Komhyr (1991). Dobson spectrophotometer 83 was established as the standard instrument for the U.S.A. Dobson instrument station network in 1962. In 1980, the instrument was designated by the World Meteorological Organization (WMO) as the Standard Dobson Spectrophotometer for the World. Long-term ozone measurement precision of the instrument has been maintained at plus or minus 0.5 percent (Komhyr et al., 1989). On an absolute scale, the ozone measurement accuracy of the instrument is estimated to plus or minus 3 percent. In early April, 1990, comparison of total ozone and vertical distribution (Umkehr) observations were made for the first time with Dobson spectrophotometer 8.3. The work was conducted at the NOAA Climate Monitoring and Diagnostics Laboratory (CMDL) in Boulder, Colorado, and at the research and instrument manufacturing facility of the Ball Aerospace System Division located about 2 km east of Boulder. (The SBUV-2 S/N-2 instrument, built by Ball Aerospace Systems Division, is scheduled for launch aboard the NOAA-13 satellite). We present results of the comparisons which include ozone vertical distribution data obtained with a balloon-borne electrochemical concentration cell (ECC) ozonesonde (Komhyr, 1969).

Heath, D. F.↗
NASA NTRS1995

Coronal structures deduced from photospheric magnetic field and He I lambda 10830 observations

The National Solar Observatory synoptic program provides an extensive and unique data base of high-resolution full-disk observations of the line-of-sight photospheric magnetic fields and of the He I lambda 10830 equivalent width. These data have been taken nearly daily for more than 21 years since 1974 and provide the opportunity to investigate the behavior of the magnetic fields in the photosphere and those inferred for the corona spanning on the time scales of a day to that of a solar cycle. The intensity of structures observed in He I lambda 10830 are strongly modulated by overlying coronal radiation; areas with low coronal emission are generally brighter in He I lambda 10830, while areas with high coronal emission are darker. For this reason, He I lambda 10830 was selected in the mid-1970's as way to identify and monitor coronal holes, magnetic fields with an open configuration, and the sources of high-speed solar wind streams. The He I lambda 10830 spectroheliograms also show a wide variety of other structures from small-scale, short-lived dark points (less than 30 arc-sec, hours) to the large-scale, long-lived two 'ribbon' flare events that follow the filament eruptions (1000 arc-sec, days). Such structures provide clues about the connections and changes in the large-scale coronal magnetic fields that are rooted in concentrations of magnetic network and active regions in the photosphere. In this paper, what observations of the photospheric magnetic field and He I lambda 10830 can tell us about the short- and long-term evolution of the coronal magnetic fields will be discussed, focussing on the quiet Sun and coronal holes. These data and what we infer from them will be compared with direct observations of the coronal structure from the Yohkoh Soft X-ray Telescope.

Harvey, Karen L.↗
NASA NTRS2006

Swift: Results from the first year of the mission

The Swift gamma-ray burst explorer was launched on Nov. 20,2004 from Cape Canaveral, Florida. The first instrument onboard became fully operational less than a month later. Since that time the Burst Alert Telescope (BAT) on Swift has detected more than one hundred gamma-ray bursts (GRBs), most of which have also been observed within two minutes by the Swift narrow-field instruments: the X-Ray Telescope (XRT) and the Ultra-Violet and Optical Telescope (UVOT). Swift trigger notices are distributed worldwide within seconds of the trigger through the Gamma-ray burst Coordinates Network (GCN) and a substantial fraction of GRBs have been followed up by ground and space-based telescopes, ranging in wavelength from radio to TeV. Results have included the first rapid localization of a short GRB and further validation of the theory that short and long bursts have different origins; detailed observations of the short-term power-law decay of burst afterglows leading to an improved understanding of the fireball model; and detection of the most distant GRB ever found. Swift is also a sensitive X-ray observatory with capabilities to monitor galactic and extragalactic transients on a daily basis, carry out the first all-sky hard X-ray survey since HEAO-1, and study in detail the spectra of X-ray transients. significant results, both in GRB science and in the search for and study of hard X-ray sources. In this talk I will provide a broad overview of the Swift mission and its most significant results, both in GRB science and in the search for and study of hard X-ray sources.

Krimm, H. A.↗
NASA NTRSDate not supplied

Super-Resolution from Space: Using MERRA-2 and MAIAC Satellite Imagery to Produce Daily Continuous 1 km PM2.5 Estimates

PM2.5 measurements from ground stations are the gold standard when available, but the expense and coverage of such stations limits widespread monitoring. Having accurate PM2.5 estimates outside the range of these stations is important for monitoring this crucial aspect of air quality. The goal of this project is to produce daily 1 km continuous PM2.5 estimates for the contiguous US relying primarily on satellite-derived data sources. This is important because models based on such data can be more easily expanded outside the study area and produce global estimates as well. The temporal availability of such data products is often weekly/daily, unlike land-use products with are often available at a yearly or worse temporal resolution. To achieve our goal, we use a couple of different deep neural network architectures to produce PM2.5 measurements at 10 km and 1 km resolution. We use two model architectures, a UNET-like model and a GAN-based model. We train both models using MERRA-2 data and MAIAC AOD data scaled to 10 km and 1 km for the two different prediction resolutions. MERRA-2 imagery is data rich with a wide range of geospatial variables at 50 km and has long historical availability (beginning in 1980). We’re also using higher spatial resolution MAIAC data at 1 km to provide finer resolution spatial context. This essentially leverages the spatial resolution of MAIAC data and the “wider” information of MERRA-2 data to predict PM2.5. For the target data we’re using a modeled 1 km PM2.5 dataset produced by Harvard to pre-train our models and then fine-tune our models using ground station measurements. Not only are our results comparable with the performance of the Harvard dataset, but can be generalized to any area or time where MERRA-2 and MAIAC data is available.

satellite imagery↗
NASA NTRSDate not supplied

Foundation AI Models for Science

Foundation Models (FM) are AI models that are designed to replace a task or an application specific model. These FM can be applied to many different downstream applications. These FM are trained using self supervised techniques and can be built on any type of sequence data. The use of self supervised learning removes the hurdle for developing a large labeled dataset for training. Most FM use transformer architecture utilizes the notion of self attention which allows the network to model the influence of distant data points to each other both in space and time. The FM models exhibit emergent properties that are induced from the data. FM can be an important tool for science. The scale of these models results in better performance for different downstream applications and these applications show better accuracy over models built from scratch. FM drastically reduces the cost of entry to build different downstream applications both in time and effort. FM for selected science datasets such as optical satellite data, can accelerate applications ranging from data quality monitoring, feature detection and prediction. FM can make it easier to infuse AI into scientific research by removing the training data bottleneck and increasing the use of science data.

Manil Maskey↗
NASA NTRSDate not supplied

Snow Depth from AMSR-2 Using Multispectral Satellite Data in an Artificial Neural Network

By using diffusion theory and Monte Carlo lidar radiative transfer simulations, Hu et al. (2022b) has derived snow depth from the first-, second- and third-order moments of the lidar backscattering pathlength distribution. Lu et al. (2022) calculated the snow depth by applying the methods to the satellite ICESat-2 lidar measurements over the Arctic sea ice, as well as land surfaces of Northern Hemisphere. In this paper, an artificial neural network (ANN) algorithm, employing several channels from Advanced Microwave Scanning Radiometer 2 (AMSR-2) and the humidity vertical profiles from Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System for Instrument Teams (GEOS-IT) product, is trained to determine snow depth identified by time and geolocation matched 2019 ICESat-2 snow-depth data during winter months over the Arctic sea ice. The trained ANN snow-depth was applied to 2018 AMSR-2 clear pixel data, although the algorithms perform reasonably well in thinner clouds. The validation data (different from the training set) of ANN snow depth from AMSR-2 showed a good agreement with time matched and co-located snow-depth values from ICESat-2. The bias was near zero, with mean absolute error (MAE) 0.05 cm and a root-mean-square-error (RMSE) 0.08 cm. Prior applying the trained ANN snow depth to AMSR-2 data, a cloud screening algorithm was developed with a similar approach. A separate ANN cloud mask was trained to determine an AMSR-2 pixel is clear or cloudy with time and geolocation matched 2015 CALIOP Vertical Feature Mask (VFM) over Arctic sea ice. The ANN cloud mask from AMSR-2 under-estimated cloud fraction by 3-6% compared to CALIOP . The additional research is needed to conclusively evaluate the ANN cloud mask accuracy. Finally, this paper will lay the foundation for a sustained long-term snowfall and snow-storm monitoring system. The future Cloud Aerosol LIdar for Global scale Observations of the ocean-Land Atmosphere system (CALIGOLA) mission will provide a means to calculate snow depth from the lidar backscattering pathlength distribution, benefiting from the UV, visible and infrared pulses. With the calculated snow depth as the truth one could develop a machine learning algorithm, as it was done in this paper, using a passive microwave instrument available at that time to generate a wide range of snow depth data, covering extensive spatial areas in the cross-orbit direction.

Neural Network↗
NASA NTRS1995

ORBCOMM: Initial operations

ORBCOMM, a subsidiary of Orbital Sciences Corporation (OSC), has designed a system to provide low-cost mobile two-way data communications for worldwide commercial markets. The primary application areas are data communications and messaging. The ORBCOMM System has been designed so a user can compose, transmit, and receive messages; control and monitor assets; and collect data on hand-held subscriber communicators (SC's). With a constellation of 36 LEO satellites and terrestrial facilities, users in the temperature climate zones around the world will have a satellite in view over 98% of the time, and will have to wait less than two minutes the rest of the time to directly access a satellite. A message transmitted from a SC and received by the satellite is relayed down to a regional gateway earth station (GES). The GES then transmits the message via fiber land-lines to the network control center (NCC). The NCC then determines the location of the recipient of the message and routes the message accordingly. The NCC can receive and transmit messages from terrestrial networks via X.400 and X.25 gateways. The NCC can translate messages from widely used e-mail systems such as Internet, cc:Mail, and Microsoft Mail into X.400 messages for transmission to and from ORBCOMM SC's.

Schoen, David↗
NASA NTRS1998

Observations of Total Lightning Associated with Severe Convection During the Wet Season in Central Florida

This paper will discuss findings of a collaborative lightning research project between National Aeronautics and Space Administration, the Massachusetts Institute of Technology and the National Weather Service office In Melbourne Florida. In August 1996, NWS/MLB received a workstation which incorporates data from the KMLB WSR-88D, Cloud to Ground (CG) stroke data from the National Lightning Detection Network (NLDN), and 3D volumetric lightning data collected from the Kennedy Space Centers' Lightning Detection And Ranging (LDAR) lightning system. The two primary objectives of this lightning workstation, called Lightning Imaging Sensor Data Applications Display (USDAD), are to: observe how total lightning relates to severe convective storm morphology over central Florida, and compare ground based total lightning data (LDAR) to a satellite based lightning detection system. This presentation will focus on objective #1. The LISDAD system continuously displays CG and total lighting activity overlaid on top of the KMLB composite reflectivity product. This allows forecasters to monitor total lightning activity associated with convective cells occurring over the central Florida peninsula and adjacent coastal waters. The LISDAD system also keeps track of the amount of total lightning data, and associated KMLB radar products with individual convective cells occurring over the region. By clicking on an individual cell, a history table displays flash rate information (CG and total lightning) in one minute increments, along with radar parameter trends (echo tops, maximum dBz and height of maximum dBz) every 5 minutes. This history table Is updated continuously, without user intervention, as long as the cell is identified. Reviewing data collected during the 1997 wet season (21 cases) revealed that storms which produced severe weather (hall greater or = 0.75 in. or wind damage) typically showed a rapid rise In total lightning prior to the onset of severe weather. On average, flash rate increases of 25 FPM per minute over a time scale of approximately 5 minutes were common. These pulse severe storms typically reached values of 150 to 200 FPM with some cells exceeding 400 FPM. One finding which could have a direct application to the warning process is that the rapid increase in lightning typically occurred in advance of the warning issuance time. Comparisons between the ending time of the rapid rate increase and the time of when the warning was issued by NWS/MLB meteorologist exhibited a lead time of 8 minutes. It is conceivable that if close monitoring of the LISDAD system by operational meteorologist is routinely performed, warnings for pulse severe storms could be issued up to 4 to 6 minutes earlier than what is issued currently.

Sharp, D.↗
NASA NTRS2005

Inductive Learning Approaches for Improving Pilot Awareness of Aircraft Faults

Neural network flight controllers are able to accommodate a variety of aircraft control surface faults without detectable degradation of aircraft handling qualities. Under some faults, however, the effective flight envelope is reduced; this can lead to unexpected behavior if a pilot performs an action that exceeds the remaining control authority of the damaged aircraft. The goal of our work is to increase the pilot s situational awareness by informing him of the type of damage and resulting reduction in flight envelope. Our methodology integrates two inductive learning systems with novel visualization techniques. One learning system, the Inductive Monitoring System (IMS), learns to detect when a simulation includes faulty controls, while two others, Inductive Classification System (INCLASS) and multiple binary decision tree system (utilizing C4.5), determine the type of fault. In off-line training using only non-failure data, IMS constructs a characterization of nominal flight control performance based on control signals issued by the neural net flight controller. This characterization can be used to determine the degree of control augmentation required in the pitch, roll, and yaw command channels to counteract control surface failures. This derived information is typically sufficient to distinguish between the various control surface failures and is used to train both INCLASS and C4.5. Using data from failed control surface flight simulations, INCLASS and C4.5 independently discover and amplify features in IMS results that can be used to differentiate each distinct control surface failure situation. In real-time flight simulations, distinguishing features learned during training are used to classify control surface failures. Knowledge about the type of failure can be used by an additional automated system to alter its approach for planning tactical and strategic maneuvers. The knowledge can also be used directly to increase the pilot s situational awareness and inform manual maneuver decisions. Our multi-modal display of this information provides speech output to issue control surface failure warnings to a lesser-used communication channel and provides graphical displays with pilot-selectable !eve!s of details to issues additional information about the failure. We also describe a potential presentation for flight envelope reduction that can be viewed separately or integrated with an existing attitude indicator instrument. Preliminary results suggest that the inductive approach is capable of detecting that a control surface has failed and determining the type of fault. Furthermore, preliminary evaluations suggest that the interface discloses a concise summary of this information to the pilot.

Spikovska, Lilly↗
NASA NTRS2004

Satellite Mapping of Rain-Induced Nitric Oxide Emissions from Soils

We use space-based observations of NO2 columns from the Global Ozone Monitoring Experiment (GOME) to map the spatial and seasonal variations of NOx emissions over Africa during 2000. The GOME observations show not only enhanced tropospheric NO2 columns from biomass burning during the dry season but also comparable enhancements from soil emissions during the rainy season over the Sahel. These soil emissions occur in strong pulses lasting 1-3 weeks following the onset of rain, and affect 3 million sq km of semiarid sub-Saharan savanna. Surface observations of NO2 from the International Global Atmospheric Chemistry (IGAC)/Deposition of Biochemically Important Trace Species (DEBITS)/Africa (IDAF) network over West Africa provide further evidence for a strong role for microbial soil sources. By combining inverse modeling of GOME NO2 columns with space-based observations of fires, we estimate that soils contribute 3.3+/-1.8 TgN/year, similar to the biomass burning source (3.8+/-2.1 TgN/year), and thus account for 40% of surface NO(x) emissions over Africa. Extrapolating to all the tropics, we estimate a 7.3 TgN/year biogenic soil source, which is a factor of 2 larger compared to model-based inventories but agrees with observation-based inventories. These large soil NO(x) emissions are likely to significantly contribute to the ozone enhancement originating from tropical Africa.

Jaegle, L.↗
NASA NTRSDate not supplied

Temporal Interpolation of Geostationary Satellite Imagery with Task Specific 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 trade-offs 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 satellites have hemispheric coverage at 10-15 minute intervals, higher temporal frequency observations are ideal for studying mesoscale severe weather events. In this work, we apply a task specific optical flow approach to temporal up-sampling using deep convolutional neural networks. We apply this technique to 16-bands of GOES-R/Advanced Baseline Imager mesoscale dataset to temporally enhance full disk hemispheric snapshots of different spatial resolutions from 15 minutes to 1 minute. Experiments show the effectiveness of task specific optical flow and multi-scale blocks for interpolating high-frequency severe weather events relative to bilinear and global optical flow baselines. Lastly, we demonstrate strong performance in capturing variability during convective precipitation events.

Optical flow, temporal interpolation, geostationar↗
NASA NTRS1992

Monitoring global geodynamical parameter variations with GPS and low earth satellites

A low earth satellite enhances global geodynamical parameters determination with GPS in two ways. First, it improves the GPS orbits, which in turn improve the estimates of other parameters. Secondly, a low earth satellite completes an orbit cycle in far shorter time (90 to 120 minutes) than do GPS satellites (12 hours); it observes more GPS satellites than a ground receiver does in shorter time and increases the correlation between GPS orbit errors. This reduces the error in the determination of nonrotational coordinate parameters, i.e., geocentric offset of ground tracking sites. Covariance results with the global geodynamical parameters modeled as constants, and as random-walk parameters to closer reflect the actual variations, are compared. The effects of using different GPS data quality and different ground tracking network are studied. The case of using two earth satellites in orthogonal orbital planes is also investigated.

Wu, S. C.↗
NASA NTRS1990

Advanced systems engineering and network planning support

The objective of this task was to take a fresh look at the NASA Space Network Control (SNC) element for the Advanced Tracking and Data Relay Satellite System (ATDRSS) such that it can be made more efficient and responsive to the user by introducing new concepts and technologies appropriate for the 1997 timeframe. In particular, it was desired to investigate the technologies and concepts employed in similar systems that may be applicable to the SNC. The recommendations resulting from this study include resource partitioning, on-line access to subsets of the SN schedule, fluid scheduling, increased use of demand access on the MA service, automating Inter-System Control functions using monitor by exception, increase automation for distributed data management and distributed work management, viewing SN operational control in terms of the OSI Management framework, and the introduction of automated interface management.

Walters, David H.↗