Results for “data availability” — Page 10

NASA NTRS1991

Pilot's Automated Weather Support System (PAWSS) concepts

The FAA and NASA are investigating concepts for providing automated support to pilots in obtaining and using weather information. The pilot's Automated Weather Support System (PAWSS) activity is using computer technology to assist the pilot in acquiring and displaying weather information in the cockpit, and to assist in assessing and applying weather data in flight management and en route support. The PAWSS concepts include data link communications with a short update cycle of new weather data, color-coded map depictions with alpha-numeric data available for backup and detailed data presentation, automated monitoring of weather trends with alerting for hazardous conditions, and expert system technology applications in weather data assessments and flight management applications.

Crabill, N. L↗

NASA NTRS1991

Observations of snow and ice features during the polar winter using moonlight as a source of illumination

Sunlight reflected off the surface of the moon provides sufficient illumination for nighttime imaging of the earth from those spacecraft on which the instruments are sensitive to low light levels. The Defense Meteorological Satellite Program (DMSP) series of satellites are at present the only systems capable of furnishing visible nocturnal images of the earth from space. Features having a high albedo in the visible wavelengths, such as snow and ice, can easily be identified on moonlit nights when the moon is in the gibbous or full phases. Little information exists about the distribution of leads, fractures, and polynya on a basin-wide scale in the winter pack ice, and knowledge of oceanic and atmospheric fluxes is hindered by the limited data available in winter. The DMSP data is a relatively untapped source for observing polar regions during the winter. Using nighttime imagery is a novel way to explore polar areas during the winter months when the sun is below the horizon for much of the time.

Foster, James L.↗

NASA NTRS1993

Local topography of Mars and its relationship to surface weathering processes

There is a growing body of evidence in favor of the importance of aqueous sedimentary processes on Mars. It is important to understand the role that surface weathering processes have played in the development of the present morphology of the Martian surface. Such an understanding is important not only for its relevance to the study of volatile sources and sinks on Mars through time, but also for its relevance to Martian geologic and tectonic history. Starting in the fall of this year, the Mars Observer Laser Altimeter will begin sending back to Earth data on the topography of Mars that is of a higher quality than most of the topography data available for the Earth. This data will be invaluable, not only for understanding global and large-scale regional processes and landforms on Mars, but also for the study of local and smaller-scale regional processes and landforms. Digital topography is an important part of geologic and geomorphic studies, useful in distinguishing between different lithologies and between different types of weathering. Digital topography data may be used to study a wide variety of local and regional-scale landforms, including valleys, sand dunes, lava flows, landslides, and slopes. Topography data are also essential to the analysis of spectral response patterns, especially in areas of high topographic relief. Geomorphic classification can be significantly improved by the addition of topographic information.

Schaefer, M. W.↗

NASA NTRS1994

A programmable heater control circuit for spacecraft

Spacecraft thermal control is accomplished for many components through use of multilayer insulation systems, electrical heaters, and radiator systems. The heaters are commanded to maintain component temperatures within design specifications. The programmable heater control circuit (PHCC) was designed to obtain an effective and efficient means of spacecraft thermal control. The hybrid circuit provides use of control instrumentation as temperature data, available to the spacecraft central data system, reprogramming capability of the local microprocessor during the spacecraft's mission, and the elimination of significant spacecraft wiring. The hybrid integrated circuit has a temperature sensing and conditioning circuit, a microprocessor, and a heater power and control circuit. The device is miniature and housed in a volume which allows physical integration with the component to be controlled. Applications might include alternate battery-powered logic-circuit configurations. A prototype unit with appropriate physical and functional interfaces was procured for testing. The physical functionality and the feasibility of fabrication of the hybrid integrated circuit were successfully verified. The remaining work to develop a flight-qualified device includes fabrication and testing of a Mil-certified part. An option for completing the PHCC flight qualification testing is to enter into a joint venture with industry.

Nguyen, D. D.↗

NASA NTRS1960

Noise Considerations for Manned Reentry Vehicles

Noise measurements pertaining mainly to the static firing, launch, 0 and exit flight phases are presented for three rocket-powered vehicles 4 in the Project Mercury test program. Both internal and external data 4 from onboard recordings are presented for a range of Mach numbers and dynamic pressures and for different external vehicle shapes. The main sources of noise are noted to be the rocket engines during static firing and launch and the aerodynamic boundary layer during the high-dynamic-pressure portions of the flight. Rocket-engine noise measurements along the surface of the Mercury Big Joe vehicle were noted to correlate well with data from small models and available data for other large rockets. Measurements have indicated that the aerodynamic noise pressures increase approximately as the dynamic pressure increases and may vary according to the external shape of the vehicle, the highest noise levels being associated with conditions of flow separation. There is also a trend for the aerodynamic noise spectra to peak at higher frequencies as the flight Mach number increases.

Hilton, David A.↗

NASA NTRS2005

Dynamic Domains in Data Production Planning

This paper discusses a planner-based approach to automating data production tasks, such as producing fire forecasts from satellite imagery and weather station data. Since the set of available data products is large, dynamic and mostly unknown, planning techniques developed for closed worlds are unsuitable. We discuss a number of techniques we have developed to cope with data production domains, including a novel constraint propagation algorithm based on planning graphs and a constraint-based approach to interleaved planning, sensing and execution.

Golden, Keith↗

NASA NTRS2015

Performance of a Discrete Wavelet Transform for Compressing Plasma Count Data and its Application to the Fast Plasma Investigation on NASA's Magnetospheric Multiscale Mission

Plasma measurements in space are becoming increasingly faster, higher resolution, and distributed over multiple instruments. As raw data generation rates can exceed available data transfer bandwidth, data compression is becoming a critical design component. Data compression has been a staple of imaging instruments for years, but only recently have plasma measurement designers become interested in high performance data compression. Missions will often use a simple lossless compression technique yielding compression ratios of approximately 2:1, however future missions may require compression ratios upwards of 10:1. This study aims to explore how a Discrete Wavelet Transform combined with a Bit Plane Encoder (DWT/BPE), implemented via a CCSDS standard, can be used effectively to compress count information common to plasma measurements to high compression ratios while maintaining little or no compression error. The compression ASIC used for the Fast Plasma Investigation (FPI) on board the Magnetospheric Multiscale mission (MMS) is used for this study. Plasma count data from multiple sources is examined: resampled data from previous missions, randomly generated data from distribution functions, and simulations of expected regimes. These are run through the compression routines with various parameters to yield the greatest possible compression ratio while maintaining little or no error, the latter indicates that fully lossless compression is obtained. Finally, recommendations are made for future missions as to what can be achieved when compressing plasma count data and how best to do so.

Particles↗

NASA NTRS2021

Spatial Variation of Fine Particulate Matter Levels in Nairobi Before and During the COVID-19 Curfew: Implications for Environmental Justice

The temporary decrease of fine particulate matter (PM(sub 2.5)) concentrations in many parts of the world due to the COVID-19 lockdown spurred discussions on urban air pollution and health. However there has been little focus on sub-Saharan Africa, as few African cities have air quality monitors and if they do, these data are often not publicly available. Spatial differentials of changes in PM(sub 2.5) concentrations as a result of COVID also remain largely unstudied. To address this gap, we use a serendipitous mobile air quality monitoring deployment of eight Sensirion SPS 30 sensors on motorbikes in the city of Nairobi starting on 16 March 2020, before a COVID-19 curfew was imposed on 25 March and continuing until 5 May 2020. We developed a random-forest model to estimate PM(sub 2.5) surfaces for the entire city of Nairobi before and during the COVID-19 curfew. The highest PM(sub 2.5) concentrations during both periods were observed in the poor neighborhoods of Kariobangi, Mathare, Umoja, and Dandora, located to the east of the city center. Changes in PM(sub 2.5) were heterogeneous over space. PM(sub 2.5) concentrations increased during the curfew in rapidly urbanizing, the lower-middle-class neighborhoods of Kahawa, Kasarani, and Ruaraka, likely because residents switched from LPG to biomass fuels due to loss of income. Our results indicate that COVID-19 and policies to address it may have exacerbated existing air pollution inequalities in the city of Nairobi. The quantitative results are preliminary, due to sampling limitations and measurement uncertainties, as the available data came exclusively from low-cost sensors. This research serves to highlight that spatial data that is essential for understanding structural inequalities reflected in uneven air pollution burdens and differential impacts of events like the COVID pandemic. With the help of carefully deployed low cost sensors with improved spatial sampling and at least one reference-quality monitor for calibration, we can collect data that is critical for developing targeted interventions that address environmental injustice in the African context.

Environmental justice↗

NASA NTRSDate not supplied

Supporting Global Air Quality Management Needs With A Flexible Data Fusion Tool for Estimation and Forecasting in Google Earth Engine

High spatial and temporal resolution air quality estimation and forecasting can be enhanced by combining global data sources, like chemical transport models and satellite remote sensing, with local information from regulatory and low-cost air quality monitors. Successful integration of data from these diverse sources is complicated by many factors, however, including differences in spatial and temporal resolution, data availability and latency issues, varying data quality, and large computational and data storage requirements. This presentation will provide an overview of a NASA-funded effort to develop the foundation for future operationalization of air quality forecasting for world-wide end-users and integration into their air quality management decision processes, which will be achieved in future phases of this multi-year project. We will summarize our progress in developing a data fusion system using the Google Earth Engine platform which can integrate model, satellite, and surface-level monitoring datasets to enhance estimation and forecasting of air-quality-relevant pollutants at sub-daily and sub-city scales. The tool is being developed in close cooperation with several city- and regional-level air quality managers in the USA and around the world. Our end-goal is to provide these air quality managers with the information they need to assess and anticipate the impacts of poor air quality, track changes in air quality due to ongoing mitigation efforts and land use changes, and identify ways to improve their air quality monitoring strategies. This presentation will focus on recent advances achieved through the project, including integration of multiple air quality datasets in a prototype data fusion system in Google Earth Engine, the quantification of uncertainties associated with our data fusion approach, and the development of user interfaces and visualization tools to convey air quality information in a way which best meets end-user needs.

Carl Malings↗

NASA NTRSDate not supplied

Supporting Global Air Quality Management Needs With A Flexible Data Fusion Tool for Estimation and Forecasting in Google Earth Engine

High spatial and temporal resolution air quality estimation and forecasting can be enhanced by combining global data sources, like chemical transport models and satellite remote sensing, with local information from regulatory and low-cost air quality monitors. Successful integration of data from these diverse sources is complicated by many factors, however, including differences in spatial and temporal resolution, data availability and latency issues, varying data quality, and large computational and data storage requirements. This presentation will provide an overview of a NASA-funded effort to develop the foundation for future operationalization of air quality forecasting for world-wide end-users and integration into their air quality management decision processes, which will be achieved in future phases of this multi-year project. We will summarize our progress in developing a data fusion system using the Google Earth Engine platform which can integrate model, satellite, and surface-level monitoring datasets to enhance estimation and forecasting of air-quality-relevant pollutants at sub-daily and sub-city scales. The tool is being developed in close cooperation with several city- and regional-level air quality managers in the USA and around the world. Our end-goal is to provide these air quality managers with the information they need to assess and anticipate the impacts of poor air quality, track changes in air quality due to ongoing mitigation efforts and land use changes, and identify ways to improve their air quality monitoring strategies. This presentation will focus on recent advances achieved through the project, including integration of multiple air quality datasets in a prototype data fusion system in Google Earth Engine, the quantification of uncertainties associated with our data fusion approach, and the development of user interfaces and visualization tools to convey air quality information in a way which best meets end-user needs.

Carl Malings↗

NASA NTRS1973

Wind utilization in remote regions: An economic study

A wind driven generator was considered as a supplement to a diesel group, for the purpose of economizing fuel when wind power is available. A specific location on Hudson's Bay, Povognituk, was selected. Technical and economic data available for a wind machine of 10-kilowatt nominal capacity and available wind data for that region were used for the study. After subtracting the yearly wind machine costs from savings in fuel costs, a net savings of $1400 per year is realized. These values are approximate, but are though to be highly conservative.

Vansant, J. H.↗

NASA NTRSDate not supplied

RadLab: A Comprehensive Database and Graphical and Programming Interfaces for Space Radiation Data

RadLab, a component of the NASA Open Science Data Repository (OSDR), is a database of radiation measurements from multiple instruments and spacecraft that provides visual and programmatic interfaces for interrogation and retrieval of these data. The attributes of data available through RadLab include spacecraft, types of radiation sensing instruments, locations within the spacecraft (e.g. ISS modules), associated celestial bodies, trajectories, and spacecraft coordinates; the primary type of data is the absorbed dose rate, as well as flux and dose equivalent rate where available. The application programming interface (API) implements a request syntax for retrieval of timestamped data filtered by various combinations of such attributes; the graphical user interface (GUI) extends this functionality with visualizations (time series plots, comparison plots, geospatial visualizations) which provide easy means to assess data availability, iteratively refine search parameters, interactively inspect the data, and export target data subsets. Datasets are continuously being added to the RadLab database as part of the rolling release process. Investigators from multiple countries, including the US, Canada, Germany, Bulgaria, Hungary, Italy, Japan, Russia and the Czech Republic, have committed to provide data from their instruments in and beyond low Earth orbit. The current release contains datasets provided by US and international collaborators and includes readings from multiple modules of the ISS, the BioSentinel CubeSat, Chang’e 4, the Lunar Reconnaissance Orbiter, the ExoMars Orbiter, and the Curiosity rover. Datasets are associated with respective RadLab knowledgebase articles which include instrument descriptions and provide bibliographical references. RadLab aims to provide a comprehensive, dynamic compendium of space radiation data, enabling the scientific community to perform analyses of data from multiple detectors and to determine the radiation environment of research missions and experiments. Some of its applications include inference of absorbed radiation dose for NASA GeneLab payloads, and training predictive models as part of the 2024 FDL-X challenge. The platform is actively expanding and seeking additional data, with plans to also cover past (e.g. Shuttle, Mir) and future (e.g. Artemis) missions. The RadLab Working Group has been created to aid in this process as well as to foster collaborations among data contributors and users, to develop standards for data harmonization, and to guide the development of the platform, with the goal to establish the use of RadLab in space radiation research and to advance our understanding of the radiation environment in outer space.

Kirill Grigorev↗

NASA NTRSDate not supplied

RadLab: A Comprehensive Database and Graphical and Programming Interfaces for Space Radiation Data

RadLab, a component of the NASA Open Science Data Repository (OSDR), is a database of radiation measurements from multiple instruments and spacecraft that provides visual and programmatic interfaces for interrogation and retrieval of these data. The attributes of data available through RadLab include spacecraft, types of radiation sensing instruments, locations within the spacecraft (e.g. ISS modules), associated celestial bodies, trajectories, and spacecraft coordinates; the primary type of data is the absorbed dose rate, as well as flux and dose equivalent rate where available. The application programming interface (API) implements a request syntax for retrieval of timestamped data filtered by various combinations of such attributes; the graphical user interface (GUI) extends this functionality with visualizations (time series plots, comparison plots, geospatial visualizations) which provide easy means to assess data availability, iteratively refine search parameters, interactively inspect the data, and export target data subsets. Datasets are continuously being added to the RadLab database as part of the rolling release process. Investigators from multiple countries, including the US, Canada, Germany, Bulgaria, Hungary, Italy, Japan, Russia and the Czech Republic, have committed to provide data from their instruments in and beyond low Earth orbit. The current release contains datasets provided by US and international collaborators and includes readings from multiple modules of the ISS, the BioSentinel CubeSat, Chang’e 4, the Lunar Reconnaissance Orbiter, the ExoMars Orbiter, and the Curiosity rover. Datasets are associated with respective RadLab knowledgebase articles which include instrument descriptions and provide bibliographical references. RadLab aims to provide a comprehensive, dynamic compendium of space radiation data, enabling the scientific community to perform analyses of data from multiple detectors and to determine the radiation environment of research missions and experiments. Some of its applications include inference of absorbed radiation dose for NASA GeneLab payloads, and training predictive models as part of the 2024 FDL-X challenge. The platform is actively expanding and seeking additional data, with plans to also cover past (e.g. Shuttle, Mir) and future (e.g. Artemis) missions. The RadLab Working Group has been created to aid in this process as well as to foster collaborations among data contributors and users, to develop standards for data harmonization, and to guide the development of the platform, with the goal to establish the use of RadLab in space radiation research and to advance our understanding of the radiation environment in outer space.

Kirill Grigorev↗

NASA NTRS1974

Measurement and correlation of aerodynamic heating to surface corrugation stiffened structures in thick turbulent boundary layers

The flow conditions for which heating distributions were measured on corrugated surfaces and wavy walls in turbulent boundary layers are shown, along with the ratio of the displacement thickness to the roughness height versus the local edge Mach number for an equivalent smooth surface. The present data are seen to greatly extend the range of data available on corrugated surfaces in turbulent boundary layers. These data were obtained by testing fullscale corrugation roughened panels in the wall boundary layer of a supersonic and hypersonic wind tunnel. The experimental program used to obtain the data is described. The data are analyzed and correlated in terms of the pertinent flow and geometric parameters. The developed correlations are compared with the available thin boundary layer data, as well as with previously published correlation techniques.

Brandon, H. J.↗

NASA NTRS1993

Rain-rate data base development and rain-rate climate analysis

The single-year rain-rate distribution data available within the archives of Consultative Committee for International Radio (CCIR) Study Group 5 were compiled into a data base for use in rain-rate climate modeling and for the preparation of predictions of attenuation statistics. The four year set of tip-time sequences provided by J. Goldhirsh for locations near Wallops Island were processed to compile monthly and annual distributions of rain rate and of event durations for intervals above and below preset thresholds. A four-year data set of tropical rain-rate tip-time sequences were acquired from the NASA TRMM program for 30 gauges near Darwin, Australia. They were also processed for inclusion in the CCIR data base and the expanded data base for monthly observations at the University of Oklahoma. The empirical rain-rate distributions (edfs) accepted for inclusion in the CCIR data base were used to estimate parameters for several rain-rate distribution models: the lognormal model, the Crane two-component model, and the three parameter model proposed by Moupfuma. The intent of this segment of the study is to obtain a limited set of parameters that can be mapped globally for use in rain attenuation predictions. If the form of the distribution can be established, then perhaps available climatological data can be used to estimate the parameters rather than requiring years of rain-rate observations to set the parameters. The two-component model provided the best fit to the Wallops Island data but the Moupfuma model provided the best fit to the Darwin data.

Crane, Robert K.↗

NASA NTRS1982

A preliminary study of the particulate fallout from the space shuttle (STS-1)

Prediction of acid fallout and the dry deposition of Al2O2 was the objective of this investigation. Sampling was done at the Kennedy Space Center in Florida. The models available were not appropriate and the data available was negligible. Thus, a bimodal particle distribution was assumed normalized to the few existing data points and used as a foundation for a crude zeroth order approximation for the acid fallout. In addition, a settling spectrum for the Al2O3 particles was devised as a table look-up since the graphs in the literature at first pass could not be fitted with reasonable analytic functions. Consulting services were rendered to researchers. Special emphasis was placed on improving current techniques and adding LIDAR (Light Radar). Suggestions for future studies are made.

Hudson, G. M.↗

NASA NTRS2020

Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (LDAS) and Other Assimilated Hydrological Data at NASA GES DISC

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) provides science support for several data sets relevant to agriculture and food security, including the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (LDAS), or FLDAS data set. The GES DISC is one of twelve NASA Earth Observing System (EOS) data centers that process, archive, document, and distribute data from Earth science missions and related projects. The GES DISC hosts a wide range of remote sensing and model data, and provides reliable and robust data access and other services to users worldwide. Beyond data archive and access, the GES DISC offers many services to visualize and analyze the data. This presentation provides a summary of the hydrological data available at the GES DISC, along with an overview of related data services. Specifically, the FLDAS data set has been adapted to work with domains, data streams, and monitoring and forecast requirements associated with food security assessment in data-sparse, developing country settings. The FLDAS global monthly data have a 0.1 x 0.1 degree spatial resolution covering the period from January 1982 to present. Global FLDAS monthly anomaly and monthly climatology data are also available at the GES DISC to evaluate how current conditions compare to averages over the FLDAS 35-year period. Several case studies using the FLDAS soil moisture, evapotranspiration, rainfall, runoff, and surface temperature data will be presented.

Loeser, Carlee↗

NASA NTRS1967

Solubility data are compiled for metals in liquid zinc

Available data is compiled on the solubilities of various metals in liquid zinc. The temperature dependence of the solubility data is expressed using the empirical straight line relationship existing between the logarithm of the solubility and the reciprocal of the absolute temperature.

Dillon, I. G.↗