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NASA NTRS1984

In Situ Data

In-situ planetary data include all measurements made by a wide variety of instruments whose sensors are exposed directly to the planetry environment. Typical measurements include those of magnetic fields, energetic particle densities and energy distributions, plasma wave characteristics, ionospheric and neutral gas densities, temperatures, composition and motions. A large body of scientists in the United States, and the world, are involved in research using in-situ data. Many of these scientists are associated with Mission investigations groups which provide specific types of in-situ data. These groups, in the course of data analysis, use data provided by similar groups. The interdisciplinary nature of these investigations fosters exchange and reliance upon one another for data is heavy. To a lesser extent, correlative data sets are obtained from the National Space Science Data Center (NSSDC). However, the data entries often have limited temporal and spatial resolution, are of uncertain quality, and are usually available only for measurements that are several years old.

Source record↗

NASA NTRS1986

Full-scale ground effects of twin impinging jets beneath a subsonic tactical V/STOL aircraft

This paper presents a summary of an analysis of flow field characteristics about a large scale twin-tilt-nacelle V/STOL aircraft in close proximity to the ground. The analysis uses experimental data in the form of measurements of pressure distributions on the ground beneath the aircraft and on the undersurface of the aircraft's fuselage. The integrals of these distributions are compared with forces measured directly, for both the total model and isolated nacelles, (i.e., net thrust). The handling qualities of the vehicle were found to be influenced by flow field characteristic features, not present in simpler models. Lift increases and significant degradation in roll control occur at low heights for fuselage strake deflections of 30 degrees or more. Reductions in control effectiveness about the other axes were also observed.

Dudley, Michael R.↗

NASA NTRS2000

A Spacebased Ocean Surface Exchange Data Analysis System

Emerging technologies have provided unprecedented opportunities to transform information into knowledge and disseminate them in a much faster, cheaper, and userfriendly mode. We have set up a system to produce and disseminate high level (gridded) ocean surface wind data from the NASA Scatterometer and European Remote Sensing missions. The data system is being expanded to produce real-time gridded ocean surface winds from an improved sensor SeaWinds on the Quikscat Mission. The wind field will be combined with hydrologic parameters from the Tropical Rain Measuring Mission to monitor evolving weather systems and natural hazard in real time. It will form the basis for spacebased Ocean Surface Exchange Data Analysis System (SOSEDAS) which will include the production of ocean surface momentum, heat, and water fluxes needed for interdisciplinary studies of ocean-atmosphere interaction. Various commercial or non-commercial software tools have been compared and selected in terms of their ability in database management, remote data accessing, graphical interface, data quality, storage needs and transfer speed, etc. Issues regarding system security and user authentication, distributed data archiving and accessing, strategy to compress large-volume geophysical and satellite data/image. and increasing transferring speed are being addressed. A simple and easy way to access information and derive knowledge from spacebased data of multiple missions is being provided. The evolving 'knowledge system' will provide relevant infrastructure to address Earth System Science, make inroads in educating an informed populace, and illuminate decision and policy making.

Tang, Wenqing↗

NASA NTRS2013

Experimental Investigation of Flow Condensation in Microgravity

Future manned missions to Mars are expected to greatly increase the space vehicle's size, weight, and heat dissipation requirements. An effective means to reducing both size and weight is to replace single-phase thermal management systems with two-phase counterparts that capitalize upon both latent and sensible heat of the coolant rather than sensible heat alone. This shift is expected to yield orders of magnitude enhancements in flow boiling and condensation heat transfer coefficients. A major challenge to this shift is a lack of reliable tools for accurate prediction of two-phase pressure drop and heat transfer coefficient in reduced gravity. Developing such tools will require a sophisticated experimental facility to enable investigators to perform both flow boiling and condensation experiments in microgravity in pursuit of reliable databases. This study will discuss the development of the Flow Boiling and Condensation Experiment (FBCE) for the International Space Station (ISS), which was initiated in 2012 in collaboration between Purdue University and NASA Glenn Research Center. This facility was recently tested in parabolic flight to acquire condensation data for FC-72 in microgravity, aided by high-speed video analysis of interfacial structure of the condensation film. The condensation is achieved by rejecting heat to a counter flow of water, and experiments were performed at different mass velocities of FC-72 and water and different FC-72 inlet qualities. It is shown that the film flow varies from smooth-laminar to wavy-laminar and ultimately turbulent with increasing FC-72 mass velocity. The heat transfer coefficient is highest near the inlet of the condensation tube, where the film is thinnest, and decreases monotonically along the tube, except for high FC-72 mass velocities, where the heat transfer coefficient is enhanced downstream. This enhancement is attributed to both turbulence and increased interfacial waviness. One-ge correlations are shown to predict the average condensation heat transfer coefficient with varying degrees of success, and a recent correlation is identified for its superior predictive capability, evidenced by a mean absolute error of 21.7%.

microgravity↗

NASA NTRS2019

Discovery and Vetting of Exoplanets. I. Benchmarking K2 Vetting Tools

We have adapted the algorithmic tools developed during the Kepler mission to vet the quality of transit-like signals for use on the K2 mission data. Using the four sets of publicly available light curves at MAST, we produced a uniformly vetted catalog of 772 transiting planet candidates from K2 as listed at the NASA Exoplanet Archive in the K2 Table of Candidates. Our analysis marks 676 of these as planet candidates and 96 as false positives. All confirmed planets pass our vetting tests. Sixty of our false positives are new identifications, effectively doubling the overall number of astrophysical signals mimicking planetary transits in K2 data. Most of the targets listed as false positives in our catalog show either prominent secondary eclipses, transit depths suggesting a stellar companion instead of a planet, or significant photocenter shifts during transit. We packaged our tools into the open-source, automated vetting pipeline Discovery and Vetting of Exoplanets (DAVE), designed to streamline follow-up efforts by reducing the time and resources wasted observing targets that are likely false positives. DAVE will also be a valuable tool for analyzing planet candidates from NASA's TESS mission, where several guest-investigator programs will provide independent light-curve sets—and likely many more from the community. We are currently testing DAVE on recently released TESS planet candidates and will present our results in a follow-up paper.

Kostov, Veselin B.↗

NASA NTRS2023

Survival of Juvenile Florida Scrub-Jays is Habitat Specific, Positively Correlated With Month and Negatively Correlated With Male Breeder Death

Juvenile survival in birds is difficult to estimate but this vital rate can be an important consideration for management decisions. We estimated juvenile survival of cooperatively breeding Florida Scrub-Jays (Aphelocoma coerulescens) in a landscape degraded by fire suppression and fragmentation using data from marked (n = 325) and unmarked juveniles (n = 1,306) with an integrated hierarchical Bayesian model. To assess the combined analyses, we also analyzed these datasets separately, with a Cormack-Jolly-Seber (CJS) model (marked) and young model (unmarked). Our data consisted of monthly censuses of territorial family groups from Florida Scrub-Jay populations in East Central Florida collected over a 22-yr period. Juvenile survival was estimated from July when young Florida Scrub-Jays begin developing independence to March when they become first year individuals and grouped according to the habitat quality class of their natal territory that were based on shrub height (with intermediate shrub heights being optimal and short and tall shrub heights being suboptimal) and the presence of sandy openings (the preferred open having many sandy openings; closed not having enough). Parameter estimates in the combined analysis were intermediate to the separate analyses. Notable differences among the separate analyses were that suboptimal habitat survival was lower in the unmarked analysis, the unmarked analysis showed a linear effect of time not seen in the marked analysis, and there was an effect of male breeder death in the marked but not unmarked analysis. The combined data analysis provided more inference than did either data set analyzed separately including juveniles in optimal-closed territories unexpectedly had higher survival than those in optimal-open, survival increased through time, and male breeder death had a negative effect on survival. This study suggests that optimal-closed habitat may play an important role in juvenile Florida Scrub-Jay survival perhaps by providing better cover from predators and warrants further investigation for management implications.

Florida Scrub-Jay↗

NASA NTRS2017

GES DISC Greenhouse Gas Data Sets and Associated Services

NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) archives and distributes rich collections of data on atmospheric greenhouse gases from multiple missions. Hosted data include those from the Atmospheric Infrared Sounder (AIRS) mission (which has observed CO2, CH4, ozone, and water vapor since 2002); legacy water vapor and ozone retrievals from TIROS Operational Vertical Sounder (TOVS); and Upper Atmosphere Research Satellite (UARS) going back to the early 1980s. GES DISC also archives and supports data from seven projects of the Making Earth System Data Records for Use in Research Environments (MEaSUREs) program that have ozone and water vapor records. Greenhouse gases data from the A-Train satellite constellation is also available: (1) Aura-Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS) ozone, nitrous oxide, and water vapor since 2004; (2) Greenhouse Gases Observing Satellite (GOSAT) CO2 observations since 2009 from the Atmospheric CO2 Observations from Space (ACOS) task; and (3) Orbiting Carbon Observatory-2 (OCO-2) CO2 data since 2014. The most recent related data set that the GES DISC archives is methane flux for North America, as part of NASAs Carbon Monitoring System (CMS) project. This dataset contains estimates of methane emission in North America based on an inversion of the GEOS-Chem chemical transport model constrained by GOSAT observations (Turner et al., 2015). Along with data stewardship, an important focus area of the GES DISC is to enhance the usability of its data and broaden its user base. Users have unrestricted access to a new user-friendly search interface, which includes many services such as variable subsetting, format conversion, quality screening, and quick browse. The majority of the GES DISC data sets are also accessible through Open-source Project for a Network Data Access Protocol (OPeNDAP) and Web Coverage Service (WCS). The latter two services provide more options for specialized subsetting, format conversion, and image viewing. Additional data exploration, data preview, and preliminary analysis capabilities are available via NASA Giovanni, which obviates the need forusers to download the data (Acker and Leptoukh, 2007). Giovanni provides a bridge between the data and science and has been very successful in extending GES DISC data to educational users and to users with limited resources.

data ordering↗

NASA NTRS2023

Unalakleet Climate: Analyzing Permafrost Degradation and Drainage Networks in Unalakleet, Alaska

The coastal community of Unalakleet is currently the 8th most at-risk community in Alaska due to the adverse effects of climate change that include permafrost degradation, severe coastal erosion, and sea-level rise-induced flood inundation caused by increasingly frequent storm surges. In response, the community has started a managed relocation with support from the Native Village of Unalakleet (NVU) and the National Renewable Energy Lab (NREL)’s Alaska campus in Fairbanks. The Unalakleet Climate NASA DEVELOP team partnered with NREL to provide remote sensing support and analysis for resilience planning in Unalakleet, supporting their ongoing relocation efforts and guiding future expansion. The team utilized Sentinel-1 C-Synthetic Aperture Radar (SAR), WorldView-2, and WorldView-3 datasets from 2017 – 2023 to analyze seasonal summer subsidence and utilized a 2014 Ancillary USGS 5 m Alaska DEM to conduct drainage network analyses that included watershed delineation and Height Above Nearest Drainage (HAND) analysis. The team also used high-resolution WorldView images to locate stable reference points that served as quality control for the team’s analyses. The team’s end products included maps containing subsidence and drainage zones information at and surrounding the relocation site. The team’s products provide NREL with valuable data that enables them to better assist Unalakleet’s managed relocation and assists Unalakleet with adapting to the catastrophic effects of climate change and build resilience in a community on the front lines of climate change.

Ian Lee↗

NASA NTRS2021

The Salinity Pilot-Mission Exploitation Platform (Pi-MEP): A Hub for Validation and Exploitation of Satellite Sea Surface Salinity Data

The Pilot-Mission Exploitation Platform (Pi-MEP) for salinity is an ESA initiative originally meant to support and widen the uptake of Soil Moisture and Ocean Salinity (SMOS) mission data over the ocean. Starting in 2017, the project aims at setting up a computational web-based platform focusing on satellite sea surface salinity data, supporting studies on enhanced validation and scientific process over the ocean. It has been designed in close collaboration with a dedicated science advisory group in order to achieve three main objectives: gathering all the data required to exploit satellite sea surface salinity data, systematically producing a wide range of metrics for comparing and monitoring sea surface salinity products’ quality, and providing user-friendly tools to explore, visualize and exploit both the collected products and the results of the automated analyses. The Salinity Pi-MEP is becoming a reference hub for the validation of satellite sea surface salinity missions by providing valuable information on satellite products (SMOS, Aquarius, SMAP), an extensive in situ database (e.g., Argo, thermosalinographs, moorings, drifters) and additional thematic datasets (precipitation, evaporation, currents, sea level anomalies, sea surface temperature, etc.). Co-localized databases between satellite products and in situ datasets are systematically generated together with validation analysis reports for 30 predefined regions. The data and reports are made fully accessible through the web interface of the platform. The datasets, validation metrics and tools (automatic, user-driven) of the platform are described in detail in this paper. Several dedicated scientific case studies involving satellite SSS data are also systematically monitored by the platform, including major river plumes, mesoscale signatures in boundary currents, high latitudes, semi-enclosed seas, and the high-precipitation region of the eastern tropical Pacific. Since 2019, a partnership in the Salinity Pi-MEP project has been agreed between ESA and NASA to enlarge focus to encompass the entire set of satellite salinity sensors. The two agencies are now working together to widen the platform features on several technical aspects, such as triple-collocation software implementation, additional match-up collocation criteria and sustained exploitation of data from the SPURS campaigns

ocean↗

NASA NTRS1973

Preliminary analysis of ERTS-relayed water resources data in the Delaware River Basin

Preliminary analysis of ERTS-DCS data from water-resources stations in the Delaware River Basin indicates that the Data-Collection System is performing well. Data-Collections Platforms have been successfully interfaced with five stream-gaging station and three ground-water observation wells and are being interfaced with 12 water-quality monitors in the basin. Data are being relayed during four or five ERTS orbital passes per day, which is within the design specifications of the ERTS-DCS.

Paulson, R. W.↗

NASA NTRS1976

Results of Skylab experiment T00-2, manual navigation sightings

An analysis of navigation data collected using a hand-held space sextant on the second and third manned Skylab missions was presented. From performance data and astronaut comments it was determined that: (1) the space sextant, the sighting station, and the sighting techniques require modification; (2) the sighting window must be of good optical quality; (3) astronaut performance was stable over long mission time; and (4) sightings made with a hand-held sextant were accurate and precise enough for reliable interplanetary manual navigation.

Randle, R. J.↗

NASA NTRS1980

Air quality monitor and acid rain networks

The air quality monitor program which consists of two permanent air monitor stations (PAMS's) and four mobile shuttle pollutant air monitor stations (SPAMS's) is evaluated. The PAMS measures SO sub X, NO sub X particulates, CO, O3, and nonmethane hydrocarbons. The SPAMS measures O3, SO2, HCl, and particulates. The collection and analysis of data in the rain monitor program are discussed.

Rudolph, H.↗

NASA NTRS1991

NACA documents database project

The plan to get all the National Advisory Committee on Aeronautics (NACA) collection online, with quality records, led to the NACA Documents Data base Project. The project has a two fold purpose: (1) to develop the definitive bibliography of NACA produced and/or held documents; and (2) to make that bibliography and the associated documents available to the aerospace community. This study supports the first objective by providing an analysis of the NACA collection and its bibliographic records, and supports the second objective by defining the NACA archive and recommending methodologies for meeting the project objectives.

Smith, Ruth S.↗

NASA NTRS1993

Analysis of the Space Shuttle main engine simulation

This is a final report on an analysis of the Space Shuttle Main Engine Program, a digital simulator code written in Fortran. The research was undertaken in ultimate support of future design studies of a shuttle life-extending Intelligent Control System (ICS). These studies are to be conducted by NASA Lewis Space Research Center. The primary purpose of the analysis was to define the means to achieve a faster running simulation, and to determine if additional hardware would be necessary for speeding up simulations for the ICS project. In particular, the analysis was to consider the use of custom integrators based on the Matrix Stability Region Placement (MSRP) method. In addition to speed of execution, other qualities of the software were to be examined. Among these are the accuracy of computations, the useability of the simulation system, and the maintainability of the program and data files. Accuracy involves control of truncation error of the methods, and roundoff error induced by floating point operations. It also involves the requirement that the user be fully aware of the model that the simulator is implementing.

Deabreu-Garcia, J. Alex↗

NASA NTRS2008

Characterization of MODIS VIS/NIR Spectral Band Detector-to-Detector Difference

MODIS has 36 spectral bands with wavelengths in the visible (VIS), near-infrared (NIR), shortwave infrared (SWTR), mid-wave infrared (MWIR), and long-wave infrared (LWIR). It makes observations at three nadir spatial resolutions: 0,25km for bands 1-2 with 40 detectors per band, 0.5km for bands 3-7 with 20 detectors per band, and 1km for bands 8-36 with 10 detectors per band. The VIS, NIR, and S\VIR spectral bands are the reflective solar bands (RSB), which are calibrated on-orbit by a solar diffuser (SD). In addition, MODIS lunar observations are used to track the RSB calibration stability. In this study, we examine detector-to-detector calibration difference for the VIStNIR spectral bands using the SD and lunar observations. The results will be compared with an independent analysis with additional information, such as polarization correction, derived from standard ocean color data products. The current MODIS RSB calibration approach only carries a band-averaged RVS (response versus scan angle) correction. The results from this study suggest that a detector-based RVS correction should be used to improve the L1B data quality, especially for several VIS bands in Terra MODIS due to large changes of the scan mirror's optical properties in recent years.

Xiong, X.↗

NASA NTRS2011

Assessment of Cloud Screening with Apparent Surface Reflectance in Support of the ICESat-2 Mission

The separation of cloud and clear scenes is usually one of the first steps in satellite data analysis. Before deriving a geophysical product, almost every satellite mission requires a cloud mask to label a scene as either clear or cloudy through a cloud detection procedure. For clear scenes, products such as surface properties may be retrieved; for cloudy scenes, scientist can focus on studying the cloud properties. Hence the quality of cloud detection directly affects the quality of most satellite operational and research products. This is certainly true for the Ice, Cloud, and land Elevation Satellite-2 (lCESat-2), which is the successor to the ICESat-l. As a top priority mission, ICESat-2 will continue to provide measurements of ice sheets and sea ice elevation on a global scale. Studies have shown that clouds can significantly affect the accuracy of the retrieved results. For example, some of the photons (a photon is a basic unit of light) in the laser beam will be scattered by cloud particles on its way. So instead of traveling in a straight line, these photons are scattered sideways and have traveled a longer path. This will result in biases in ice sheet elevation measurements. Hence cloud screening must be done and be done accurately before the retrievals.

Yang, Yuekui↗

NASA NTRS2022

Evaluation of Retrieving Chlorophyll a Concentration and Colored Dissolved Organic Matter Absorption from Satellite Ocean Color Remote Sensing in the Coastal Waters of Hokkaido, Japan

Coastal ecosystems are important in biogeochemical cycles, because they provide many pathways for chemical elements and compounds to flow between the physical environment and living organisms. The spatial and temporal scales of the biotic and abiotic interactions establish the inherent dynamics of the coastal zone and the requirement for high-resolution investigations. Using match-up data collected in Hokkaido coastal waters, the study herein evaluated the performance of deriving chlorophyll (Chl) a concentration and colored dissolved organic matter (CDOM) absorption (a(sub CDOM)) as a function of wavelength (λ) from satellite observations. The Japanese Aerospace Exploration Agency (JAXA) Second generation GLobal Imager (SGLI), which has 250 m spatial resolution, was compared with other satellite ocean color (OC) sensors. Our results show that the standard SGLI OC4 algorithm with the 530 nm band provided the best performance for Chl a retrievals, and the end-member analysis (EMA) technique improved the estimation of a(sub CDOM)(λ) . Overall differences between in situ radiometric data and satellite retrievals suggest that additional challenges remain, especially in the ultraviolet and blue spectral domains which are useful for studying CDOM and harmful algae blooms in coastal waters. To fulfill applications wherein high-quality remote sensing data are required, the improvement of the atmospheric correction is a likely research area where additional accomplishment will be beneficial for satellite observations of optically complex coastal waters.

SGLI/GCOM-C↗

NASA NTRS2021

Time series comparisons in Deep Space Network

The Deep Space Network (DSN) is NASA’s international array of antennas that support interplanetary spacecraft missions. DSN provides radar and radio astronomy observations that enhance our understanding of the solar system and the larger universe. A track is a block of continuous multi-dimensional time series from the beginning to end of DSN communication with the target spacecraft, containing 129 monitor data items lasting several hours at a frequency of 0.2-1Hz. Monitor data on each track reports on the performance of specific spacecraft operations and the DSN itself. DSN is receiving signals from 32 spacecraft across the solar system. DSN has pressure to reduce costs while maintaining the quality of support for DSN mission users. DSN operators need to simultaneously monitor multiple tracks and identify anomalies in real time. DSN has seen that as the number of missions increases, the data that needs to be processed increases over time. In this project, we look at the last 8 years of data for analysis. Any anomaly in the track indicates a problem with either the spacecraft, DSN equipment, or weather conditions. DSN operators typically write “discrepancy reports” for further analysis. It is recognized that it would be quite helpful to identify 10 similar historical tracks out of the huge database to quickly find/match anomalies. This tool has three functions: (1) identification of the top 10 similar historical tracks, (2) detection of anomalies compared to the reference normal track, and (3) comparison of statistical differences between two given tracks. The requirements for these features were confirmed by survey responses from 21 DSN operators and engineers. The preliminary machine learning model has shown promising performance (AUC=0.92). We plan to increase the number of data sets and perform additional testing to improve performance further before its planned integration into the Track Visualizer to assist DSN field operators and engineers.

Rebbapragada, Umaa↗