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All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Satellite-Based Drought Reporting on the Navajo Nation

The Navajo Nation (NN) is the largest reservation in the US, and faces challenges related to water management during long-term and widespread drought episodes. The Navajo Nation is a federally recognized tribe, which has boundaries within Arizona, New Mexico, and Utah. The Navajo Nation has a land area of over 70,000 square kilometers. The Navajo Nation Department of Water Resources (NNDWR) reports on drought and climatic conditions through the use of regional Standardized Precipitation Index (SPI) values and a network of in-situ rainfall, streamflow, and climate data. However, these data sources lack the spatial detail and consistent measurements needed to provide a coherent understanding of the drought regime within the Nation's regional boundaries. This project, as part of NASA's Western Water Applications Office (WWAO), improves upon the recently developed Drought Severity Assessment Tool (DSAT) to ingest satellite-based precipitation data to generate SPI values for specific administrative boundaries within the reservation. The tool aims to: (1) generate SPI values and summary statistics for regions of interest on various timescales, (2) to visualize SPI values within a web-map application, and (3) produce maps and comparative statistical outputs in the format required for annual drought reporting. The co-development of the DSAT with NN partners is integral to increasing the sustained use of Earth Observations for water management applications. This tool will provide data to support the NN in allocation of drought contingency dollars to the regions most adversely impacted by declines in water availability.

Satellite-based↗
NASA NTRS1997

Balance Velocities of the Greenland Ice Sheet

We present a map of balance velocities for the Greenland ice sheet. The resolution of the underlying DEM, which was derived primarily from radar altimetry data, yields far greater detail than earlier balance velocity estimates for Greenland. The velocity contours reveal in striking detail the location of an ice stream in northeastern Greenland, which was only recently discovered using satellite imagery. Enhanced flow associated with all of the major outlets is clearly visible, although small errors in the source data result in less accurate estimates of the absolute flow speeds. Nevertheless, the balance map is useful for ice-sheet modelling, mass balance studies, and field planning.

Joughin, Ian↗
NASA NTRS2000

Using TRMM Field Campaign Data for Assessing GEOS Forecast and Assimilation Products

The wealth of in-situ measurements gathered during Tropical Rain Measuring Mission (TRMM) field campaigns over a wide range of tropical conditions constitute an important data source for evaluating the quality of global model forecasts and assimilated datasets. In this study we use selected observations of cloud microphysics and atmospheric sounding from TEFLUN-1998, SCEMEX-1998, and TRMMLBA-1999 to examine the assimilation and forecast fields produced by the operational GEOS-3 (Goddard Earth Observing System - version 3) global data assimilation system (DAS) and a new finite-volume DAS under development at the Data Assimilation Office. Additionally, TRMM field campaign measurements are used to verify the impact of assimilating rainfall and moisture data derived from TRMM Microwave Imager and Special Sensor Microwave/Imager instruments on the GEOS analysis. We will also explore issues concerning the 'error of representativeness' in using in-situ observations of quantities with large spatial and temporal variability such as precipitation for validating gridded global data products.

Hou, Arthur Y.↗
NASA NTRS2001

Calibration of Sea Ice Motion from QuikSCAT with those from SSM/I and Buoy

QuikSCAT backscatter and DMSP SSM/I radiance data are used to derive sea ice motion for both the Arctic and Antarctic region using wavelet analysis method. This technique provides improved spatial coverage over the existing array of Arctic Ocean buoys and better temporal resolution over techniques utilizing satellite data from Synthetic Aperture Radar (SAR). Sea ice motion of the Arctic for the period from October 1999 to March 2000 derived from QuikSCAT and SSM/I data agrees well with that derived from ocean buoys quantitatively. Thus the ice tracking results from QuikSCAT and SSM/I are complement to each other, Then, three sea-ice drift daily results from QuikSCAT, SSM/I, and buoy data can be merged to generate composite maps with more complete coverage of sea ice motion than those from single data source. A series of composite sea ice motion maps for December 1999 show that the major circulation patterns of sea ice motion are changing and shifting significantly within every four days and they are dominated by wind forcing. Sea-ice drift in the summer can not be derived from NSCAT and SSM/I data. In later summer of 1999 (in September), however, QuikSCAT data can provide good sea ice motion information in the Arctic. QuiksCAT can also provide at least partial sea ice motion information until June 15 in early summer 1999. For the Antarctic, case study shows that sea ice motion derived from QuikSCAT data is predominantly forced by and is consistent with wind field derived from QuikSCAT around the polar region. These calibrated/validated results indicate that QuikSCAT, SSM/I, and buoy merged daily ice motion are suitably accurate to identify and closely locate sea ice processes, and to improve our current knowledge of sea ice drift and related processes through the data assimilation of ocean-ice numerical model.

Liu, Antony K.↗
NASA NTRS2020

Medical Data Architecture Prototype Development - Summary of Recent Work and Proposed Ideas for Upcoming Work

The Medical Data Architecture (MDA) project supports the Exploration Medical Capability (ExMC) risk to minimize or reduce the risk of adverse health outcomes and decrements in performance due to in-flight medical capabilities on human exploration missions. To mitigate this risk, the ExMC MDA project addresses the technical limitations identified in ExMC Gap Med 07: We do not have the capability to comprehensively process medically-relevant information to support medical operations during exploration missions, and in ExMC Gap Med 10: We do not have the capability to provide computed medical decision support during exploration missions. These gaps recognize the need for a comprehensive medical data management system and the accompanying computational support to provide autonomous medical care during long duration exploration missions. As the MDA matures—including the capability to comprehensively process and discover medically-relevant information to support medical operations during exploration missions—project focus will shift to maturing and extending the MDA platform to enable clinical decision support and real-time guidance. To date, the MDA foundational architecture has recommended exploration medical system Level of Care IV requirements through a series of test bed prototype developments and analog demonstrations. The next stage in the development will focus on more autonomous clinical decision making necessary to address challenges in executing a self-contained medical system that enables health care both with and without assistance from ground support. A thorough understanding of current state of medical decision support systems, advanced machine learning algorithms and vast and varied data sources is required. The development of a clinical decision support for exploration missions (Level of Care V) roadmap is needed: one that assesses of current state of the art of clinical decision support systems (CDSS), interoperability issues, identification of challenges in health and performance monitoring, obtaining and processing information from biosensors, knowledge and data management, data integration and fusion, and advanced algorithm development. This roadmap must also include rapid prototype development in the areas of data processing, advanced analysis and prediction of medical events, and treatment based on medically relevant information processing and evidence-based best practices. In this presentation, an overview of the relevant issues and the beginning framework of a Level of Care V CDSS development roadmap will be provided.

Krihak, M.↗
NASA NTRS1988

Effects of variables upon pyrotechnically induced shock response spectra, part 2

Throughout the aerospace industry, large variations of 50 percent (6 dB) or more in shock response spectra (SRS) derived from pyrotechnic separation events continue to be reported from actual spaceflight data and from laboratory tests. As a result of these variations, NASA funded a research program for 1984 through 1986. The purpose of the 1984 through 1986 project was to analyze variations in pyrotechnically induced SRS and to determine if and to what degree manufacturing and assembly variables and tolerances, distance from the shock source, data acquisition instrumentation, and shock energy propagation affect the SRS. Sixty-four free-free boundary plate tests were performed. NASA funded an additional study for 1987 through 1988. This paper is a summary of the additional study. The purpose was to evaluate shock dissipation through various spacecraft structural joint types, to evaluate shock variation for various manufacturing and assembly variables on clamped boundary test plates, and to verify data correction techniques. Five clamped boundary plate tests investigated manufacturing and assembly variables and mass loading effects. Six free-free boundary plate tests investigated shock dissipation across spacecraft joint structures.

Smith, James Lee↗
NASA NTRS2019

Seasonal and Diurnal Opportunities for XCH4, XCO2, and XCO for the Amazonian Rainforest Region Allowing Sampling and Validation

There is considerable interest in the aggregate methane emissions from the Amazon and similar moist tropical regions, and XCH4 measurements are well suited to constrain sources to the global atmosphere. Similarly, XCO2 measurements constrain CO2 in the region. XCO helps to partition CO2 patterns among burning and respiration processes. GeoCarb may allow these column measurements over the Western Hemisphere, but satellite retrieval require exacting calibration and validation by sun-focused Fourier transform spectrometers (FTS). The rarity of sufficiently large gaps in the cloud cover over the Amazon and similar rainforests restricts the validation opportunities for useful FTS observations and even more the opportunities for accurate retrievals. TropOMI observational statistics are extremely poor for the region. We have used two data sources to evaluate FTS opportunities at Manaus, Brazil, an FTS operated for 8 months near Manaus by Mavendra Dubey, and also sun-photometer measurements at several stations. The promise of using data from other satellites, e.g. GOES-16 ( (Geostationary Operational Environmental Satellite) and CALIOP (Cloud-Aerosol LIdar with Orthogonal Polarization gaps and aerosol layering will be described. We report initial results on five questions: (1) how frequent are observing opportunities of FTS?, (2) What evidence is there that gaps in clouds are wide enough for satellite retrievals at an appropriate accuracy, (3) What is the diurnal and seasonal variability of cloud gaps?, and (4) What limitations are currently suggested for unbiased FTS measurement of XCH4 due to diurnal effects, and (5) What evidence is there for incidence of problematic high aerosol extinction at higher layers of the troposphere (800 hPa to 120 hPa) which alter the XCH4 light-paths?

Chatfield, Robert↗
NASA NTRS1973

Coordination and establishment of centralized facilities and services for the University of Alaska ERTS survey of the Alaskan environment

The author has identified the following significant results. Scene 1072-21173 of the Anaktuvuk Pass region of the Brooks Range, Alaska, was studied from the point of view of a resource survey for purposes of land use planning as part of the effort to develop ERTS data processing and interpretation techniques. Other data sources and surface observations were utilized to produce a resource survey of a remote and undeveloped region of Alaska. Three vegetative types are apparent: moist tundra, low brush, and high brush. Watersheds are easily defined on the multispectral imagery. Features related indirectly to economic minerals are discernible from ERTS-1 imagery supported by ground truth data. These include mountains, outwash plains and alluvial deposits, drainage patterns, lineaments and probable bedding planes. This region falls within present land class categories which are not inconsistent with the imperatives of the resources. These land class categories include native village withdrawals, regional deficiency area, national interest study area for possible inclusion in a national system, public interest areas, utility corridor, and state land selection.

Belon, A. E.↗
NASA NTRS1994

Pinatubo Aerosol Evolution: Using Composite Data Sets to Build the Global- to Micro-Scale Picture and Assess Consistency of Different Measurements

This paper brings together experimental evidence required to build realistic models of the global evolution of physical, chemical, and optical properties of the aerosol resulting from the 1991 Pinatubo volcanic eruption. Such models are needed to compute the effects of the aerosol on atmospheric chemistry, dynamics, radiation, and temperature. Whereas there is now a large and growing body of post-Pinatubo measurements by a variety of techniques, some results are in conflict, and a self-consistent, unified picture is needed, along with an assessment of remaining uncertainties. This paper examines data from photometers, radiometers, impactors, optical counters/sizers, and lidars operated on the ground, aircraft, balloons, and spacecraft. Example data sources include: - Tracking sunphotometers and lidars at Mauna Loa Observatory (MLO) and on the DC-8 - Particle spectrometers and wire impactors on the ER-2 and DC-8 - Dustsondes (particle counters/sizers on balloons) - SAGE II, SAM II, AVHRR, CLAES, and ISAMS sensors on a variety of satellites. We assess the mutual consistency of these disparate data sets and recommend 'consensus" properties and uncertainties in the process of developing a composite data set. Recommended properties include the spatial and temporal evolution of particle chemical composition, shape, wavelength and temperature-dependent refractive index, size distribution, and optical depth spectra. Supporting references are cited and representative data shown.

Russell, P. B.↗
NASA NTRS2014

Technical Data Interoperability (TDI) Pathfinder Via Emerging Standards

The TDI project (TDI) investigates trending technical data standards for applicability to NASA vehicles, space stations, payloads, facilities, and equipment. TDI tested COTS software compatible with a certain suite of related industry standards for capabilities of individual benefits and interoperability. These standards not only esnable Information Technology (IT) efficiencies, but also address efficient structures and standard content for business processes. We used source data from generic industry samples as well as NASA and European Space Agency (ESA) data from space systems.

Technical Data↗
NASA NTRSDate not supplied

Supporting Exploration Missions by Enabling Exploration Mission System Software

Future exploration missions will consist of a multitude of data sources, systems, and operators collaborating to complete mission objectives. Presently, NASA is instantiating the contractual mechanisms, such as the xEVAS and HLS contracts, to produce these mission assets. Architectural planning is also underway to establish the networking protocols and infrastructure to digitally create and connect mission elements, such as LunaNET. However, without new horizontally integrated data systems, these advancements will be limited in their ability to get mission data appropriately integrated into the plan, train, fly, explore workflow of the operations workforce. Here we describe several mission system software development efforts underway that are designed to support human spaceflight missions. We describe the current iterations of a suite of tools to support EVA procedure authoring and execution, for both ISS and Artemis missions, as well as a software solution to establish and interact with mission context and data products. These tools have been developed iteratively and continue to be tested in several NASA facilities such as the Neutral Buoyancy Lab (NBL), Artemis field testing, and in present-day International Space Station (ISS) operations on orbit. Our solutions demonstrate how software development can be aligned with ongoing operations development activities to discover the features that best support future human spaceflight missions.

EVA Mission System Software↗
NASA NTRS1972

On the application of Rice's exceedance statistics to atmospheric turbulence.

Discrepancies produced by the application of Rice's exceedance statistics to atmospheric turbulence are examined. First- and second-order densities from several data sources have been measured for this purpose. Particular care was paid to each selection of turbulence that provides stationary mean and variance over the entire segment. Results show that even for a stationary segment of turbulence, the process is still highly non-Gaussian, in spite of a Gaussian appearance for its first-order distribution. Data also indicate strongly non-Gaussian second-order distributions. It is therefore concluded that even stationary atmospheric turbulence with a normal first-order distribution cannot be considered a Gaussian process, and consequently the application of Rice's exceedance statistics should be approached with caution.

Chen, W. Y.↗
NASA NTRS2019

Improvements in the Uncertainty Model in the Goddard Institute for Space Studies Surface Temperature (GISTEMP) Analysis

We outline a new and improved uncertainty analysis for the Goddard Institute of Space Studies (GISS) Surface Temperature product version 4 (GISTEMP v4). Historical spatial variations in surface temperature anomalies are derived from historical weather station data and ocean data from ships, buoys and other sensors. Uncertainties arise from measurement uncertainty, changes in spatial coverage of the station record, and systematic biases due to technology shifts and land cover changes. Previously published uncertainty estimates for GISTEMP included only the effect of incomplete station coverage. Here, we update this term using currently available spatial distributions of source data, state‐of‐the‐art reanalyses and incorporate independently derived estimates for ocean data processing, station homogenization and other structural biases. The resulting 95% uncertainties are near 0.05° C in the global annual mean for the last 50 years, and increase going back further in time reaching 0.15° C in 1880. In addition, we quantify the benefits and inherent uncertainty due to the GISTEMP interpolation and averaging method. We use the total uncertainties to estimate the probability for each record year in the GISTEMP to actually be the true record year (to that date), and conclude with 86% likelihood that 2016 was indeed the hottest year of the instrumental period (so far).

Nathan J L Lenssen↗
NASA NTRS2021

Evaluation of SWIR Crop Residue Bands for the Landsat Next Mission

This research reports the findings of a Landsat Next expert review panel that evaluated the use of narrow shortwave infrared (SWIR) reflectance bands to measure ligno-cellulose absorption features centered near 2100 and 2300 nm, with the objective of measuring and mapping non-photosynthetic vegetation (NPV), crop residue cover, and the adoption of conservation tillage practices within agricultural landscapes. Results could also apply to detection of NPV in pasture, grazing lands, and non-agricultural settings. Currently, there are no satellite data sources that provide narrowband or hyperspectral SWIR imagery at sufficient volume to map NPV at a regional scale. The Landsat Next mission, currently under design and expected to launch in the late 2020’s, provides the opportunity for achieving increased SWIR sampling and spectral resolution with the adoption of new sensor technology. This study employed hyperspectral data collected from 916 agricultural field locations with varying fractional NPV, fractional green vegetation, and surface moisture contents. These spectra were processed to generate narrow bands with centers at 2040, 2100, 2210, 2260, and 2230 nm, at various bandwidths, that were subsequently used to derive 13 NPV spectral indices from each spectrum. For crop residues with minimal green vegetation cover, two-band indices derived from 2210 and 2260 nm bands were top performers for measuring NPV (R^(2) = 0.81, RMSE = 0.13) using bandwidths of 30 to 50 nm, and the addition of a third band at 2100 nm increased resistance to atmospheric correction residuals and improved mission continuity with Landsat 8 Operational Land Imager Band 7. For prediction of NPV over a full range of green vegetation cover, the Cellulose Absorption Index, derived from 2040, 2100, and 2210 nm bands, was top performer (R^(2) = 0.77, RMSE = 0.17), but required a narrow (≤20 nm) bandwidth at 2040 nm to avoid interference from atmospheric carbon dioxide absorption. In comparison, broadband NPV indices utilizing Landsat 8 bands centered at 1610 and 2200 nm performed poorly in measuring fractional NPV (R^(2) = 0.44), with significantly increased interference from green vegetation

Wells Dean Hively↗
NASA NTRS2010

The TRMM Multi-Satellite Precipitation Analysis (TMPA)

The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) is intended to provide a "best" estimate of quasi-global precipitation from the wide variety of modern satellite-borne precipitation-related sensors. Estimates are provided at relatively fine scales (0.25 deg x 0.25 deg. 3-h) in both real and post-real time to accommodate a wide range of researchers. However, the errors inherent in the finest scale estimates are large. The most successful use of the TMPA data is when the analysis takes advantage of the fine-scale data to create time/space averages appropriate to the user fs application. We review the conceptual basis for the TMPA, summarize the processing sequence, and focus on two new activities. First, a recent upgrade for the real-time version incorporates several additional satellite data sources and employs monthly climatological adjustments to approximate the bias characteristics of the research quality post-real-time product. Second, an upgrade for the research quality post-real-time TMPA from Versions 6 to 7 (in beta test at press time) is designed to provide a variety of improvements that increase the list of input data sets and correct several issues. Future enhancements for the TMPA will include improved error estimation, extension to higher latitudes, and a shift to a Lagrangian time interpolation scheme.

Huffman, George J.↗
NASA NTRS2008

The TRMM Multi-Satellite Precipitation Analysis (TMPA)

The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) is intended to provide a "best" estimate of quasi-global precipitation from the wide variety of modern satellite-borne precipitation-related sensors. Estimates are provided at relatively fine scales (0.25degx0.25deg, 3-hourly) in both real and post-real time to accommodate a wide range of researchers. However, the errors inherent in the finest scale estimates are large. The most successful use of the TMPA data is when the analysis takes advantage of the fine-scale data to create time/space averages appropriate to the user s application. We review the conceptual basis for the TMPA, summarize the processing sequence, and focus on two new activities. First, a recent upgrade to the real-time version incorporates several additional satellite data sources and employs monthly climatological adjustments to approximate the bias characteristics of the research quality post-real-time product. Second, an upgrade of the research quality post-real-time TMPA from Version 6 to Version 7 (in beta test at press time) is designed to provide a variety of improvements that increase the list of input data sets and correct several issues. Future enhancements for the TMPA will include improved error estimation, extension to higher latitudes, and a shift to a Lagrangian time interpolation scheme.

Huffman, George J.↗
NASA NTRS2018

Knowledge Discovery Process: Case Study of RNAV Adherence of Radar Track Data

This talk is an introduction to the knowledge discovery process, beginning with: identifying the problem, choosing data sources, matching the appropriate machine learning tools, and reviewing the results. The overview will be given in the context of an ongoing study that is assessing RNAV adherence of commercial aircraft in the national airspace.

Machine Learning↗
NASA NTRS2013

Tool for Automated Retrieval of Generic Event Tracks (TARGET)

Methods have been developed to identify and track tornado-producing mesoscale convective systems (MCSs) automatically over the continental United States, in order to facilitate systematic studies of these powerful and often destructive events. Several data sources were combined to ensure event identification accuracy. Records of watches and warnings issued by National Weather Service (NWS), and tornado locations and tracks from the Tornado History Project (THP) were used to locate MCSs in high-resolution precipitation observations and GOES infrared (11-micron) Rapid Scan Operation (RSO) imagery. Thresholds are then applied to the latter two data sets to define MCS events and track their developments. MCSs produce a broad range of severe convective weather events that are significantly affecting the living conditions of the populations exposed to them. Understanding how MCSs grow and develop could help scientists improve their weather prediction models, and also provide tools to decision-makers whose goals are to protect populations and their property. Associating storm cells across frames of remotely sensed images poses a difficult problem because storms evolve, split, and merge. Any storm-tracking method should include the following processes: storm identification, storm tracking, and quantification of storm intensity and activity. The spatiotemporal coordinates of the tracks will enable researchers to obtain other coincident observations to conduct more thorough studies of these events. In addition to their tracked locations, their areal extents, precipitation intensities, and accumulations all as functions of their evolutions in time were also obtained and recorded for these events. All parameters so derived can be catalogued into a moving object database (MODB) for custom queries. The purpose of this software is to provide a generalized, cross-platform, pluggable tool for identifying events within a set of scientific data based upon specified criteria with the possibility of storing identified events into a searchable database. The core of the application uses an implementation of the connected component labeling (CCL) algorithm to identify areas of interest, then uses a set of criteria to establish spatial and temporal relationships between identified components. The CCL algorithm is used for identifying objects within images for computer vision. This application applies it to scientific data sets using arbitrary criteria. The most novel concept was applying a generalized CCL implementation to scientific data sets for establishing events both spatially and temporally. The combination of several existing concepts (pluggable components, generalized CCL algorithm, etc.) into one application is also novel. In addition, how the system is designed, i.e., its extensibility with pluggable components, and its configurability with a simple configuration file, is innovative. This allows the system to be applied to new scenarios with ease.

Clune, Thomas↗