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At least 379 records · Page 21

All sources NASA NTRS DOE OSTI

NASA NTRS2011

NASA Giovanni Portals for NLDAS/GLDAS Online Visualization, Analysis, and Intercomparison

The North American Land Data Assimilation System (NLDAS) and Global Land Data Assimilation System (GLDAS) are generating a series of land surface forcing (e.g., precipitation, surface meteorology, and radiation), state (e.g., soil moisture and temperature, and snow), and flux (e.g., evaporation and sensible heat flux) products, simulated by several land surface models. To date, NLDAS and GLDAS have generated more than 30 (1979 - present) and 60 (1948 - present) years of data, respectively. To further facilitate data accessibility and utilization, three new portals in the NASA Giovanni system have been made available for NLDAS and GLDAS online visualization, analysis, and intercomparison.

Rui, Hualan↗

NASA NTRS2008

The NPOESS Preparatory Project (NPP) Science Data Segment (SDS) Data Depository and Distribution Element (SD3E) System Architecture

The National Polar-orbiting Operational Environmental Satellite System (NPOESS), the U.S. Government's future low-Earth orbiting satellite system, will monitor global weather and environmental conditions. Serving as a risk reduction for NPOESS, the NPOESS Preparatory Project (NPP) will provide remotely sensed atmospheric, land, ocean, ozone, and sounder data that will serve the meteorological and global climate change scientific communities. The National Aeronautics and Space Administration (NASA) NPP Science Data Segment's (SDS) primary role is to independently assess the quality of the NPP science and environmental data records for their ability to support climate research. The SDS is composed of nine elements; an input element that receives data from the operational agencies and acts as a buffer, a calibration analysis element, five elements devoted to measurement based quality assessment, an element used to test algorithmic improvements, and an element that provides overall science direction. Each element requires a set of sensor specific science data products for their evaluation. There are four NPP sensors that will be flown on the NPP observatory. They are the Visible Infrared Imagining Radiometer Suite (VIIRS), the Advanced Technology Microwave Sounder (ATMS), the Cross-Track Infrared Sounder (CrIS), and the Ozone Mapper/Profiler Suite (OMPS). It is estimated that these four sensors combined will make daily data requests for approximately six terabytes of NPP science products from the operational data providers. As a result, issues associated with duplicate data requests, data transfers of large volumes of diverse products, and data transfer failures raised concerns with respect to the network traffic and bandwidth consumption. Therefore, a central data broker system for receiving and buffering data requests and data products for the SDS was developed. The data element for this system is called the SDS Data Depository and Distribution Element (SD3E). It supports science mission data assessment by assuring the timely and validated acquisition and subsequent transfer of the NPP Science Mission data to the SDS Elements and NPP Science Team. The six science elements that interface with the SD3E span across the NASA Goddard Space Flight Center (GSFC), the NASA Jet Propulsion Laboratory (JPL), and the University of Wisconsin. As the primary communication vehicle for the science elements and science team, the SD3E has an interface to the operational data providers: National Environment Satellite, Data, and Information Service (NESDIS) Interface Data Processing System (IDPS) and the National Oceanic Atmospheric Administration's (NOAA) Comprehensive Large Array-data Stewardship system (CLASS) Archive Data System (ADS), that are responsible for product generation and archive and distribution respectively. The SD3E is designed to be a semi-customizable and semi-automated system. This system is designed to provide flexibility and ease of use for the science users in accessing the latest data products by creating a rolling data cache that temporarily stores the products locally before transferring the data to the SDS Measurement based elements for the land, ocean, atmosphere, sounder, and ozone. This paper describes the design and architecture of one of the nine SDS elements, the SD3E, and how this system has provided a mechanism for efficient data exchange, how it has helped in alleviating some of the network traffic and usage, and how it has contributed to reducing operational costs.

Ho, Evelyn L.↗

NASA NTRS1994

Strategy for selecting Mars Pathfinder landing sites

A strategy for Pathfinder site selection must be developed that is fundamentally different from most previous considerations. At least two approaches can be identified. In one approach, the objective is to select a site representing a key geologic unit on Mars, i.e., a unit that is widespread, easily recognized, and used frequently as a datum in various investigations. The second approach is to select a site that potentially affords access to a wide variety of rock types. Because rover range is limited, rocks from a variety of sources must be assembled in a small area for sampling. Regardless of the approach taken in site selection, the Pathfinder site should include eolian deposits and provisions should be made to obtain measurements on soils. A recommended approach for selecting the Mars Pathfinder landing site is to identify a deltaic deposit, composed of sediments derived from sources of various ages and geologic units that shows evidence of eolian activity. The site should be located as close as possible to the part of the outwash where rapid deposition occurred because the likelihood of 'sorting' by size and composition increases with distance, decreasing the probability of heterogeneity. In addition, it is recommended that field operation tests be conducted to gain experience and insight into conducting science with Pathfinder.

Greeley, Ronald↗

DOE OSTI2022

Drivers of Future Physical Water Scarcity and Its Economic Impacts in Latin America and the Caribbean

Future water scarcity is a global concern with significant impacts on the energy, water, and land (EWL) sectors. Countries in Latin America and the Caribbean (LAC) are significant producers of agricultural goods consumed around the world, so disruptions resulting from land and water scarcity in LAC are a source of global supply chain risk. Understanding where water scarcity in LAC could occur and what could drive that scarcity to emerge is critical for strategic resource management and planning, both regionally and globally. Assessing future water scarcity impacts in LAC is challenging given the multisector dynamic interactions among the EWL sectors and the multiple uncertainties acting across different spatial scales. We use scenario discovery to explore the drivers of future water scarcity, considering assumptions related to climate, socioeconomics, and the EWL sectors. Understanding what factors drive uncertainty across outcomes can help stakeholders in targeting long-term solutions and future data collection efforts. To illuminate these dynamics, we use a database consisting of a large ensemble of scenarios representing diverse worlds simulated using the Global Change Analysis Model. We quantify future water scarcity and its economic impacts across the scenarios using three metrics: (1) physical water scarcity, (2) water price, and (3) crop profit. We find that physical water scarcity and water price are driven primarily by reservoir storage capacity assumptions, highlighting the importance of strategic water infrastructure development in maintaining future water availability and accessibility. Crop profit is driven by a combination of water supply and demand assumptions, which emphasizes the complex nature of EWL multisector dynamics. While most of LAC is poised to have abundant land and water resources available for development, water basins in Mexico and along the Pacific coast of South America experience high levels of severity and uncertainty across the future scenarios for at least one of the metrics. We find that the drivers of extreme scarcity vary spatially and across the metrics, further highlighting the heterogeneity of the region and the importance of considering multiple metrics to assess water scarcity vulnerability.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Evapotranspiration partitioning estimates from 8 methods from 47 NEON sites, 2019-2021

This dataset provides daily estimates of evapotranspiration (ET) and the transpiration-to-evapotranspiration ratio (T/ET) across 47 terrestrial National Ecological Observatory Network (NEON) sites spanning diverse environmental and biome conditions in the United States across three years of data (2019-2021). Daily ET is reported in both energy units (MJ m⁻² day⁻¹) and equivalent water depth (mm day⁻¹), assuming a constant latent heat of vaporization of 2.45 MJ/kg. The primary method uses a hybrid recurrent neural network–Penman–Monteith framework (RNN-PM), which integrates physically based surface energy balance constraints with data-driven learning to partition ET into transpiration and evaporation components. Model inputs include in situ meteorological observations (air temperature, vapor pressure deficit, wind speed, and radiation) combined with satellite-derived land surface temperature, leaf area index, and soil moisture. For benchmarking and uncertainty assessment, T/ET estimates from seven additional models are included: Priestley-Taylor Jet Propulsion Laboratory (PT-JPL), Penman-Monteith (P-M), Two-Source Energy Balance (TSEB), Support Vector Regression (SVR), and Categorical Boosting (CatBoost), among others—spanning empirical, machine-learning, and process-based approaches (see methods section or linked publication for detailed descriptions). Data Package Contents: The dataset a csv files containing daily ET and T/ET estimates for each site and model, along with associated metadata files these variables. Data can be accessed using common spreadsheet software (e.g., Microsoft Excel, LibreOffice) or programming environments such as R or Python. Together, these data support cross-site comparisons of ecosystem water use, evaluation of ET partitioning methods, and development of improved land–atmosphere exchange models.

EARTH SCIENCE > ATMOSPHERE↗

DOE OSTI2024

Enriching OpenStreetMap network data for transportation applications: Insights into the impact of urban congestion on accessibility

OpenStreetMap (OSM) data is a valuable open-source resource for various transportation, traffic, and planning applications. However, OSM network data lack operating traffic speed information, which is critical for transport planning and operations. Addressing this shortcoming, this study leverages commercial vendor data (to serve as ground truth) with exogenous, open-source variables characterizing local transport infrastructure, land use, and demographic information to predict average congested traffic speeds on OSM networks. Three machine-learning models were tested and estimated for OSM links with and without speed limit information in the Denver metropolitan region. Among these, XGBoost performed best, with mean absolute errors of 3.27 and 3.62 mph for links with and without speed limits, respectively. The developed models accurately predicted traffic speeds for different hours and days of the week compared to ground truth data. Using these predicted speeds, drive accessibility scores were computed for the Denver region for different time periods using the Mobility Energy Productivity (MEP) metric to understand the impact of congestion on energy-efficient accessibility. Results show that congestion-adjusted drive accessibility can be significantly lower compared to accessibility calculated using free flow speeds. Specifically, weekday evening hours saw a 42 % drop in accessibility due to reduced speeds, particularly around downtown Denver. Across the Denver metro region, approximately half as many opportunities and jobs are accessible in under 20 min by car during the evening peak period relative to free flow conditions. These findings underscore the importance of using congestion-adjusted operating speeds rather than speed limits in accessibility calculations, as reliance on speed limits can substantially overestimate energy-efficient drive accessibility in large, car-centric cities susceptible to significant congestion. In conclusion, the methodology presented here could further enrich OSM network data, making them useful for an even broader range of transportation applications.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Soil Water Potentials (1990-2018) from a calibrated ELM-FATES, and rooting depth analyses scripts, PA-BCI, Panama

This dataset has ELM-FATES simulation outputs and scripts that accompany and reproduce the manuscript, Chitra-Tarak et al. (2021) Hydraulically-vulnerable trees survive on deep-water access during droughts in a tropical forest. New Phytologist. https: //doi.org/10.1111/nph.17464”. This dataset is organized into four parts: (1) source code for the model, Energy Exascale Earth System Land Model-Functionally Assembled Advanced Terrestrial simulator (ELM-FATES) (2) R scripts associated with ELM-FATES simulations: to prepare files for parameter ensemble simulation runs, calibrate the model with observed hydrological fluxes observed at BCI, re-run the model with tuned parameters and obtain long-term soil matric potentials by depth (3) ELM-FATES hydrological simulation outputs at PA-BCI, Panama (1990-2018, daily), and (4) R scripts for inverse rooting depth modeling and post-processing: prepare datasets and inversely model tree rooting depths at BCI, test hypotheses about the relationship of rooting depths with plant hydraulic traits and drought-induced mortality, and reproduce the manuscript itself. n.b. 1. Data needed to run R scripts in (2) and (4) and thus to reproduce the manuscript are sourced from various public datasets. Refer to the data availability section of the accompanying manuscript. 2. This dataset version (prepared on May 12, 2021) matches the revised and published version of the accompanying manuscript (published May 16, 2021) replacing a previous version. In the publication, this dataset is cited as follows: Chitra-Tarak R, Xu C, Aguilar S, Anderson-Teixeira K, Chambers J, Detto M, Faybishenko B, Fisher R, Knox R, Koven C et al. 2020. Soil water potentials (1990–2018) from a calibrated ELM-FATES, and rooting depth analyses scripts, PA-BCI, Panama. 2.0. NGEE Tropics Data Collection. doi: 10.15486/ngt/1696806

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2011

Environmental Signatures for Habitability: What to Measure and How to Rank the Habitability Potential of Mars

The environmental signatures for habitability are not necessarily biosignatures, even though on Earth, they are definitive proof of habitability. It is the constant overprint of the chemical signatures of life that makes it difficult to recognize the chemical and physical properties of a potentially habitable environment as distinct from an inhabited one. Mars Science Laboratory (MSL) will soon embark on a mission to Mars to assess its past or present habitability, so it is useful to examine how we measure habitability on Earth and prepare for how that approach may differ for Mars. This exercise includes: (a) articulation of fundamental assumptions about habitability, (b) an inventory of factors that affect habitability, (c) development of metrics, measurement approach and implementation, and (d) a new classification scheme for planetary habitability that goes beyond the binary "yes" or "no." There may be dozens of factors that affect habitability and they can be weighted as a function of specific environment. However a robotic, in situ investigation even on Earth has constraints that prevent the measurement of every environmental factor, so metrics must be reduced to the most relevant subset, given available time, cost, technical feasibility and scientific importance. Many of the factors could be measured with a combination of orbital data and the MSL payload. We propose that, at a minimum, a designation of high habitability potential requires the following conditions be met: (a) thermally stable with respect to extremes and frequency of fluctuation, (b) has more than one energy source, (c) sufficient chemical diversity to make compounds with covalent and hydrogen bonding, (d) can moderate ionizing radiation enough to allow a stable or evolving pool of organic molecules, (e) must have water or other high quality polar solvent, (f) must be able to renew chemical resources (e.g., plate tectonics, volcanism or something else we haven't envisioned). A measurement approach we have taken to measure habitability on Earth is : 1. Study remote sensing data, maps, etc. 2. Decide how big an area to measure. 3. Determine the spatial sampling rate. 4. Determine the temporal sampling rate. 5. Determine the order of measurements 6. Decide where to begin measurements 7. Select locations at field site and proceed While science drives each of the steps, there are additional constraints, e.g., technical, time, cost, safety (risk). This approach is also executable on Mars. Measurement of past habitability is more challenging both for Earth and Mars where access to the past means subsurface access and confrontation with unknowns about preservation of the martian past. Some environments preserve evidence of past habitability better than others, and this is where selection of the landing site to maximize the preservation potential of habitability indicators will be key. Mars presents an opportunity to discover transitional states between habitable or not, and we offer a ranking scale for planetary habitability with Mars as the second test subject: CLASS ONE Uninhabitable and likely has never been so CLASS TWO Has a high potential but no confirmed observation of life (as defined above) CLASS THREE Inhabited (we find life) 3-A Globally inhabited 3-B Primitive life; early in its evolution, but not yet globally established 3-C Exists only in refugia -- planet heading toward class four CLASS FOUR Post-habitable (there once was life, but now it's gone) MSL provides an opportunity to carefully investigate the habitability of at least one site on Mars and it will reveal much about the possible states of planetary habitability

Conrad, Pamela G.↗

DOE OSTI2022

Machine learning-enabled model-data integration for predicting subsurface water storage

Subsurface water storage (SWS) is a key variable of the climate system and a storage component for precipitation and radiation anomalies, inducing persistence in the climate system. It plays a critical role in climate-change projections and can mitigate the impacts of climate change on ecosystems. However, because of the difficult accessibility of the underground, hydrologic properties and dynamics of SWS are poorly known. Direct observations of SWS are limited, and accurate incorporation of SWS dynamics into Earth system land models remains challenging. We propose a machine learning-enabled model-data integration framework to improve the SWS prediction at local to conus scales in a changing climate by leveraging all the available observation and simulation resources, as well as to inform the model development and guide the observation collection. The accurate prediction will enable an optimal decision of water management and land use and improve the ecosystem's resilience to the climate change.

Lu, Dan↗

NASA NTRS2022

A Central Asia Hydrologic Monitoring Dataset for Food and Water Security Applications in Afghanistan

From the Hindu Kush mountains to the Registan Desert, Afghanistan is a diverse landscape where droughts, floods, conflict, and economic market accessibility pose challenges for agricultural livelihoods and food security. The ability to remotely monitor environmental conditions is critical to support decision making for humanitarian assistance. The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) global and Central Asia data streams provide information on hydrologic states for routine integrated food security analysis. While developed for a specific project, these data are publicly available and useful for other applications that require hydrologic estimates of the water and energy balance. These two data streams are unique because of their suitability for routine monitoring, as well as for being a historical record for computing relative indicators of water availability. The global stream is available at ∼ 1-month latency, and monthly average outputs are on a 10 km grid from 1982–present. The second data stream, Central Asia (21–56°N, 30–100°E), at ∼ 1 d latency, provides daily average outputs on a 1 km grid from 2000–present. This paper describes the configuration of the two FLDAS data streams, background on the software modeling framework, selected meteorological inputs and parameters, and results from previous evaluation studies. We also provide additional analysis of precipitation and snow cover over Afghanistan. We conclude with an example of how these data are used in integrated food security analysis. For use in new and innovative studies that will improve understanding of this region, these data are hosted by U.S. Geological Survey data portals and the National Aeronautics and Space Administration (NASA). The Central Asia data described in this paper can be accessed via the NASA repository at https://doi.org/10.5067/VQ4CD3Y9YC0R (Jacob and Slinski, 2021), and the global data described in this paper can be accessed via the NASA repository at https://doi.org/10.5067/5NHC22T9375G (McNally, 2018).

Amy McNally↗

DOE OSTI2025

Revenue-Maximizing Shared Parking and Electric Vehicle Charging Management in Multi-Unit Dwellings

In urban areas, searching for parking and electric vehicle (EV) charging can result in cruising, congestion, and environmental externalities. Recognizing the business opportunity of offering private parking and charging infrastructure access within multi-unit dwellings (MUDs) during daytime, we model a shared parking and EV charging management system. We maximize the revenue of MUD charging hubs in mixed land use, catering to public demand. Our approach accounts for the objectives of the two stakeholders involved: a demand model is fitted on the choices of EV charging users, and the supply model optimizes the allocation of parking and charging requests in an MUD parking lot. A binary integer linear programming model for the allocation of parking and charging spaces with a rolling horizon is integrated with matching rules that handle both parking and charging requests. In our numerical experiments in a neighborhood of Chicago, Illinois, we estimate the performance of the MUD parking and charging system with metrics that include revenue, number of matchings, and utilization rates. At any given time, MUDs with lower prices attract more charging requests, particularly those of longer duration, resulting in higher revenue and greater charging utilization. Dynamic pricing facilitates a more equitable distribution of requests; as MUD parking lots reach capacity and their fees increase, other MUDs become more competitive, attracting additional requests. Comparing our method against first-come-first-served and optimal-solution benchmarks, we demonstrate our model’s effectiveness in dynamically managing mixed parking and charging demand in MUD charging hubs.

electric vehicle, multi-unit dwelling, charging in↗

NASA NTRS1981

LANDSAT data for coastal zone management

The lack of adequate, current data on land and water surface conditions in New Jersey led to the search for better data collections and analysis techniques. Four-channel MSS data of Cape May County and access to the OSER computer interpretation system were provided by NASA. The spectral resolution of the data was tested and a surface cover map was produced by going through the steps of supervised classification. Topics covered include classification; change detection and improvement of spectral and spatial resolution; merging LANDSAT and map data; and potential applications for New Jersey.

Mckenzie, S.↗

NASA NTRS1984

Information systems - Issues in global habitability

The present investigation is concerned with fundamental issues, related to information considerations, which arise in an interdisciplinary approach to questions of global habitability. Information system problems and issues are illustrated with the aid of an example involving biochemical cycling and biochemical productivity. The estimation of net primary production (NPP) as an important consideration in the overall global habitability issue is discussed. The NPP model requires three types of data, related to meteorological information, a land surface inventory, and the vegetation structure. Approaches for obtaining and processing these data are discussed. Attention is given to user requirements, information system requirements, workstations, network communications, hardware/software access, and data management.

Norman, S. D.↗

NASA NTRS1996

The Pathfinder Microrover

An exciting scientific component of the Pathfinder mission is the rover, which will act as a mini-field geologist by providing us with access to samples for chemical analyses and close-up images of the Martian surface, performing active experiments to modify the surface and study the results, and exploring the landing site area.

Mars Mars Pathfinder rover microrover imager for p↗

NASA NTRS2004

Autonomous Space Shuttle

The continued assembly and operation of the International Space Station (ISS) is the cornerstone within NASA's overall Strategic P an. As indicated in NASA's Integrated Space Transportation Plan (ISTP), the International Space Station requires Shuttle to fly through at least the middle of the next decade to complete assembly of the Station, provide crew transport, and to provide heavy lift up and down mass capability. The ISTP reflects a tight coupling among the Station, Shuttle, and OSP programs to support our Nation's space goal . While the Shuttle is a critical component of this ISTP, there is a new emphasis for the need to achieve greater efficiency and safety in transporting crews to and from the Space Station. This need is being addressed through the Orbital Space Plane (OSP) Program. However, the OSP is being designed to "complement" the Shuttle as the primary means for crew transfer, and will not replace all the Shuttle's capabilities. The unique heavy lift capabilities of the Space Shuttle is essential for both ISS, as well as other potential missions extending beyond low Earth orbit. One concept under discussion to better fulfill this role of a heavy lift carrier, is the transformation of the Shuttle to an "un-piloted" autonomous system. This concept would eliminate the loss of crew risk, while providing a substantial increase in payload to orbit capability. Using the guidelines reflected in the NASA ISTP, the autonomous Shuttle a simplified concept of operations can be described as; "a re-supply of cargo to the ISS through the use of an un-piloted Shuttle vehicle from launch through landing". Although this is the primary mission profile, the other major consideration in developing an autonomous Shuttle is maintaining a crew transportation capability to ISS as an assured human access to space capability.

Siders, Jeffrey A.↗

NASA NTRSDate not supplied

On the Application of an Actuator Line Model for Rotorcraft Outwash Predictions

As the next generation of Vertical Take-Off and Landing (VTOL) vehicles develops and the VTOL industry makes significant progress in utilizing them for public transportation, it is crucial to have validated and cost-effective computational models accessible to both industry professionals and academics in the rotorcraft community. This study investigates the implementation of a new actuator line model within NASA’s OVERFLOW computational fluid dynamics (CFD) solver, with a focus on predicting rotorcraft outwash. This reduced-order rotor model was developed to provide reasonably accurate outwash predictions while reducing computational costs compared to traditional blade-resolved CFD simulations. To validate the actuator line model for outwash predictions, the model was compared against existing experimental data across three validation cases: single rotor Out-of-Ground Effect (OGE), single rotor In-Ground Effect (IGE), and tandem rotor IGE. All three cases were based on the rotor geometry and configuration of the CH-47D. To evaluate the feasibility and advantages of using an actuator line model for outwash predictions the results were compared to a series of high-fidelity blade-resolved CFD simulations as well as comprehensive analyses simulations based on CHARM predictions. The results of this study demonstrate the feasibility of leveraging this new actuator line model for reasonably accurate outwash predictions. The outwash results obtained from the actuator line model closely matched those from high-fidelity blade-resolved simulations while significantly reducing the overall computational costs of the simulations. Results from this study further demonstrated the feasibility of using CHARM for efficient, and reasonably accurate time-averaged outwash predictions for multi-rotor configurations.

CFD↗

NASA NTRSDate not supplied

The Evolution of NASA’s Land, Atmosphere Near real-time Capability for Earth Observing Systems (LANCE) to Support an Increasingly Diverse Range of User Needs

NASA’s Land, Atmosphere Near Real-Time Capability for Earth Observing Systems (LANCE) supports a host of near real-time (NRT) monitoring applications from air quality to wildfires to flooding to droughts to severe storms. LANCE distributes 40-75 TB of data per week from 13 instruments to a wide range of users in over 200 countries. Most of the data and imagery served through LANCE are available within three hours of satellite overpass. Leveraging the existing NASA infrastructure and science teams has made LANCE unique in its ability to provide data from instruments onboard Earth observing satellites rapidly, accurately, and consistently. LANCE continually ensures these data are findable, readily accessible, and freely available. The evolution of LANCE is ongoing, as it strives to enable researchers and applications users to quickly incorporate the latest satellite data into their work, leading to more timely and accurate understanding of the Earth’s land and atmosphere.

Jenny Hewson↗

NASA NTRSDate not supplied

The Evolution of NASA's Land, Atmosphere Near Real-Time Capability for EOS (LANCE) to Support an Increasingly Diverse Range of User Needs

Jenny Hewson↗