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At least 199 records · Page 11

Results of the Collaborative Energy and Water Cycle Information Services (CEWIS) Workshop on Heterogeneous Dataset Analysis Preparation

In support of the NASA Energy and Water Cycle Study (NEWS), the Collaborative Energy and Water Cycle Information Services (CEWIS), sponsored by NEWS Program Manager Jared Entin, was initiated to develop an evolving set of community-based data and information services that would facilitate users to locate, access, and bring together multiple distributed heterogeneous energy and water cycle datasets. The CEWIS workshop, June 15-16, 2010, at NASA/GSFC, was the initial step of the process, starting with identifying and scoping the issues, as defined by the community.

Kempler, Steven↗

The SeaFlux Turbulent Flux Dataset Version 1.0 Documentation

Under the auspices of the World Climate Research Programme (WCRP) Global Energy and Water cycle EXperiment (GEWEX) Data and Assessment Panel (GDAP), the SeaFlux Project was created to investigate producing a high-resolution satellite-based dataset of surface turbulent fluxes over the global oceans. The most current release of the SeaFlux product is Version 1.0; this represents the initial release of turbulent surface heat fluxes, associated near-surface variables including a diurnally varying sea surface temperature.

Clayson, Carol Anne↗

Dust Optical Properties Over North Africa and Arabian Peninsula Derived from the AERONET Dataset

Dust optical properties over North Africa and the Arabian Peninsula are extracted from the quality assured multi-year datasets obtained at 14 sites of the Aerosol Robotic Network (AERONET). We select the data with (a) large aerosol optical depth (AOD >= 0.4 at 440 nm) and (b) small Angstrom exponent (A(sub ext)<= 0.2) for retaining high accuracy and reducing interference of non-dust aerosols. The result indicates that the major fraction of high aerosol optical depth days are dominated by dust over these sites even though it varies depending on location and time. We have found that the annual mean and standard deviation of single scattering albedo, asymmetry parameter, real refractive index, and imaginary refractive index for Saharan and Arabian desert dust is 0.944 +/- 0.005, 0.752 +/- 0.014, 1.498 +/- 0.032, and 0.0024 +/- 0.0034 at 550 nm wavelength, respectively. Dust aerosol selected by this method is less absorbing than the previously reported values over these sites. The weaker absorption of dust from this study is consistent with the studies using remote sensing techniques from satellite. These results can help to constrain uncertainties in estimating global dust shortwave radiative forcing.

Kim, D.↗

Recent Upgrades to NASA SPoRT Initialization Datasets for the Environmental Modeling System

The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed several products for its NOAA/National Weather Service (NWS) partners that can initialize specific fields for local model runs within the NOAA/NWS Science and Training Resource Center Environmental Modeling System (EMS). The suite of SPoRT products for use in the EMS consists of a Sea Surface Temperature (SST) composite that includes a Lake Surface Temperature (LST) analysis over the Great Lakes, a Great Lakes sea-ice extent within the SST composite, a real-time Green Vegetation Fraction (GVF) composite, and NASA Land Information System (LIS) gridded output. This paper and companion poster describe each dataset and provide recent upgrades made to the SST, Great Lakes LST, GVF composites, and the real-time LIS runs.

Case, Jonathan L.↗

Public Health Applications of Remotely-sensed Environmental Datasets for the Conterminous United States

NASA Marshall Space Flight Center is collaborating with the University of Alabama at Birmingham (UAB) School of Public Health and the Centers for Disease Control and Prevention (CDC) National Center for Public Health Informatics to address issues of environmental health and enhance public health decision-making using NASA remotely-sensed data and products. The objectives of this study are to develop high-quality spatial data sets of environmental variables, link these with public health data from a national cohort study, and deliver the linked data sets and associated analyses to local, state and federal end-user groups. Three daily environmental data sets were developed for the conterminous U.S. on different spatial resolutions for the period 2003-2008: (1) spatial surfaces of estimated fine particulate matter (PM2.5) exposures on a 10-km grid using the US Environmental Protection Agency (EPA) ground observations and NASA's MODerate-resolution Imaging Spectroradiometer (MODIS) data; (2) a 1-km grid of Land Surface Temperature (LST) using MODIS data; and (3) a 12-km grid of daily Incoming Solar Radiation (Insolation) and heat-related products using the North American Land Data Assimilation System (NLDAS) forcing data. These environmental data sets were linked with public health data from the UAB REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether exposures to these environmental risk factors are related to cognitive decline, stroke and other health outcomes. These environmental datasets and the results of the public health linkage analyses will be disseminated to end-users for decision-making through the CDC Wide-ranging Online Data for Epidemiologic Research (WONDER) system and through peer-reviewed publications respectively. The linkage of these data with the CDC WONDER system substantially expands public access to NASA data, making their use by a wide range of decision makers feasible. By successful completion of this research, decision-making activities, including policy-making and clinical decision-making, can be positively affected through utilization of the data products and analyses provided on the CDC WONDER system.

Al-Hamdan, Mohammad↗

Long Hole Film Cooling Dataset for CFD Development : Infrared Thermography and Thermocouple Surveys - Part 1

An experiment investigating flow and heat transfer of long (length to diameter ratio of 18) cylindrical film cooling holes has been completed. In this paper, the thermal field in the flow and on the surface of the film cooled flat plate is presented for nominal freestream turbulence intensities of 1.5 and 8 percent. The holes are inclined at 30deg above the downstream direction, injecting chilled air of density ratio 1.0 onto the surface of a flat plate. The diameter of the hole is 0.75 in. (0.01905 m) with center to center spacing (pitch) of 3 hole diameters. Coolant was injected into the mainstream flow at nominal blowing ratios of 0.5, 1.0, 1.5, and 2.0. The Reynolds number of the freestream was approximately 11,000 based on hole diameter. Thermocouple surveys were used to characterize the thermal field. Infrared thermography was used to determine the adiabatic film effectiveness on the plate. Hotwire anemometry was used to provide flowfield physics and turbulence measurements. The results are compared to existing data in the literature. The aim of this work is to produce a benchmark dataset for Computational Fluid Dynamics (CFD) development to eliminate the effects of hole length to diameter ratio and to improve resolution in the near-hole region. In this report, a Time-Filtered Navier Stokes (TFNS), also known as Partially Resolved Navier Stokes (PRNS), method that was implemented in the Glenn-HT code is used to model coolant-mainstream interaction. This method is a high fidelity unsteady method that aims to represent large scale flow features and mixing more accurately.

Shyam, Vikram↗

Long Hole Film Cooling Dataset for CFD Development - Flow and Film Effectiveness

An experiment investigating flow and heat transfer of long (length to diameter ratio of 18) cylindrical film cooling holes has been completed. In this paper, the thermal field in the flow and on the surface of the film cooled flat plate is presented for nominal freestream turbulence intensities of 1.5 and 8 percent. The holes are inclined at 30 deg above the downstream direction, injecting chilled air of density ratio 1.0 onto the surface of a flat plate. The diameter of the hole is 0.75 in. (approx. 0.02 m) with center to center spacing (pitch) of 3 hole diameters. Coolant was injected into the mainstream flow at nominal blowing ratios of 0.5, 1.0, 1.5, and 2.0. The Reynolds number of the freestream was approximately 11,000 based on hole diameter. Thermocouple surveys were used to characterize the thermal field. Infrared thermography was used to determine the adiabatic film effectiveness on the plate. Hotwire anemometry was used to provide flowfield physics and turbulence measurements. The results are compared to existing data in the literature. The aim of this work is to produce a benchmark dataset for Computational Fluid Dynamics (CFD) development to eliminate the effects of hole length to diameter ratio and to improve resolution in the near-hole region. In this report, a Time Filtered Navier Stokes (TFNS), also known as Partially Resolved Navier Stokes (PRNS), method that was implemented in the Glenn-HT code is used to model coolant-mainstream interaction. This method is a high fidelity unsteady method that aims to represent large scale flow features and mixing more accurately.

Shyam, Vikram↗

Incorporating the TRMM Dataset into the GPM Mission Data Suite

In June 2015 the TRMM satellite came to its end. The 17 plus year of mission data that it provided has proven a valuable asset to a variety of science communities. This 17plus year data set does not, however, stagnate with the end of the mission itself. NASA/JAXA intend to integrate the TRMM data set into the data suite of the GPM mission. This will ensure the creation of a consistent, intercalibrated, accurate dataset within GPM that extends back to November of 1998. This paper describes the plans for incorporating the TRMM 17plus year data into the GPM data suite. These plans call for using GPM algorithms for both radiometer and radar to reprocess TRMM data as well as intercalibrating partner radiometers using GPM intercalibration techniques. This reprocessing will mean changes in content, logical format and physical format as well as improved geolocation, sensor corrections and retrieval techniques.

TRMM↗

Comparison Of Downscaled CMIP5 Precipitation Datasets For Projecting Changes In Extreme Precipitation In The San Francisco Bay Area.

Water resource managers planning for the adaptation to future events of extreme precipitation now have access to high resolution downscaled daily projections derived from statistical bias correction and constructed analogs. We also show that along the Pacific Coast the Northern Oscillation Index (NOI) is a reliable predictor of storm likelihood, and therefore a predictor of seasonal precipitation totals and likelihood of extremely intense precipitation. Such time series can be used to project intensity duration curves into the future or input into stormwater models. However, few climate projection studies have explored the impact of the type of downscaling method used on the range and uncertainty of predictions for local flood protection studies. Here we present a study of the future climate flood risk at NASA Ames Research Center, located in South Bay Area, by comparing the range of predictions in extreme precipitation events calculated from three sets of time series downscaled from CMIP5 data: 1) the Bias Correction Constructed Analogs method dataset downscaled to a 1/8 degree grid (12km); 2) the Bias Correction Spatial Disaggregation method downscaled to a 1km grid; 3) a statistical model of extreme daily precipitation events and projected NOI from CMIP5 models. In addition, predicted years of extreme precipitation are used to estimate the risk of overtopping of the retention pond located on the site through simulations of the EPA SWMM hydrologic model. Preliminary results indicate that the intensity of extreme precipitation events is expected to increase and flood the NASA Ames retention pond. The results from these estimations will assist flood protection managers in planning for infrastructure adaptations.

Storm↗

Climate Impact and GIS Education Using Realistic Applications of Data.gov Thematic Datasets in a Structured Lesson-Based Workbook

This project created a workbook which teaches Earth Science to undergraduate and graduate students through guided in-class activities and take-home assignments organized around climate topics which use GIS to teach key geospatial analysis techniques and cartography skills. The workbook is structured to the White House's Data.gov climate change themes, which include Coastal Flooding, Ecosystem Vulnerability, Energy Infrastructure, Arctic, Food Resilience, Human Health, Transportation, Tribal Nations, and Water. Each theme provides access to framing questions, associated data, interactive tools, and further reading (e.g. The US Climate Resilience Toolkit and National Climate Assessment). Lessons make use of the respective theme's available resources. The structured thematic approach is designed to encourage independent exploration. The goal is to teach climate concepts and concerns, GIS techniques and approaches, and effective cartographic representation and communication results; and foster a greater awareness of publicly available resources and datasets. To reach more audiences more effectively, a two level approach was used. Level 1 serves as an introductory study and relies on only freely available interactive tools to reach audiences with fewer resources and less familiarity. Level 2 presents a more advanced case study, and focuses on supporting common commercially available tool use and real-world analysis techniques.

Satellite Imagery↗

NASA Crew Earth Observations: A Long-Term Dataset for Earth System Research, Applications, and Education

Polar orbit; Sun-synchronous - designed for long term repeatability of data, Typically nadir viewing, crosses every point on Earth ~ 12-14 days near local solar noon/local midnight, Landsat series collecting data since 1972, Pointing capability, satellite constellations. Inclined Equatorial Orbit: ISS; Sun-asynchronous - similar illumination 3-4 days every 90 days, Nadir to highly oblique imagery possible from hand-held cameras, WORF, external sensors, Provides opportunity to collect unique datasets for scientific study, disaster response, Data is complementary to polar-orbiting satellite data.

Stefanov, William L.↗

Deriving Three Dimensional Reservoir Bathymetry from Multi-Satellite Datasets

We evaluate different techniques that rebuild reservoir bathymetry by combining multi-satellite imagery of surface water elevation and extent. Digital elevation models (DEMs) are processed in two distinct ways in order to determine 3-D reservoir bathymetry. They are defined as (a) linear extrapolation and (b) linear interpolation. The first one linearly extrapolates the land slope, defining the bottom as the intersection of all extrapolated lines. The second linearly interpolates the uppermost and lowermost pixels of the reservoir's main river, repeating the process for all other tributaries. A visible bathymetry, resulting from the combination of radar altimetry and water extent masks, can be coupled with the DEM, improving the accuracy of techniques (a) and (b). Envisat-and Altika-based altimetric time series is combined to a Landsat-based water extent database over the2002-2016 period in order to generate the visible bathymetry, and topography is derived from the 3-arcsecHydroSHEDS DEM. Fourteen 3-D bathymetries derived from the combination of these techniques and datasets, plus the inclusion of upstream and downstream riverbed elevations, are evaluated over Lake Mead. Accuracy is measured using ground observations, and show that metrics improve as a function of added data requirement and processing. Best bathymetry estimates are obtained when the visible bathymetry, linear extrapolation technique and riverbed elevation are combined. Water storage variability is also evaluated and shows that best results are derived from the aforementioned combination. This study contributes to our understanding and representation of reservoir water impoundment impacts on the hydrological cycle.

HydroSHEDS↗

Ground and Satellite Based Observation Datasets for the Lower Mekong River Basin

In ‘Satellite observations and modeling to understand the Lower Mekong River Basin streamflow variability’ [1] hydrological fluxes, meteorological variables, land cover land use maps, and soil characteristics and parameters data were compiled and processed for the Lower Mekong River Basin. In this work, daily streamflow time series data at nine gauges located at five different countries in the Mekong region (Thailand, Laos People׳s Democratic Republic (PDR), Myanmar, Cambodia, and Viet Nam) is presented. Satellite-based daily precipitation and air temperature (minimum & maximum) data is processed and provided over the entire basin as part of the dataset provided in this work. Moreover, land cover land use raster data that contains 18 classes that cover agriculture, urban, range and forests land cover land use classes for the basin is offered. In addition, a soil data that contains physical and chemical characteristics needed by physically based hydrological models to simulate the cycling of water and air is also provided.

Streamflow↗

Central Valley Water Resources: Improving California Groundwater Assessments using GRACE and InSAR Datasets for Water Resource Management

California’s Central Valley is one of the most productive agricultural areas in the world, producing approximately $20 billion in crops annually. The recent California droughts of 2007-2010 and 2012-2019 resulted in increased groundwater pumping in the Central Valley to adequately irrigate farmland. Overdrafting of the Central Valley aquifer results in groundwater depletion, land subsidence, and permanent loss of groundwater storage. In 2014, depletion of groundwater led the state of California to enact the Sustainable Groundwater Management Act (SGMA),requiring critically overdrafted, high, and medium priority sub-basins to reach sustainable levels of groundwater pumping and recharge by 2042. SGMA allows local Groundwater Sustainability Agencies (GSAs) the authority to create Groundwater Sustainability Plans (GSPs) at the sub-basin level. To assist California’s Department of Water Resources (DWR), this project quantified groundwater change and land subsidence in Central Valley sub-basins with sparse or unreliable well and Geographic Positioning Systems (GPS) data. This was done using NASA’s Gravity Recovery and Climate Experiment (GRACE), GRACE FollowOn (GRACE-FO), and interferograms derived from Sentinel-1 C-band Synthetic Aperture Radar (C-SAR) and Advanced Land Observing Satellite 2 (ALOS-2)Phased Array L-band Synthetic Aperture Radar 2 (PALSAR-2). Time series of the GRACE and InSAR data were compared with well and GPS data in data-dense sub-basins to determine the feasibility of these datasets for groundwater storage and subsidence monitoring. We found that GRACE and InSAR data are effective tools for determining groundwater change and land subsidence and can be used on their own to monitor sub-basins in the absence of well and GPS data

Water Resources↗

Central Valley Water Resources: Improving California Groundwater Assessments using GRACE and InSAR Datasets for Water Resource Management

California’s Central Valley is one of the most productive agricultural areas in the world, producing approximately $20 billion in crops annually. The recent California droughts of 2007-2010 and 2011-2017 resulted in increased groundwater pumping in the Central Valley to adequately irrigate farmland. Overdrafting of the Central Valley aquifer results in groundwater depletion, land subsidence, and permanent loss of groundwater storage. In 2014, depletion of groundwater led the state of California to enact the Sustainable Groundwater Management Act (SGMA), requiring critically overdrafted, high, and medium priority sub-basins to reach sustainable levels of groundwater pumping and recharge by 2042. SGMA allows local Groundwater Sustainability Agencies the authority to create Groundwater Sustainability Plans at the sub-basin level. To assist California’s Department of Water Resources, this project quantified groundwater change and land subsidence in Central Valley sub-basins with sparse or unreliable well and GPS data. This was done using NASA’s Gravity Recovery and Climate Experiment (GRACE), GRACE Follow-On (GRACE-FO), and interferograms derived from Sentinel-1 C-band Synthetic Aperture Radar (C-SAR) and Advanced Land Observing Satellite 2 (ALOS-2) Phased Array L-band Synthetic Aperture Radar 2 (PALSAR-2). Time series of the GRACE and InSAR data were compared with well and GPS data in data-dense sub-basins to determine the feasibility of these datasets for groundwater storage and subsidence monitoring. We found thatGRACE and InSAR data are effective tools for determining groundwater change and land subsidence and can be used on their own to monitor sub-basins in the absence of well and GPS data.

Water Resources↗

Creating a knowledge graph to connect scientific publications and datasets for improving discovery of GES DISC’s data and services

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) archives and distributes to the public hundreds of Earth Science data collections. These collections are used in research, resulting in thousands of scientific papers published each year. As new users come to GES DISC for the data, it is important for them to understand how these data were used in the prior research. For this we are creating the Knowledge Graph that connects research paper citation and the data collection metadata. The relationships created in the graph have potential for the Web applications that utilize this information to directly connect the paper research to the GES DISC datasets and services. We will demonstrate these relationships using the Web application prototype.

Nathaniel Ross Crosby↗

Synthesizing Disparate LiDAR and Satellite Datasets through Deep Learning to Generate Wall-to-Wall Regional Inventories for the Complex, Mixed-Species Forests of the Eastern United States

Light detection and ranging (LiDAR) has become a commonly-used tool for generating remotely-sensed forest inventories. However, LiDAR-derived forest inventories have remained uncommon at a regional scale due to varying parameters among LiDAR data acquisitions and the availability of sufficient calibration data. Here, we present a model using a 3-D convolutional neural network (CNN), a form of deep learning capable of scanning a LiDAR point cloud, combined with coincident satellite data (spectral, phenology, and disturbance history). We compared this approach to traditional modeling used for making forest predictions from LiDAR data (height metrics and random forest) and found that the CNN had consistently lower uncertainty. We then applied the CNN to public data over six New England states in the USA, generating maps of 14 forest attributes at a 10 m resolution over 85% of the region. Aboveground biomass estimates produced a root mean square error of 36 Mg ha−1 (44%) and were within the 97.5% confidence of independent county-level estimates for 33 of 38 or 86.8% of the counties examined. CNN predictions for stem density and percentage of conifer attributes were moderately successful, while predictions for detailed species groupings were less successful. The approach shows promise for improving the prediction of forest attributes from regional LiDAR data and for combining disparate LiDAR datasets into a common framework for large-scale estimation.

Elias Ayrey↗

Quantifying radiation quality for space relevant radiation types: Fitting excess risk models to three combined HZE-irradiated mouse datasets

Radiation health risks are predominantly derived from low linear energy transfer (LET) terrestrial exposures; however, space radiation includes exposure to high-LET and high-charge, high-energy (HZE) particles. Accurately quantifying the differences in radiation quality between the space and terrestrial radiation environments is important for assessing and predicting health risks for astronauts. Weil et al. 2009 and 2014 used two different inbred mouse strains to study differences in hepatocellular carcinoma (HCC) tumorigenesis after exposures to low- and high- LET radiation. More recently, Edmundson et al. 2020 provided valuable new tumor data in outbred mice that were exposed to low- and high-LET radiation. The present study aims to rigorously investigate a relative biological effectiveness (RBE) factor by leveraging the HCC tumor data from Weil et al. 2009, Weil et al. 2014, and Edmundson et al. 2020. The three experiments were similarly designed, allowing the raw data to be combined into a pooled dataset to estimate excess relative risk (ERR) and excess absolute risk (EAR) models using Bayesian Poisson regression. These effect estimates from the pooled data provide greater power to calculate a data driven RBE. Extensive sensitivity analyses test the robustness of RBE estimates to various model assumptions. The following questions will be explored through the sensitivity analyses: • Is the shape of the dose response different for low-LET radiation and HZE radiation, indicating that RBE is a function of dose? • Does attained age modify the effect estimates differently for low-LET radiation and HZE radiation, indicating RBE is a function of attained age? • Are the effect estimates and RBE estimates different for inbred mouse strains and outbred mouse strains? • Do assumptions about differences in ERR models and EAR models change the estimated RBE? Additional studies would be needed to validate the findings from these exploratory analyses.

Lori J. Chappell↗