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At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Bioaccumulation Factor for Radium in Rio Grande Biota

The Los Alamos National Laboratory (LANL or Laboratory) Environmental Protection and Compliance Division’s Environmental Stewardship Group (EPC-ES) performs the radiological analysis portion of biota dose assessments using the computer program RESRAD-Biota (DOE 2004, DOE 2020). RESRAD-Biota uses a default bioaccumulation factor, B iv , for radium in aquatic animals. This bioaccumulation factor, also listed in Department of Energy (DOE) Standard, DOE-STD-1153-2019, is the ratio of a radionuclide concentration in fresh weight biota to the environment in which it resides, which for aquatic animals is water. DOE-STD-1153-2019 offers conservative values for these ratios that are specific to the radiological contaminant, the biota, and the environment. The B iv in RESRAD-Biota can be used for general screening (Level 1 screening). If the factor proves to be too conservative, then a site-specific B iv may be used. The biota dose assessment of fish from the northern New Mexico region of the Rio Grande fail the general screen, which indicates that further analysis is necessary. Level 2 analysis is site-specific but still has conservative limitations. Level 3 analysis is a more realistic and representative, sitespecific analysis. The purpose of this paper is to use data specific to the Rio Grande to calculate a new aquatic animal radium B iv . This B iv can then be used for Level 3 analysis of aquatic animals specifically in the northern New Mexico region of the Rio Grande. The calculated radium B iv is a useful value for performing detailed, accurate biota dose assessments and calculating dose to fish using RESRAD-Biota when only sediment data are available.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Large Vessel Activity and Low-Frequency Underwater Sound Benchmarks in United States Waters

Chronic low-frequency noise from commercial shipping is a worldwide threat to marine animals that rely on sound for essential life functions. Although the U.S. National Oceanic and Atmospheric Administration recognizes the potential negative impacts of shipping noise in marine environments, there are currently no standard metrics to monitor and quantify shipping noise in U.S. marine waters. However, one-third octave band acoustic measurements centered at 63 and 125 Hz are used as international (European Union Marine Strategy Framework Directive) indicators for underwater ambient noise levels driven by shipping activity. We apply these metrics to passive acoustic monitoring data collected over 20 months in 2016–2017 at five dispersed sites throughout the U.S. Exclusive Economic Zone: Alaskan Arctic, Hawaii, Gulf of Mexico, Northeast Canyons and Seamounts Marine National Monument (Northwest Atlantic), and Cordell Bank National Marine Sanctuary (Northeast Pacific). To verify the relationship between shipping activity and underwater sound levels, vessel movement data from the Automatic Identification System (AIS) were paired to each passive acoustic monitoring site. Daily average sound levels were consistently near to or higher than 100 dB re 1 μPa in both the 63 and 125 Hz one-third octave bands at sites with high levels of shipping traffic (Gulf of Mexico, Northeast Canyons and Seamounts, and Cordell Bank). Where cargo vessels were less common (the Arctic and Hawaii), daily average sound levels were comparatively lower. Specifically, sound levels were ~20 dB lower year-round in Hawaii and ~10-20 dB lower in the Alaskan Arctic, depending on the season. Although these band-level measurements can only generally facilitate differentiation of sound sources, these results demonstrate that international acoustic indicators of commercial shipping can be applied to data collected in U.S. waters as a unified metric to approximate the influence of shipping as a driver of ambient noise levels, provide critical information to managers and policy makers about the status of marine environments, and to identify places and times for more detailed investigation regarding environmental impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Calculating Methane Emissions from Offshore Facilities Using Bottom-Up Methods

With changing demands in regulation, understanding methane emissions from offshore oil and gas production infrastructure has become increasingly important. Reported emissions from facilities in the Gulf of Mexico range from zero to thousands of tons of methane per hour, but these is currently no clear understanding of how this range compares to expected emissions from normally operating facilities. To generate realistic emission estimates, we create two bottom-up models that simulate emissions from facilities operating in the Gulf of Mexico. We estimate type 1 prototypical facilities (typically unmanned, older, lower-producing platforms in shallow water with little processing equipment, compressors, or storage tanks) to emit an average of 13 kg CH 4 h −1 , which corresponds to a loss of 2.7% of the average facility production. Type 2 prototypical facilities (continuously manned, higher production and operate in deeper water with processing equipment, oil storage tanks, compressors and power generation) emit an average of 88 kg CH 4 h −1 , which corresponds to a loss of 2.5% of production. The average measured emission from type 1 facilities was 18 kg CH 4 h −1 with a median production loss estimated at 8%. The average measured emission from type 2 facilities was 36 kg CH 4 h −1 with a median production loss estimated at 2.4%. Using emission factors that consider the long-tail emission distribution partly reconciles the difference between modelled and measured emission estimates, but we suggest the current the fugitive emission estimate may be an underestimate and more data on the number and size of fugitive emissions could explain differences between the modelled and measured emission estimate. We suggest the bottom-up approach described here that uses production data coupled with facility equipment could be used to identify facilities that have abnormally large measured emissions, caused by methodological failure or larger than expected fugitive emissions, which should be targeted for further evaluation resulting in remeasurement or identification of source type so that a more accurate estimates can be made on the absolute emission.

bottom-up↗
DOE OSTI2023

Structuring Nutrient Yields throughout Mississippi/Atchafalaya River Basin Using Machine Learning Approaches

To minimize the eutrophication pressure along the Gulf of Mexico or reduce the size of the hypoxic zone in the Gulf of Mexico, it is important to understand the underlying temporal and spatial variations and correlations in excess nutrient loads, which are strongly associated with the formation of hypoxia. This study’s objective was to reveal and visualize structures in high-dimensional datasets of nutrient yield distributions throughout the Mississippi/Atchafalaya River Basin (MARB). For this purpose, the annual mean nutrient concentrations were collected from thirty-three US Geological Survey (USGS) water stations scattered in the upper and lower MARB from 1996 to 2020. Eight surface water quality indicators were selected to make comparisons among water stations along the MARB over the past two decades. Principal component analysis (PCA) was used to comprehensively evaluate the nutrient yields across thirty-three USGS monitoring stations and identify the major contributing nutrient loads. The results showed that all samples could be analyzed using two main components, which accounted for 81.6% of the total variance. The PCA results showed that yields of orthophosphate (OP), silica (SI), nitrate–nitrites (NO 3 -NO 2 ), and total suspended sediment (TSS) are major contributors to nutrient yields. It also showed that land-planted crops, density of population, domestic and industrial discharges, and precipitation are fundamental causes of excess nutrient loads in MARB. These factors are of great significance for the excess nutrient load management and pollution control of the Mississippi River. It was found that the average nutrient yields were stable within the sub-MARB area, but the large nitrogen yields in the upper MARB and the large phosphorus yields in the lower MARB were of great concern. t-distributed stochastic neighbor embedding (t-SNE) revealed interesting nonlinear and local structures in nutrient yield distributions. Clustering analysis (CA) showed the detailed development of similarities in the nutrient yield distribution. Moreover, PCA, t-SNE, and CA showed consistent clustering results. This study demonstrated that the integration of dimension reduction techniques, PCA, and t-SNE with CA techniques in machine learning are effective tools for the visualization of the structures of the correlations in high-dimensional datasets of nutrient yields and provide a comprehensive understanding of the correlations in the distributions of nutrient loads across the MARB.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Implications of Sea Breeze Circulations on boundary layer aerosols in the southern coastal Texas region

The Sea Breeze Circulation (SBC) influences atmospheric processes at multiple scales in coastal regions. Understanding how SBCs impact the aerosol number budget and aerosol–cloud interaction processes is essential. This study investigates sea breeze–aerosol interactions (SAIs) during 46 summertime SBC events using data from the TRacking Aerosol Convection Interactions Experiment (TRACER) field campaign across urban (main) and rural (supplemental) coastal sites in southern Texas. Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) simulations complement observations to explore spatio-temporal meteorological controls on boundary layer aerosols. During the summertime, Sea Breeze Fronts (SBF) penetrating inland transported cool, moist air over the land, introducing air masses with distinct properties compared to the preexisting continental air. These SAIs cause variability in number concentrations of up to a factor of two, with events typically lasting ∼ 5 h before returning to background conditions. SAI impact on aerosols varies with site proximity to water and the preceding sea breeze (SB) history, primarily affecting the marine-influenced accumulation mode. The main site, influenced by both Galveston Bay and the Gulf of Mexico, reflects a stronger marine influence. In contrast, a supplemental site, at a similar shoreline distance but exposed only to the Gulf of Mexico and typically upstream of the urban core, samples SB air that has traversed land and partially regained continental characteristics. Simulations show that the regional SAIs extend ∼ 50 km inland and reach up to the boundary layer height. SAIs further decrease cloud condensation nuclei relevant aerosol number concentrations in ∼ 20 % of events during SBF passage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

HarDWR - Harmonized Water Rights Records

A dataset within the Harmonized Database of Western U.S. Water Rights (HarDWR). For a detailed description of the database, please see the meta-record v2.0. Changelog v2.0 - Recalculated based on data sourced from WestDAAT - Changed using a Site ID column to identify unique records to using aa combination of Site ID and Allocation ID - Removed the Water Management Area (WMA) column from the harmonized records. The replacement is a separate file which stores the relationship between allocations and WMAs. This allows for allocations to contribute to water right amounts to multiple WMAs during the subsequent cumulative process. - Added a column describing a water rights legal status - Added "Unspecified" was a water source category - Added an acre-foot (AF) column - Added a column for the classification of the right's owner v1.02 - Added a .RData file to the dataset as a convenience for anyone exploring our code. This is an internal file, and the one referenced in analysis scripts as the data objects are already in R data objects. v1.01 - Updated the names of each file with an ID number less than 3 digits to include leading 0s v1.0 - Initial public release Description Here we present an updated database of Western U.S. water right records. This database provides consistent unique identifiers for each water right record, and a consistent categorization scheme that puts each water right record into one of seven broad use categories. These data were instrumental in conducting a study of the multi-sector dynamics of inter-sectoral water allocation changes though water markets (Grogan et al., *in review*). Specifically, the data were formatted for use as input to a process-based hydrologic model, Water Balance Model (WBM), with a water rights module (Grogan et al., *in review*). While this specific study motivated the development of the database presented here, water management in the U.S. West is a rich area of study (e.g., Anderson and Woosly, 2005; Tidwell, 2014; Null and Prudencio, 2016; Carney et al., 2021) so releasing this database publicly with documentation and usage notes will enable other researchers to do further work on water management in the U.S. West. We produced the water rights database presented here in four main steps: (1) data collection, (2) data quality control, (3) data harmonization, and (4) generation of cumulative water rights curves. Each of steps (1)-(3) had to be completed in order to produce (4), the final product that was used in the modeling exercise in Grogan et al. (*in review*). All data in each step is associated with a spatial unit called a Water Management Area (WMA), which is the unit of water right administration utilized by the state in which the right came from. Steps (2) and (3) required use to make assumptions and interpretation, and to remove records from the raw data collection. We describe each of these assumptions and interpretations below so that other researchers can choose to implement alternative assumptions an interpretation as fits their research aims. Motivation for Changing Data Sources The most significant change has been a switch from collecting the raw water rights directly from each state to using the water rights records presented in WestDAAT, a product of the Water Data Exchange (WaDE) Program under the Western States Water Council (WSWC). One of the main reasons for this is that each state of interest is a member of the WSWC, meaning that WaDE is partially funded by these states, as well as many universities. As WestDAAT is also a database with consistent categorization, it has allowed us to spend less time on data collection and quality control and more time on answering research questions. This has included records from water right sources we had previously not known about when creating v1.0 of this database. The only major downside to utilizing the WestDAAT records as our raw data is that further updates are tied to when WestDAAT is updated, as some states update their public water right records daily. However, as our focus is on cumulative water amounts at the regional scale, it is unlikely most records updates would have a significant effect on our results. The structure of WestDAAT led to several important changes to how HarWR is formatted. The most significant change is that WaDE has calculated a field known as `SiteUUID`, which is a unique identifier for the Point of Diversion (POD), or where the water is drawn from. This separate from `AllocationNativeID`, which is the identifier for the allocation of water, or the amount of water associated with the water right. It should be noted that it is possible for a single site to have multiple allocations associated with it and for an allocation to be able to be extracted from multiple sites. The site-allocation structure has allowed us to adapt a more consistent, and hopefully more realistic, approach in organizing the water right records than we had with HarDWR v1.0. This was incredibly helpful as the raw data from many states had multiple water uses within a single field within a single row of their raw data, and it was not always clear if the first water use was the most important, or simply first alphabetically. WestDAAT has already addressed this data quality issue. Furthermore, with v1.0, when there were multiple records with the same water right ID, we selected the largest volume or flow amount and disregarded the rest. As WestDAAT was already a common structure for disparate data formats, we were better able to identify sites with multiple allocations and, perhaps more importantly, allocations with multiple sites. This is particularly helpful when an allocation has sites which cross WMA boundaries, instead of just assigning the full water amount to a single WMA we are now able to divide the amount of water between the number of relevant WMAs. As it is now possible to identify allocations with water used in multiple WMAs, it is no longer practical to store this information within a single column. Instead the stAllocationToWMATab.csv file was created, which is an allocation by WMA matrix containing the percent Place of Use area overlap with each WMA. We then use this percentage to divide the allocation's flow amount between the given WMAs during the cumulation process to hopefully provide more realistic totals of water use in each area. However, not every state provides areas of water use, so like HarDWR v1.0, a hierarchical decision tree was used to assign each allocation to a WMA. First, if a WMA could be identified based on the allocation ID, then that WMA was used; typically, when available, this applied to the entire state and no further steps were needed. Second was the spatial analysis of Place of Use to WMAs. Third was a spatial analysis of the POD locations to WMAs, with the assumption that allocation's POD is within the WMA it should belong to; if an allocation still had multiple WMAs based on its POD locations, then the allocation's flow amount would be divided equally between all WMAs. The fourth, and final, process was to include water allocations which spatially fell outside of the state WMA boundaries. This could be due to several reasons, such as coordinate errors / imprecision in the POD location, imprecision in the WMA boundaries, or rights attached with features, such as a reservoir, which crosses state boundaries. To include these records, we decided for any POD which was within one kilometer of the state's edge would be assigned to the nearest WMA. Other Changes WestDAAT has Allowed In addition to a more nuanced and consistent method of assigning water right's data to WMAs, there are other benefits gained from using the WestDAAT dataset. Among those is a consistent categorization of a water right's legal status. In HarDWR v1.0, legal status was effectively ignored, which led to many valid concerns about the quality of the database related to the amounts of water the rights allowed to be claimed. The main issue was that rights with legal status' such as "application withdrawn", "non-active", or "cancelled" were included within HarDWR v1.0. These, and other water rights status' which were deemed to not be in use have been removed from this version of the database. Another major change has been the addition of the "unspecified water source category. This is water that can come from either surface water or groundwater, or the source of which is unknown. The addition of this source category brings the total number of categories to three. Due to reviewer feedback, we decided to add the acre-foot (AF) column so that the data may be more applicable to a wider audience. We added the ownerClassification column so that the data may be more applicable to a wider audience. File Descriptions The dataset is a series of various files organized by state sub-directories. In addition, each file begins with the state's name, in case the file is separate from its sub-directory for some reason. After the state name is the text which describes the contents of the file. Here is each file described in detail. Note that st is a placeholder for the state's name. stFullRecords_HarmonizedRights.csv: A file of the complete water records for each state. The column headers for each of this type of file are: state - The name of the state to which the allocations belong to. FIPS - The two digit numeric state ID code. siteID - The site location ID for POD locations. A site may have multiple allocations, which are the actual amount of water which can be drawn. In a simplified hypothetical, a farm stead may have an allocation for "irrigation" and an allocation for "domestic" water use, but the water is drawn from the same pumping equipment. It should be noted that many of the site ID appear to have been added by WaDE, and therefore may not be recognized by a given state's water rights database. allocationID - The allocation ID for the water right. For most states this is the water right ID, and what is recommended to use should a right be looked up on a given state's water rights database. The water amounts associated with these IDs tend to be finer scaled than those associated with siteID. It should be noted that some allocations may be extracted from multiple sites, particularly for larger Places of Use. ownerClassification - A classification of the types of owners for water rights. The most common is `Private` which incorporates a wide range of entities. Several classifications would be grouped into a government category, most of which are for the U.S. Federal Government. These allocations could be listed as "Federal", "United States of America", or as the names of any number of federal agencies. The last major grouping of entities is for "Native American"s. priorityDate - The date we use as the water right priority date for our modeling analysis. This is the legal priority date when it is available. However, for some rights, specifically from California and New Mexico, we used a pseudo priority date (e.g. well completion date or start of well drilling date) when a legal priority date was not available. The most questionable dates come from New Mexico, where the only date associated with certain water right records was the date the allocation was recorded in the database. As the allocation record creation tended to be within a few months of the filing of the application of the water right, from manually double checking the water rights, and our analysis focuses on aggregating water rights on the timescale of years, we determined it was acceptable to use such dates to include as many records as possible. primaryBeneficialUse - From the numerous state water use categories, WaDE categorized them into 21 categories WestDAAT. This column is the original WaDE category for the primary water use at the PoD site. allocationBeneficialUse - From the numerous state water use categories, WaDE categorized them into 21 categories for WestDAAT. This column is the original WaDE category

Economics↗
DOE OSTI2020

HarDWR - Water Management Area (WMA) Shapefiles

A dataset within the Harmonized Database of Western U.S. Water Rights (HarDWR). For a detailed description of the database, please see the meta-record v2.0. Changelog v2.0 - No changes v1.0 - Initial public release Description Borders of all Water Management Areas (WMAs) across the 11 western-most states of the coterminous United States are available filtered through a single source. The legal name for this set of boundaries varies state-by-state. The data is provided as two compressed shapefiles. One, stateWMAs, contains data for all 11 states. For 10 of those states, Arizona being the exception, the polygons represent the legal management boundaries used by those states to manage their surface and groundwater resources respectively. WMAs refer to the set of boundaries a particular state uses to manage its water resources. Each set of boundaries was collected from the states individually, and then merged into one spatial layer. The merging process included renaming some columns to enable merging with all other source layers, as well as removing columns deemed not required for followup analysis. The retained columns for each boundary are: basinNum - the state provided unique numerical ID; basinName - the state provided English name of the area, where applicable; state - the state name; and uniID - a unique identifier we created by concatenating the state name, and underscore, and the state numerical ID. Arizona is unique within this collection of states in that surface and groundwater resources are managed using two separate sets of boundaries. During our followup analysis (Grogan et al., in review) we decided to focus on one set of boundaries, those for surface water. This is due to the recommendation of our hydrologists that the surface water boundary set is a more realist representation of how water moves across the landscape, as a few of the groundwater boundaries are based on political and/or economic considerations. Therefore, the Arizona surface WMAs are included within stateWMAs. The Arizona groundwater WMAs are provided as a separate file, azGroundWMAs, as a companion to the first file for completeness and general reference. WMA spatial boundary data sources by state: Arizona: Arizona Surface Water Watersheds; Collected February, 2020; https://gisdata2016-11-18t150447874z-azwater.opendata.arcgis.com/datasets/surface-watershed/explore?location=34.158174%2C-111.970823%2C7.50 Arizona: Arizona Ground Water Basins; Collected February, 2020; https://gisdata2016-11-18t150447874z-azwater.opendata.arcgis.com/datasets/groundwater-basin-2/explore?location=34.158174%2C-111.970823%2C7.50 California: California CalWater 2.2.1; Collected February, 2020; https://www.mlml.calstate.edu/mpsl-mlml/data-center/data-entry-tools/data-tools/gis-shapefile-layers/ Colorado: Colorado Water District Boundaries; Collected February, 2020; https://www.colorado.gov/pacific/cdss/gis-data-category Idaho: Idaho Department of Water Resources (IDWR) Administrative Basins; Collected November, 2015; https://data-idwr.opendata.arcgis.com/datasets/fb0df7d688a04074bad92ca8ef74cc26_4/explore?location=45.018686%2C-113.862284%2C6.93 Montana: Collected June, 2019; Directly contacted Montana Department of Natural Resources and Conservation (DNRC) Office of Information Technology (OIT) Nevada: Nevada State Engineer Admin Basin Boundaries; Collected April, 2020 https://ndwr.maps.arcgis.com/apps/mapviewer/index.html?layers=1364d0c3a0284fa1bcd90f952b2b9f1c New Mexico: New Mexico Office of the State Engineer (OSE) Declared Groundwater Basins; Collected April, 2020 https://geospatialdata-ose.opendata.arcgis.com/datasets/ose-declared-groundwater-basins/explore?location=34.179783%2C-105.996542%2C7.51 Oregon: Oregon Water Resources Department (OWRD) Administrative Basins; Collected February, 2020; https://www.oregon.gov/OWRD/access_Data/Pages/Data.aspx Utah: Utah Adjudication Books; Collected April, 2020; https://opendata.gis.utah.gov/datasets/utahDNR::utah-adjudication-books/explore?location=39.497165%2C-111.587782%2C-1.00 Washington: Washington Water Resource Inventory Areas (WRIA); Collected June, 2017; https://ecology.wa.gov/Research-Data/Data-resources/Geographic-Information-Systems-GIS/Data Wyoming: Wyoming State Engineer's Office Board of Control Water Districts; Collected June, 2019; Directly contacted Wyoming State Engineer's Office

Economics↗
NASA NTRS1970

Geologic interpretation of Apollo 6 stereophotography from Baja California to west Texas

Excellent space photography of parts of the southwestern United States and northwestern Mexico was obtained during the unmanned Apollo 6 spaceflight. Two features of this photography made it useful for geologic interpretations: its vertical stereocoverage and its exposure under a relatively low angle of solar illumination through an unusually cloud-free and clear atmosphere. The structural patterns, which were topographically enhanced by the longer shadows, were annotated on the photographs, in order to analyze their trends with respect to the continental tectonic framework, and to attempt to correlate the pattern with known copper or other base metal deposits. The annotated fracture patterns showed the regional trends and their distribution. The area studied was a 100- to 105-mile swath of terrain covering a total land area of approximately 60,000 square statute miles. The coverage began from a point centered on Punta Colnett on the Pacific coast of Baja California and extended to the Sacramento Mountains of New Mexico and west Texas.

Gawarecki, S. J.↗
NASA NTRS1972

Flow direction determination of lava flows.

The flow direction technique, previously applied to ash-flow sheets, can be used to determine direction of movement and locate eruptive centers for lava flows. The method provides statistically stronger and more consistent flow direction data for lava than ash-flow tuff. The accuracy and reliability of the technique was established on the porphyritic basaltic andesite of Mount Taylor, New Mexico, which erupted from a known center, the Mount Taylor Amphitheater. The technique was then applied to volcanic units with unknown sources: the John Kerr Peak Quartz Latite and mid-Tertiary andesite flows in the Mogollon Mountains, both in southwestern New Mexico. The flow direction technique indicated flow patterns and suggested source areas for each rock unit. In the Mogollon Mountains flow direction measurements were supported by independent directional criteria such as dips of cross beds, stratigraphic thickening, facies changes, and megascopic textures.-

Smith, E. I.↗
NASA NTRS1973

Conjugate photoelectron excitation of O I 4368 airglow emission.

Conjugate photoelectron fluxes are shown to be mainly responsible for the excitation of O I 4368 radiation observed with a grille spectrometer located at Langmuir Laboratory, New Mexico (L equals 1.7). On the basis of observations of O I 7774 and 4368 tropical emissions at Agulhas, Negras, Brazil (L equals 1.1), the contribution of radiative recombination to the New Mexico intensity is shown to be small. Conjugate photoelectron excitation at the Brazilian site has not been observed.

Christensen, A. B.↗
NASA NTRS1974

Preliminary analysis of Skylab RADSCAT results over the ocean

Preliminary observations at 13.9 GHz of the radar backscatter and microwave emission from the sea were analyzed using data obtained by the radiometer scatterometer on Skylab. Results indicate approximately a square-law relationship between differential scattering coefficient and windspeed at angles of 40 deg to 50 deg, after correction for directional effect, over a range from about 4 up to about 25 meters/sec. The brightness temperature response was also observed, and considerable success was achieved in correcting it for atmospheric attenuation and emission. Measurements were made in June, 1973, over Hurricane Ava off the west coast of Mexico and over relatively calm conditions in the Gulf of Mexico and Caribbean Sea.

Moore, R. K.↗
NASA NTRS1976

Twilight helium 10,830-A calculations and observations

The 10,830-A column emission rates from metastable He(2 3S) atoms in the upper atmosphere are calculated and compared with other calculations and observations. It is shown that the calculated emission rates agree well with the observed emission rates provided that the cross sections for loss of He(2 3S) by Penning ionization on atomic oxygen is near 5 A 2 and not 44 A 2 as previously measured. It is assumed that the local and conjugate photoelectron fluxes above 300 km are about double those calculated. The dawn/dusk intensity ratio observed in New Mexico, Brazil and Peru is accounted for by changes in the ambient thermal electron concentration in Brazil and Peru and by changes in plasmasphere opacity to conjugate point electrons for New Mexico and to some extent for Brazil. The rate at which He-4 would escape from the earth if sufficient energy were imparted to the He-4 atoms during the Penning reaction was recalculated and found to be inadequate by an order of magnitude to balance the terrestrial production.

Tinsley, B. A.↗
NASA NTRS1976

Screwworm Eradication Data System (SEDS) applications program documentation, part 2

National Oceanic and Atmospheric Administration (NOAA) satellite data, which is in analog tape form, is converted into output data products. These products are used by investigators in determining optimum areas for airdrops of steril screwworm flies over Mexico. The output product of SEDS takes the form of imagery data film slides showing Mexico and the lower southwest portion of the United States. The area analog preprocessing phase of the SEDS task is covered.

Source record↗
NASA NTRS1977

An atlas of 1975 GEOS-3 radar altimeter data for hurricane/tropical disturbance studies, volume 1

Geographic locations of 1975 hurricanes and other tropical disturbances were correlated with the closest approaching orbits of the GEOS-3 satellite and its radar altimeter. The disturbance locations and altimeter data were gathered for a seven-month period beginning with GEOS-3 launch in mid-April 1975. Areas of coverage were the Atlantic Ocean, the Carribean, the Gulf of Mexico, the west coast of the continental United States, and the central and western Pacific Ocean. Volume 1 contains disturbance coverage data for the Atlantic Ocean, Gulf of Mexico, and Eastern Pacific Ocean. Central and Western Pacific coverage is documented in Volume II.

Stanley, H. R.↗
NASA NTRS1978

Use of satellite images in the evaluation of farmlands

Remote sensing techniques in the evaluation of farmland in Mexico are discussed. Electronic analysis techniques and photointerpretation techniques are analyzed. Characteristics of the basic crops in Mexico as related to remote sensing are described.

Lozano H., A. E.↗
NASA NTRS1982

Helical screw expander evaluation project

A one MW helical rotary screw expander power system for electric power generation from geothermal brine was evaluated. The technology explored in the testing is simple, potentially very efficient, and ideally suited to wellhead installations in moderate to high enthalpy, liquid dominated field. A functional one MW geothermal electric power plant that featured a helical screw expander was produced and then tested with a demonstrated average performance of approximately 45% machine efficiency over a wide range of test conditions in noncondensing, operation on two-phase geothermal fluids. The Project also produced a computer equipped data system, an instrumentation and control van, and a 1000 kW variable load bank, all integrated into a test array designed for operation at a variety of remote test sites. Data are presented for the Utah testing and for the noncondensing phases of the testing in Mexico. Test time logged was 437 hours during the Utah tests and 1101 hours during the Mexico tests.

Mckay, R.↗
NASA NTRS1981

Rankine engine solar power generation. I - Performance and economic analysis

Results of a computer simulation of the performance of a solar flat plate collector powered electrical generation system are presented. The simulation was configured to include locations in New Mexico, North Dakota, Tennessee, and Massachusetts, and considered a water-based heat-transfer fluid collector system with storage. The collectors also powered a Rankine-cycle boiler filled with a low temperature working fluid. The generator was considered to be run only when excess solar heat and full storage would otherwise require heat purging through the collectors. All power was directed into the utility grid. The solar powered generator unit addition was found to be dependent on site location and collector area, and reduced the effective solar cost with collector areas greater than 400-670 sq m. The sites were economically ranked, best to worst: New Mexico, North Dakota, Massachusetts, and Tennessee.

Gossler, A. A.↗
NASA NTRS1982

Methods of editing cloud and atmospheric layer affected pixels from satellite data

Practical methods of computer screening cloud-contaminated pixels from data of various satellite systems are proposed. Examples are given of the location of clouds and representative landscape features in HCMM spectral space of reflectance (VIS) vs emission (IR). Methods of screening out cloud affected HCMM are discussed. The character of subvisible absorbing-emitting atmospheric layers (subvisible cirrus or SCi) in HCMM data is considered and radiosonde soundings are examined in relation to the presence of SCi. The statistical characteristics of multispectral meteorological satellite data in clear and SCi affected areas are discussed. Examples in TIROS-N and NOAA-7 data from several states and Mexico are presented. The VIS-IR cluster screening method for removing clouds is applied to a 262, 144 pixel HCMM scene from south Texas and northeast Mexico. The SCi that remain after cluster screening are sited out by applying a statistically determined IR limit.

Nixon, P. R.↗