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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 181 records · Page 10
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NASA NTRSDate not supplied

Keweenaw Bay Water Resources: Turbidity Data Decision Support for Shoreline Assessment and Management in Lake Superior's Keweenaw Bay

Community Concerns: Keweenaw Bay is vulnerable to flooding and erosion, which threaten wetland restoration projects that provide a critical habitat for wildlife and fish. Erosion and flooding threaten the loss of public beaches and shoreline, coastal infrastructure, and impacts to coastal highways, which has driven shoreline armoring projects. Our goals were to first, produce a seasonal turbidity analysis to understand the sediment redistribution patterns in the bay during snow melt and rainy seasons to provide insight into coastal sediment dynamics. KBIC has completed an Environmental Protection Agency (EPA) - funded project titled "Long-Term Coastal Dynamic Collaborative for Tribal Resiliency" which has thus far produced detailed shoreline maps of the area. The NASA DEVELOP project is taking place in between phase 1 and 2 of the EPA-funded project.

Khaim Syed-Raza↗
NASA NTRS2022

Keweenaw Bay Water Resources: Turbidity Data Decision Support for Shoreline Assessment and Management in Lake Superior'S Keweenaw Bay

The Keweenaw Bay Indian Community (KBIC) has shoreline along the south of Lake Superior that is contaminated with copper stamp sands from legacy mining. The stamp sands have been capped with sandy-loam soils and restored native species, but erosion and flooding threaten to re-deposit these stamp sands onto wetlands and into the bay. Erosion and flooding also threaten loss of beaches and shoreline, infrastructure, wetland restoration projects, and coastal highways, which has driven shoreline armoring. However, while shoreline armoring can be effective in protecting the intended areas, it can also exacerbate erosion in nearby unarmored areas, so its net impact on the shoreline is yet to be quantified. The DEVELOP team partnered with KBIC and the Environmental Protection Agency (EPA) to utilize imagery from Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) in order to analyze turbidity proxies. The results from these analyses showed that seasonal variation in study area was greatest during the season dominated by rain, but spatial variability across our study period did not show a clear trend. These results will be used to better inform future shoreline management efforts and support resilience in the face of more coastal erosion.

remote sensing↗
NASA NTRS1973

ERTS-1 Virgin Islands experiment 589: Determine boundaries of ERTS and aircraft data within which useful water quality information can be obtained

The author has identified the following significant results. The boundaries of application of ERTS-1 and aircraft data are established for St. Thomas harbor within which useful water quality information can be obtained. In situ physical, chemical, and biological water quality and benthic data were collected. Moored current meters were employed. Optical measurements of solar irradiance, color test panel radiance and water absorption were taken. Procedures for correlating in situ optical, biological, and chemical data with underflight aircraft I2S data and ERTS-1 MSS scanner data are presented. Comparison of bulk and precision CCT computer printout data for this application is made, and a simple method for geometrically locating bulk data individual pixels based on land-water interface is described. ERTS spacecraft data and I2S aircraft imagery are correlated with optical in situ measurements of the harbor water, with the aircraft green photographic and ERTS-1 MSS-4 bands being the most useful. The biological pigments correlate inversely with the optical data for inshore areas and directly further seaward. Automated computer data processing facilitated analysis.

Coulbourn, W. C.↗
NASA NTRS1985

Distribution of Snow and Maximum Snow Water Equivalent Obtained by LANDSAT Data and Degree Day Method

Maximum snow water equivalence and snowcover distribution are estimated using several LANDSAT data taken in snowmelting season over a four year period. The test site is Okutadami-gawa Basin located in the central position of Tohoku-Kanto-Chubu District. The year to year normalization for snowmelt volume computation on the snow line is conducted by year to year correction of degree days using the snowcover percentage within the test basin obtained from LANDSAT data. The maximum snow water equivalent map in the test basin is generated based on the normalized snowmelt volume on the snow line extracted from four LANDSAT data taken in a different year. The snowcover distribution on an arbitrary day in snowmelting of 1982 is estimated from the maximum snow water equivalent map. The estimated snowcover is compared with the snowcover area extracted from NOAA-AVHRR data taken on the same day. The applicability of the snow estimation using LANDSAT data is discussed.

Takeda, K.↗
NASA NTRS1999

Inter-Annual Variability of Atmospheric Water Vapor as seen from the TOVS Pathfinder Path a Data Set

The atmospheric water vapor is a major greenhouse gas and plays a critical role in determining energy and water cycle in the climate system. A new, global, long-term (1985-98) water vapor data set derived from the TIROS Operational Vertical Sounder (TOVS) Path A system will be introduced in the presentation. An assessment of the accuracy of the TOVS Path A water vapor data will he presented. The focus of this oral presentation will be on the inter-annual variability of the water vapor distribution in the atmosphere. Also, water vapor distribution observed during 1997/98 ENSO event will be shown.

Mehta, Amita↗
NASA NTRS1976

Evaluation of surface water resources from machine-processing of ERTS multispectral data

The surface water resources of a large metropolitan area, Marion County (Indianapolis), Indiana, are studied in order to assess the potential value of ERTS spectral analysis to water resources problems. The results of the research indicate that all surface water bodies over 0.5 ha were identified accurately from ERTS multispectral analysis. Five distinct classes of water were identified and correlated with parameters which included: degree of water siltiness; depth of water; presence of macro and micro biotic forms in the water; and presence of various chemical concentrations in the water. The machine processing of ERTS spectral data used alone or in conjunction with conventional sources of hydrological information can lead to the monitoring of area of surface water bodies; estimated volume of selected surface water bodies; differences in degree of silt and clay suspended in water and degree of water eutrophication related to chemical concentrations.

Mausel, P. W.↗
NASA NTRS2013

Data Assimilation of AIRS Water Vapor Profiles: Impact on Precipitation Forecasts for Atmospheric River Cases Affecting the Western of the United States

Atmospheric rivers are transient, narrow regions in the atmosphere responsible for the transport of large amounts of water vapor. These phenomena can have a large impact on precipitation. In particular, they can be responsible for intense rain events on the western coast of North America during the winter season. This paper focuses on attempts to improve forecasts of heavy precipitation events in the Western US due to atmospheric rivers. Profiles of water vapor derived from from Atmospheric Infrared Sounder (AIRS) observations are combined with GFS forecasts by a three-dimensional variational data assimilation in the Gridpoint Statistical Interpolation (GSI). Weather Research and Forecasting (WRF) forecasts initialized from the combined field are compared to forecasts initialized from the GFS forecast only for 3 test cases in the winter of 2011. Results will be presented showing the impact of the AIRS profile data on water vapor and temperature fields, and on the resultant precipitation forecasts.

Blankenship, Clay↗
NASA NTRS2015

Evaluation of Radar Vegetation Indices for Vegetation Water Content Estimation Using Data from a Ground-Based SMAP Simulator

Vegetation water content (VWC) is an important component of microwave soil moisture retrieval algorithms. This paper aims to estimate VWC using L band active and passive radar/radiometer datasets obtained from a NASA ground-based Soil Moisture Active Passive (SMAP) simulator known as ComRAD (Combined Radar/Radiometer). Several approaches to derive vegetation information from radar and radiometer data such as HH, HV, VV, Microwave Polarization Difference Index (MPDI), HH/VV ratio, HV/(HH+VV), HV/(HH+HV+VV) and Radar Vegetation Index (RVI) are tested for VWC estimation through a generalized linear model (GLM). The overall analysis indicates that HV radar backscattering could be used for VWC content estimation with highest performance followed by HH, VV, MPDI, RVI, and other ratios.

soil moisture↗
NASA NTRS2004

An Investigation of the Correlation of Water-Ice and Dust Retrievals Via the MGS TES Data Set

Water-ice in the Martian atmosphere was first identified in the Mariner 9 Infrared Interferometer Spectrometer (IRIS) spectra. The Viking Imaging Subsystem (VIS) instruments aboard the Viking orbiter also observed water-ice clouds and hazes in the Martian atmosphere. The MGS TES instrument is an infrared inferometer/spectrometer which covers the spectral range 6-50 micron with a selectable sampling resolution of either 5 or 10 per cm. Using the relatively independent and distinct spectral signatures for dust and water-ice, these two retrieved quantities have been retrieved simultaneously. Although the interrelations among the two quantities have been analyzed by Smith et al. and the retrievals are thought to be robust, understanding the impact of each quantity on the other during their retrievals as well as the impact from the surface for retrievals is important for correctly interpreting the science, and therefore requires close examination. An understanding of the correlation or a-correlation between dust and water-ice would aid in understanding the physical processes responsible for the transport of aerosols in the Martian atmosphere. In this presentation, we present an investigation of the correlation between water-ice and dust in the MGS TES data set.

Qu, Z.↗
NASA NTRS1994

Near-global survey of effective droplet radii in liquid water clouds using ISCCP data

A global survey of cloud particle size variations can provide crucial constraints on how cloud processes determine cloud liquid water contents and their variation with temperature, and further, may indicate the magnitude of aerosol effects on clouds. A method, based on a complete radiative transfer model for Advanced Very High Resolution Radiometer (AVHRR)-measured radiances, is described for retrieving cloud particle radii in liquid water clouds from satellite data currently available from the International Satellite Cloud Climatology Project. Results of sensitivity tests and validation studies provide error estimates. AVHRR data from NOAA-9 and NOAA-10 have been analyzed for January, April, July and October in 1987 and 1988. The results of this first survey reveal systematic continental and maritime differences and hemispheric contrasts that are indicative of the effects of associated aerosol concentration differences: cloud droplet radii in continental water clouds are about 2-3 micrometers smaller than in marine clouds, and droplet radii are about 1 micrometer smaller in marine clouds of the Northern Hemisphere than in the Southern Hemisphere. The height dependencies of cloud droplet radii in continental and marine clouds are also consistent with differences in the vertical profiles of aerosol concentration. Significant seasonal and diurnal variations of effective droplet radii are also observed, particularly at lower latitudes. Variations of the relationship between cloud optical thickness and droplet radii may indicate variations in cloud microphysical regimes.

Han, Qingyan↗
NASA NTRS2021

Data Assimilation of Terrestrial Water Storage Observations to Estimate Precipitation Fluxes: A Synthetic Experiment

The Gravity Recovery and Climate Experiment (GRACE) mission and its Follow-On (GRACE-FO) mission provide unprecedented observations of terrestrial water storage (TWS) dynamics at basin to continental scales. Established GRACE data assimilation techniques directly adjust the simulated water storage components to improve the estimation of groundwater, streamflow, and snow water equivalent. Such techniques artificially add/subtract water to/from prognostic variables, thus upsetting the simulated water balance. To overcome this limitation, we propose and test an alternative assimilation scheme in which precipitation fluxes are adjusted to achieve the desired changes in simulated TWS. Using a synthetic data assimilation experiment, we show that the scheme improves performance skill in precipitation estimates in general, but that it is more robust for snowfall than for rainfall, and it fails in certain regions with strong horizontal gradients in precipitation. The results demonstrate that assimilation of TWS observations can help correct (adjust) the model’s precipitation forcing and, in turn, enhance model estimates of TWS, snow mass, soil moisture, runoff, and evaporation. A key limitation of the approach is the assumption that all errors in TWS originate from errors in precipitation. Nevertheless, the proposed approach produces more consistent improvements in simulated runoff than the established GRACE data assimilation techniques.

Data Assimilation↗