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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Forecasting Lake-Effect Precipitation in the Great Lakes Region Using NASA Enhanced-Satellite Data

Lake-effect precipitation is common in the Great Lakes region, particularly during the late fall and winter. The synoptic processes of lake-effect precipitation are well understood by operational forecasters, but individual forecast events still present a challenge. Locally run, high resolution models can assist the forecaster in identifying the onset and duration of precipitation, but model results are sensitive to initial conditions, particularly the assumed surface temperature of the Great Lakes. The NASA Short-term Prediction Research and Transition (SPoRT) Center has created a Great Lakes Surface Temperature (GLST) composite, which uses infrared estimates of water temperatures obtained from the MODIS instrument aboard the Aqua and Terra satellites, other coarser resolution infrared data when MODIS is not available, and ice cover maps produced by the NOAA Great Lakes Environmental Research Lab (GLERL). This product has been implemented into the Weather Research and Forecast (WRF) model Environmental Modeling System (WRF-EMS), used within forecast offices to run local, high resolution forecasts. The sensitivity of the model forecast to the GLST product was analyzed with a case study of the Lake Effect Storm Echinacea, which produced 10 to 12 inches of snowfall downwind of Lake Erie, and 8 to 18 inches downwind of Lake Ontario from 27-29 January 2010. This research compares a forecast using the default Great Lakes surface temperatures from the Real Time Global sea surface temperature (RTG SST), in the WRF-EMS model to the enhanced NASA SPoRT GLST product to study forecast impacts. Results from this case study show that the SPoRT GLST contained less ice cover over Lake Erie and generally cooler water temperatures over Lakes Erie and Ontario. Latent and sensible heat fluxes over Lake Ontario were decreased in the GLST product. The GLST product decreased the quantitative precipitation forecast (QPF), which can be correlated to the decrease in temperatures and heat fluxes. A slight increase in precipitation coverage was noted over Lake Erie due to a decrease in ice cover. Both the RTG SST and the GLST products predicted the precipitation south of the actual location of precipitation. This single case study is the first part of an examination to determine how MODIS data can be applied to improve model forecasts in the Great Lakes region.

Cipullo, Michelle↗
NASA NTRS2020

Great Lakes Water Resources II: A Google Earth Engine Tool to Automate Wetland Mapping Using Optical and Radar Satellite Sensors in the Great Lakes Basin for Wetland Management and Monitoring

The Great Lakes Basin is one of the world’s largest freshwater ecosystems. The Basin harbors over 200,000 acres of wetlands. These wetlands provide a variety of environmental, ecological, and recreational functions to over 30 million people in the region. Some of these functions include improving water quality, mitigating flood impacts, providing wildlife habitat, and housing recreational activities. However, due to anthropogenic activities, habitat conversion and degradation threaten to disrupt or destroy remaining wetland ecosystems. Maps of wetland distribution based on ground surveys are costly and labor-intensive, prohibiting timely evaluations of wetland loss and gain. The Great Lakes Water Resources II team at the NASA Jet Propulsion Laboratory developed the Wetlands Extent Tool 2.0 (WET 2.0) in Google Earth Engine to automate mapping of wetland distribution in the Great Lakes Basin. The team partnered with the US Fish and Wildlife Service (USFWS), Environmental Protection Agency (EPA), Minnesota Department of Natural Resources (MDNR), the National Oceanic and Atmospheric Administration (NOAA), and Ducks Unlimited (DU). WET 2.0 incorporates Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), and Sentinel-2 Multispectral Instrument (MSI) satellite data. WET 2.0 is trained to classify anywhere in the Great Lakes Basin. Utilizing a Random Forest classifier, WET 2.0 is capable of automatically mapping wetland extent in the entire Great Lakes Basin, achieving a mean overall accuracy of 80.12% when tested in Michigan. Findings and maps produced in WET 2.0 will enable our partners to identify areas of ecosystem degradation and wetland destruction in order to enact environmental practices and policy initiatives to maintain environmental and economic health in the area.

Water Resources↗
NASA NTRS2020

Great Lakes Water Resources II: A Google Earth Engine Tool to Automate Wetland Extent Mapping Using Optical and Radar Satellite Sensors in the Great Lakes Basin for Wetland Management and Monitoring

The Great Lakes Basin is one of the world’s largest freshwater ecosystems. The Basin harbors over 200,000 acres of wetlands that provide a variety of environmental, ecological, and recreational functions to over 30 million people in the region. Some of these functions include improving water quality, mitigating flood impacts, providing wildlife habitat, and housing recreational activities. However, due to anthropogenic activities, habitat conversion and degradation threaten to disrupt or destroy remaining wetland ecosystems. Maps of wetland distribution based on ground surveys are costly and labor-intensive, prohibiting timely evaluations of wetland loss and gain. The Great Lakes Water Resources II team at the NASA Jet Propulsion Laboratory developed the Wetlands Extent Tool 2.0 (WET 2.0) in Google Earth Engine to automate mapping of wetland distribution in the Great Lakes Basin. The team partnered with the US Fish and Wildlife Service, Environmental Protection Agency, Minnesota Department of Natural Resources, the National Oceanic and Atmospheric Administration, and Ducks Unlimited. WET 2.0 incorporates Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), and Sentinel-2 Multispectral Instrument (MSI) data. Utilizing a Random Forest classifier, WET 2.0 is capable of automatically mapping wetland extent in the entire Great Lakes Basin, achieving a mean overall accuracy of 80.12% when tested in Michigan.Findings and maps produced by WET 2.0 will enable our partners to identify areas of ecosystem degradation and wetland destruction in order to enact environmental practices and policy initiatives to maintain environmental and economic health in the area.

Water Resources↗
NASA NTRS2011

Forecasting Lake-Effect Snow in the Great Lakes Using NASA Satllite Data

This slide presentation reviews the forecast of the lake effect snow in the Great Lakes region using models and infrared estimates of Great Lake Surface Temperatures (GLSTs) from the MModerate Resolution Imaging Spectroradiometer (MODIS) instrument on Terra and Aqua satellites, and other satellite data. This study analyzes Lake Erie and Lake Ontario which produce storm total snowfall ranged from 8-18 inches off of Lake Ontario and 10-12 inches off of Lake Erie for the areas downwind.

Cipullo, Michelle↗
NASA NTRS1994

Variation of Great Lakes Water Levels Derived from Geosat Altimetry

A technique for using satellite radar altimetry data to estimate the temporal variation of the water level in moderate to large lakes and enclosed seas is described. Great Lakes data from the first 2 years of the U.S. Navy's Geosat Exact Repeat Mission (November 1986 to November 1988), for which there is an improved orbit, are used to demonstrate the technique. The Geosat results are compared to the lake level data collected by the Great Lakes Section, National Ocean Service, National Oceanic and Atmospheric Administration, and are found to reproduce the temporal variations of the five major lakes with Root-Mean-Square error (RMS) ranging from 9.4 to 13.8 cm and a combined average of 11.1 cm. Geosat data are also analyzed for Lake St. Clair, representing a moderate-sized lake, with a resulting rms of 17.0 cm. During this study period, the water level in the Great Lakes varied in a typical annual cycle of about 0.2 m (0.5 in for Lake Ontario) superimposed on a general decline of approximately 0.5 m. The altimeter data reproduced the general decline reasonably well for all the lakes, but the annual cycle was obscured in some lakes due to systematic errors in the altimeter data. Current and future altimetry missions will have markedly improved accuracy which will permit many moderate (25 km diameter) or larger lakes or enclosed seas to be routinely monitored.

Morris, Charles S.↗
NASA NTRS1975

Application of LANDSAT to the surveillance and control of lake eutrophication in the Great Lakes Basin

The author has identified the following significant results. Preliminary results in Saginaw Bay show that processed LANDSAT data provides a synoptic view of turbidity and circulation patterns that no degree of ground monitoring can provide. Processed imagery was produced to show nine discrete categories of turbidity, as indicated by nine Secchi depths between 0.3 and 3.3 meters. Analysis of lakes near Madison, Wisconsin show that inland lake water can be categorized by LANDSAT as clear, tannin, algal, and red clay. LANDSAT's capability to inventory watershed land use was throughly demonstrated in the Ohio-Kentucky-Indiana regional planning area. Computer tabulations providing area covered by each of 16 land use categories were rapidly and economically produced for each of the 225 watersheds and nine counties.

Rogers, R. H.↗
NASA NTRS1975

Application of LANDSAT to the surveillance and control of lake eutrophication in the Great Lakes basin

The author has identified the following significant results. By use of distilled water samples in the laboratory, and very clear lakes in the field, a technique was developed where the atmosphere and surface noise effects on LANDSAT signals from water bodies can be removed. The residual signal dependent only on the material in water was used as a basis for computer categorization of lakes by type and concentration of suspended material. Several hundred lakes in the Madison and Spooner, Wisconsin area were categorized by computer techniques for tannin or nontannin waters and for the degree of algae, silt, weeds, and bottom effects present. When the lakes are categorized as having living algae or weeds, their concentration is related to the enrichment or eutrophication of the lake.

Rogers, R. H.↗
NASA NTRS1975

Application of LANDSAT to the Surveillance and Control of Lake Eutrophication in the Great Lakes Basin

The author has identified the following significant results. Computer techniques developed for mapping water quality parameters from LANDSAT data were demonstrated, using ground truth collected in an ongoing survey of water quality in Saginaw Bay (Lake Huron), Michigan. Regression correlation coefficients varied from 0.99 for total phosphorus to 0.72 for chlorophyll-a corrected. Five water quality parameters are best correlated with LANDSAT band 6 alone. Temperature relates to band 5 alone and only two bands are justified for mapping the remaining six parameters. LANDSAT CCTs were used as a basis for inventorying land use within each of the Ohio-Kentucky-Indiana regional commissions, 225 drainage areas, and nine counties. Sixteen categories were merged into ten categories and mapped at a scale of 1 in. = 5,000 ft, with detail to 0.44 hectares for the 2,700 sq mi region. These products were produced in less than 90 days at a cost of one cent an acre. LANDSAT CCTS were also used as a basis for inventorying land cover within the Triangle J council of governments 1,750 sq mi study area. The complete inventory was accomplished within a period of 60 days at a cost of about one half cent per acre.

Rogers, R. H.↗
NASA NTRS1976

Application of LANDSAT to the surveillance and control of lake eutrophication in the Great Lakes basin

The author has identified the following significant results. Computer techniques were developed for mapping water quality parameters from LANDSAT data, using surface samples collected in an ongoing survey of water quality in Saginaw Bay. Chemical and biological parameters were measured on 31 July 1975 at 16 bay stations in concert with the LANDSAT overflight. Application of stepwise linear regression bands to nine of these parameters and corresponding LANDSAT measurements for bands 4 and 5 only resulted in regression correlation coefficients that varied from 0.94 for temperature to 0.73 for Secchi depth. Regression equations expressed with the pair of bands 4 and 5, rather than the ratio band 4/band 5, provided higher correlation coefficients for all the water quality parameters studied (temperature, Secchi depth, chloride, conductivity, total kjeldahl nitrogen, total phosphorus, chlorophyll a, total solids, and suspended solids).

Rogers, R. H.↗
NASA NTRS1977

Application of LANDSAT to the surveillance of lake eutrophication in the Great Lakes basin

The author has identified the following significant results. A step-by-step procedure for establishing and monitoring the trophic status of inland lakes with the use of LANDSAT data, surface sampling, laboratory analysis, and aerial observations were demonstrated. The biomass was related to chlorophyll-a concentrations, water clarity, and trophic state. A procedure was developed for using surface sampling, LANDSAT data, and linear regression equations to produce a color-coded image of large lakes showing the distribution and concentrations of water quality parameters, causing eutrophication as well as parameters which indicate its effects. Cover categories readily derived from LANDSAT were those for which loading rates were available and were known to have major effects on the quality and quantity of runoff and lake eutrophication. Urban, barren land, cropland, grassland, forest, wetlands, and water were included.

Rogers, R. H.↗
NASA NTRS2021

Cold Season Performance of the NU-WRF Regional Climate Model in the Great Lakes Region

As Earth’s largest collection of freshwater, the Laurentian Great Lakes have enormous ecological and socio-economic value. Their basin has become a regional hotspot of climatic and limnological change, potentially threatening its vital natural resources. Consequentially, there is a need to assess the current state of climate models regarding their performance across the Great Lakes region and develop the next generation of high-resolution regional climate models to address complex limnological processes and lake-atmosphere interactions. In response to this need, the current paper focuses on the generation and analysis of a 20-member ensemble of 3-km National Aeronautics and Space Administration (NASA)-Unified Weather Research and Forecasting (NU-WRF) simulations for the 2014-2015 cold season. The study aims to identify the model’s strengths and weaknesses; optimal configuration for the region; and the impacts of different physics parameterizations, coupling to a 1D lake model, time-variant lake-surface temperatures, and spectral nudging. Several key biases are identified in the cold-season simulations for the Great Lakes region, including an atmospheric cold bias that is amplified by coupling to a 1D lake model but diminished by applying the Community Atmosphere Model radiation scheme and Morrison microphysics scheme; an excess precipitation bias; anomalously early initiation of fall lake turnover and subsequent cold lake bias; excessive and overly persistent lake ice cover; and insufficient evaporation over Lakes Superior and Huron. The research team is currently addressing these key limitations by coupling NU-WRF to a 3D lake model in support of the next generation of regional climate models for the critical Great Lakes Basin.

Michael Notaro↗
NASA NTRS1984

Phase 2 development of Great Lakes algorithms for Nimbus-7 coastal zone color scanner

A series of experiments have been conducted in the Great Lakes designed to evaluate the application of the NIMBUS-7 Coastal Zone Color Scanner (CZCS). Atmospheric and water optical models were used to relate surface and subsurface measurements to satellite measured radiances. Absorption and scattering measurements were reduced to obtain a preliminary optical model for the Great Lakes. Algorithms were developed for geometric correction, correction for Rayleigh and aerosol path radiance, and prediction of chlorophyll-a pigment and suspended mineral concentrations. The atmospheric algorithm developed compared favorably with existing algorithms and was the only algorithm found to adequately predict the radiance variations in the 670 nm band. The atmospheric correction algorithm developed was designed to extract needed algorithm parameters from the CZCS radiance values. The Gordon/NOAA ocean algorithms could not be demonstrated to work for Great Lakes waters. Predicted values of chlorophyll-a concentration compared favorably with expected and measured data for several areas of the Great Lakes.

Tanis, Fred J.↗
NASA NTRS2020

Leveraging Google Earth Engine User Interface for Semiautomated Wetland Classification in the Great Lakes Basin at 10 m With Optical and Radar Geospatial Datasets

As one of the world’s largest freshwater ecosystems,the Great Lakes Basin houses hundreds of thousands of acres of wetlands that support a variety of crucial ecological and environmental functions at the local, regional, and global levels.Monitoring these wetlands is critical to conservation and restoration efforts, however current methods that rely on field monitoring are labor-intensive, costly, and often outdated. In this study, we present a graphical user interface constructed in Google Earth Engine called the Wetland Extent Tool (WET),which allows semi-automatic wetland classification according to a user-input area of interest and date range. WET composites datasets and conducts multi source, moderate resolution processing utilizing Landsat 8 OLI, Sentinel-2 MSI, Sentinel-1 C-SAR, and Shuttle Radar Topography Mission (SRTM) datasets to classify wetlands in the entire Great Lakes Basin. We evaluated classification results of wetlands, uplands, and open water from May-September 2019, and tested whether SRTM elevation, slope,or the Dynamic Surface Water Extent produced the most accurate results in each Great Lake Basin in conjunction with optical indices and radar composites. We found that elevation produced the most accurate classification in Lake Erie, Michigan,and Ontario, while slope performed best in Lake Huron and Superior. Lake Erie, Michigan, Ontario, and Huron achieved high overall accuracy and identification of wetlands. WET leverages cloud-computing for multi source processing of moderate resolution remote sensing data, and employs a user interface in Google Earth Engine that wetland managers and conservationists can use to monitor wetland extent in the Great Lakes Basin in near real-time.

Vanessa L Valenti↗
NASA NTRS1981

Development of Great Lakes algorithms for the Nimbus-G coastal zone color scanner

A series of experiments in the Great Lakes designed to evaluate the application of the Nimbus G satellite Coastal Zone Color Scanner (CZCS) were conducted. Absorption and scattering measurement data were reduced to obtain a preliminary optical model for the Great Lakes. Available optical models were used in turn to calculate subsurface reflectances for expected concentrations of chlorophyll-a pigment and suspended minerals. Multiple nonlinear regression techniques were used to derive CZCS water quality prediction equations from Great Lakes simulation data. An existing atmospheric model was combined with a water model to provide the necessary simulation data for evaluation of the preliminary CZCS algorithms. A CZCS scanner model was developed which accounts for image distorting scanner and satellite motions. This model was used in turn to generate mapping polynomials that define the transformation from the original image to one configured in a polyconic projection. Four computer programs (FORTRAN IV) for image transformation are presented.

Tanis, F. J.↗