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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 NTRS1986

Constructing a coherent long-term global total ozone climatology from the BUV, MFR, and SBUV/TOMS data sets

Multichannel Filter Radiometer (MFR) data are intercompared with Solar Backscattered Ultraviolet (SBUV) and Total Ozone Mapping Spectrometer (TOMS) data during a five-month period of their data overlap in order to determine the usefulness of MFR data to represent the SBUV/TOMS and backscatter ultraviolet spectrometer data during their data gap period. Total ozone differences found were latitudinally and monthly dependent. Average differences between the TOMS and the MFR ozone data vary from -12.4 to +13.3 percent, with differences in the tropical to middle latitudes generally between + or - 4 percent. Biases between the data sets can be removed for certain latitude zones by applying an appropriate algorithm to the MFR ozone data.

Ellis, J. S.↗
DOE OSTI2021

Exploring Causal Physical Mechanisms via Non-Gaussian Linear Models and Deep Kernel Learning: Applications for Ferroelectric Domain Structures

Rapid emergence of multimodal imaging in scanning probe, electron, and optical microscopies has brought forth the challenge of understanding the information contained in these complex data sets, targeting the intrinsic correlations between different channels, and further exploring the underpinning causal physical mechanisms. Here, we develop such an analysis framework for Piezoresponse Force Microscopy. We argue that under certain conditions, we can bootstrap experimental observations with the prior knowledge of materials structure to get information on certain nonobserved properties, and demonstrate linear causal analysis for PFM observables. We further demonstrate that the strength of individual causal links between complex descriptors can be ascertained using the deep kernel learning (DKL) model. In this DKL analysis, we use the prior information on domain structure within the image to predict the physical properties. This analysis demonstrates the correlative relationships between morphology, piezoresponse, elastic property, etc., at nanoscale. The prediction of morphology and other physical parameters illustrates a mutual interaction between surface condition and physical properties in ferroelectric materials. Overall, this analysis is universal and can be extended to explore the correlative relationships of other multichannel data sets, and allow for high-fidelity reconstruction of underpinning functionalities and physical mechanisms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1985

Analysis of data acquired by Shuttle Imaging Radar SIR-A and Landsat Thematic Mapper over Baldwin County, Alabama

Seasonally compatible data collected by SIR-A and by Landsat 4 TM over the lower coastal plain in Alabama were coregistered, forming a SIR-A/TM multichannel data set with 30 m x 30 m pixel size. Spectral signature plots and histogram analysis of the data were used to observe data characteristics. Radar returns from pine forest classes correlated highly with the tree ages, suggesting the potential utility of microwave remote sensing for forest biomass estimation. As compared with the TM-only data set, the use of SIR-A/TM data set improved classification accuracy of the seven land cover types studied. In addition, the SIR-A/TM classified data support previous finding by Engheta and Elachi (1982) that microwave data appear to be correlated with differing bottomland hardwood forest vegetation as associated with varying water regimens (i.e., wet versus dry).

Wu, S.-T.↗
NASA NTRS1997

Arctic and Antarctic Sea Ice Concentrations from Multichannel Passive-Microwave Satellite Data Sets: October 1978-September 1995 User's Guide

Satellite multichannel passive-microwave sensors have provided global radiance measurements with which to map, monitor, and study the Arctic and Antarctic polar sea ice covers. The data span over 18 years (as of April 1997), starting with the launch of the Scanning Multichannel Microwave Radiometer (SMMR) on NASA's SeaSat A and Nimbus 7 in 1978 and continuing with the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSMI) series beginning in 1987. It is anticipated that the DMSP SSMI series will continue into the 21st century. The SSMI series will be augmented by new, improved sensors to be flown on Japanese and U.S. space platforms. This User's Guide provides a description of a new sea ice concentration data set generated from observations made by three of these multichannel sensors. The data set includes gridded daily ice concentrations (every-other-day for the SMMR data) for both the north and south polar regions from October 26, 1978 through September 30, 1995, with the one exception of a 6-week data gap from December 3, 1987 through January 12, 1988. The data have been placed on two CD-ROMs that include a ReadMeCD file giving the technical details on the file format, file headers, north and south polar grids, ancillary data sets, and directory structure of the CD-ROM. The CD-ROMS will be distributed by the National Snow and Ice Data Center in Boulder, CO.

Sea ice concentration↗
NASA NTRS1980

An improvement in land cover classification achieved by merging microwave data with Landsat multispectral scanner data

The improvement in land cover classification achieved by merging microwave data with Landsat MSS data is examined. To produce a merged data set for analysis and comparison, a registration procedure by which a set of Seasat SAR digital data was merged with the MSS data is described. The Landsat MSS data and the merged Landsat/Seasat data sets were processed using conventional multichannel spectral pattern recognition techniques. An analysis of the classified data sets indicates that while Landsat data delineate different forest types (i.e., deciduous/coniferous) and allow some species separation, SAR data provide additional information related to plant canopy configuration and vegetation density as associated with varying water regimes, and therefore allow for further subdivision in the classification of forested wetlands of the coastal region of the southern United States.

Wu, S. T.↗
NASA NTRS1981

SMMR data set development for GARP

The NIMBUS 7 Scanning Multichannel Microwave Radiometer (SMMR) data are analyzed. The impact of cross polarization and Faraday rotation on SMMR derived brightness temperatures is evaluated. The algorithms used to retrieve the geophysical parameters are tested, refined, and compared with values derived by other techniques. The technical approach taken is described and the results presented.

Kogut, J.↗
NASA NTRS1992

Spatial patterns of increases and decreases in the length of the sea ice season in the north polar region, 1979-1986

Recently it was reported that sea ice extents in the Northern Hemisphere showed a very slight but statistically significant decrease over the 8.8-year period of the Nimbus 7 scanning multichannel microwave radiometer (SMMR) data set. In this paper the same SMMR data are used to reveal spatial patterns in increasing and decreasing sea ice coverage. Specifically, the length of the ice season is mapped for each full year of the SMMR data set (1979-1986), and the trends over the 8 years in these ice season lengths are also mapped. These trends show considerable spatial coherence, with a shortening in the sea ice season apparent in much of the eastern hemisphere of the north polar ice cover, particularly in the Sea of Okhotsk, the Barents Sea, and the Kara Sea, and a lengthening of the sea ice season apparent in much of the western hemisphere of the north polar ice cover, particularly in Davis Strait, the Labrador Sea, and the Beaufort Sea.

Parkinson, Claire L.↗
NASA NTRS1989

Polar microwave brightness temperatures from Nimbus-7 SMMR: Time series of daily and monthly maps from 1978 to 1987

A time series of daily brightness temperature gridded maps (October 25, 1978 through August 15, 1987) were generated from all ten channels of the Nimbus-7 Scanning Multichannel Microwave Radiometer orbital data. This unique data set can be utilized in a wide range of applications including heat flux, ocean circulation, ice edge productivity, and climate studies. Two sets of data in polar stereographic format are created for the Arctic region: one with a grid size of about 30 km on a 293 by 293 array similar to that previously utilized for the Nimbus-5 Electrically Scanning Microwave Radiometer, while the other has a grid size of about 25 km on a 448 by 304 array identical to what is now being used for the DMSP Scanning Multichannel Microwave Imager. Data generated for the Antaractic region are mapped using the 293 by 293 grid only. The general technique for mapping, and a quality assessment of the data set are presented. Monthly and yearly averages are also generated from the daily data and sample geophysical ice images and products derived from the data are given. Contour plots of monthly ice concentrations derived from the data for October 1978 through August 1987 are presented to demonstrate spatial and temporal detail which this data set can offer, and to show potential research applications.

Comiso, Josefino C.↗
NASA NTRS1983

Satellite data

Three satellite SST data sets were analyzed, all derived from the Scanning Multichannel Microwave Radiometer (SMMR) on the Nimbus-7 satellite. The SMMR is a ten-channel instrument flown also on the SEASAT satellite (now defunct) and was designed to measure SST in addition to other oceanographic and meteorological parameters. The three SMMR data sets were referred to as SMMR-I, -II, and -III. SMMR-I and -II were produced by T. Wilheit and A. Milman, SMMR-II being an updated and refined version of SMMR-I. A description of the algorithms used is provided. SMMR-III was provided by C. Prabhakara and was obtained by a different retrieval approach from the others. For consistency with the file code conventions and to avoid confusion, SMMRs I, II, and III were renamed SMMR-A, SMMR-D, and SMMR-C, respectively.

Njoku, E. G.↗
NASA NTRS1983

Ocean color algorithm for remote sensing of chlorophyll

An algorithm for the remote detection of chlorophyll a in the ocean was tested during a Nantucket Shoals experiment conducted by NASA. A set of Multichannel Ocean Color Sensor (MOCS) data selected from one flight for each of the two altitudes flown was used to calibrate the algorithm for chlorophyll a concentration. The equations were then applied to all unsaturated MOCS data collected during the 8-day experiment to generate contour maps of chlorophyll a concentration over the shoals. One additional flight was conducted away from the shoals both on and off the Continental Shelf. Although no solar elevation or environmental corrections were made to the original conversions, the equations in these tests determined chlorophyll a concentrations to an accuracy better than 1.0 micron g/L despite the fact that the solar elevation varied between 20 deg and 56 deg during the data collection periods of the experiments.

Grew, G. W.↗
DOE OSTI2024

Discovering invariant spatial features in electron energy loss spectroscopy images on the mesoscopic and atomic levels

Over the last two decades, Electron Energy Loss Spectroscopy (EELS) imaging with a scanning transmission electron microscope has emerged as a technique of choice for visualizing complex chemical, electronic, plasmonic, and phononic phenomena in complex materials and structures. The availability of the EELS data necessitates the development of methods to analyze multidimensional data sets with complex spatial and energy structures. Traditionally, the analysis of these data sets has been based on analysis of individual spectra, one at a time, whereas the spatial structure and correlations between individual spatial pixels containing the relevant information of the physics of underpinning processes have generally been ignored and analyzed only via the visualization as 2D maps. Here, we develop a machine learning-based approach and workflows for the analysis of spatial structures in 3D EELS data sets using a combination of dimensionality reduction and multichannel rotationally invariant variational autoencoders. This approach is illustrated for the analysis of both the plasmonic phenomena in a system of nanowires and in the core excitations in functional oxides using low loss and core-loss EELS, respectively. The code developed in this manuscript is open sourced and freely available and provided as a Jupyter notebook for the interested reader.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Observations and seismoacoustic simulations of earthquake-generated infrasound waves in non-epicentral regions

We analysed infrasound waves associated with the Gyeongju earthquake (ML 5.8) that occurred on 2016 September 12, in the southeastern Korean Peninsula. For infrasound wave detection, the Progressive Multichannel Correlation method was applied to the infrasound data set recorded at seven arrays operating in South Korea at epicentral distances ranging from 178 to 472 km. Based on the back-projection method constrained by array-dependent celerity and azimuth deviation models, the source regions were identified in both the epicentral and non-epicentral regions. Remarkably, the non-epicentral secondary sources of this earthquake were located in regions with shallow water depths: (i) the western coastal area in the Yellow Sea and (ii) the shallow ocean basin and bank in the East Sea. The location results obtained from the earthquake could be corroborated through its foreshock (ML 5.1), yielding location results consistent with those of the main shock. The generation of infrasound waves over shallow water depths was fortuitously validated by direct recordings of dominant single-frequency (~0.3 Hz) infrasound waves at close range via temporary sensors near the ocean basin and bank. We interpreted that low-frequency infrasound signals could be generated from interactions among the ocean floor, shallow seawater and atmosphere. We performed numerical simulations of seismoacoustic fields to predict ground motions on the seafloor and acoustic transmission efficiency between the water and air interface. The simulations quantified the energy transfer through different media and clarified our observational results. We found that because this solid Earth‒water‒atmosphere coupled air wave has a relatively low frequency (~0.3 Hz), it can survive propagation over long distances compared with high-frequency infrasound waves generated in inland and mountain regions. In this study, we extend our understanding of water‒atmosphere coupling and the monitoring framework for earthquake-associated non-epicentral infrasound waves, encompassing not only inland ground shaking but also shallow sea regions located far from the epicentre.

Earthquake monitoring and test-ban treaty verifica↗
NASA NTRS1979

Improvement of selected satellite applications through the use of microwave data

This paper describes an analysis of a data set in which satellite acquired microwave data (Seasat Synthetic Aperture Radar) have been registered with Landsat Multispectral Scanner data and the combined data processed using conventional multichannel spectral pattern recognition programs. Results of this analysis indicate that the combined data set offers improvement in surface classification that is significant to certain applications. A brief description of the registration procedure is given. The improvement in results rendered to selected resource management applications is discussed. The presented results are preliminary due to the short time that the satellite microwave data have been available; however, a more comprehensive analysis is in progress and will be completed during the coming year.

Mooneyhan, D. W.↗
NASA NTRS2000

Satellite Data Sets in the Polar Regions

We have generated about two decades of consistently derived geophysical parameters in the polar regions. The key parameters are sea ice concentration, surface temperature, albedo, and cloud cover statistics. Sea ice concentrations were derived from the Scanning Multichannel Microwave Radiometer (SMMR) data and the Special Scanning Cl Microwave Imager (SSM/I) data from several platforms using the enhanced Bootstrap Algorithm for the period 1978 through 1999. The new algorithm reduces the errors associated with spatial and temporal variations in the emissivity and surface temperatures of sea ice. Also, bad data at ocean/land interfaces are identified and deleted in an unsupervised manner. Surface ice temperature, albedo and cloud cover statistics are derived simultaneously from the Advanced Very High Resolution Radiometer (AVHRR) data from 1981 through 1999 and mapped at a higher resolution but the same format as the ice concentration data. The technique makes use these co-registered ice concentration maps to enable cloud masking to be done separately for open ocean, sea ice and land areas. The effect of inversion is minimized by taking into consideration the expected changes in the effect of inversion with altitude, especially in the Antarctic. A technique for ice type regional classification has also been developed using multichannel cluster analysis and a neural network. This provide a means to identify large areas of thin ice, first year ice, and older ice types. The data sets have been shown to be coherent with each other and provide a powerful tool for in depth studies of the currently changing Arctic and Antarctic environment.

Comiso, Josefino C.↗
NASA NTRS1999

Generation of Level 3 SMMR and SSM/I Brightness Temperatures for the Period 1978-1999

The NOAA/NASA Pathfinder Program was initially designed to assure that certain key remote sensing data sets of particular significance to global change research were scientifically validated, consistently processed and made readily available to the research community at minimal cost. Through this Program the National Snow and Ice Data Center (NSIDC), University of Colorado has successfully processed, archived and distributed the Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave/Imager (SSM/I) Level 3 (EASE-Grid format) Pathfinder data sets for the period 1978 to 1999. These data are routinely distributed to approximately 150 researchers through various media including CD-ROM, 8 mm tape, ftp and the EOS Information Management System (IMS). At NSIDC these data are currently being applied in the development and validation of algorithms to derive snow water equivalent (NASA NAG5-6636), the mapping of frozen ground and the detection of the onset of melt over ice sheets, sea ice and snow cover. The EASE-Grid format, developed at NSIDC in conjunction with the SMMR-SSM/I Pathfinder project has also been applied to Advanced Very High Resolution Radiometer (AVHRR) and TOVS Pathfinder data, as well as ancillary data such as digital elevation, land cover classification and several in situ data sets. EASE-Grid will also be used for all land products derived from the NASA EOS AMSR-E instrument.

Partington, Kim↗
NASA NTRS1981

The Seasat commercial demonstration program

The background and development of the Seasat commercial demonstration program are reviewed and the Seasat spacecraft and its sensors (altimeter, wind field scatterometer, synthetic aperture radar, and scanning multichannel microwave radiometer) are described. The satellite data distribution system allows for selected sets of data, reformatted or tailored to specific needs and geographical regions, to be available to commercial users. Products include sea level and upper atmospheric pressure, sea surface temperature, marine winds, significant wave heights, primary wave direction and period, and spectral wave data. The results of a set of retrospective case studies performed for the commercial demonstration program are described. These are in areas of application such as marine weather and ocean condition forecasting, offshore resource exploration and development, commercial fishing, and marine transportation.

Mccandless, S. W.↗
NASA NTRS1982

Analysis of ocean color scanner data from the Superflux III Experiment

The scanner collected data on October 15, 20, and 22, 1980. Single-channel gray-scale data products generated 5 minutes after the scanner were collected and showed details of the Chesapeake plume structure, suggesting that this quick-look capability could have potential use for experiments in real time. The Chesapeake Bay plume extended offshore about 5 nautical miles on October 15, and 7 nautical miles on October 20. Using the October 15 1980, data, a correlation coefficient of r = 0.889 was obtained between chlorophyll 'A' and the ratio of band 7 (664-684 nanometers) to band 9 (746-766 nanometers). This ratio was then used to calculate the average surface chlorophyll 'A' concentration of the water flowing out of the Chesapeak Bay. A ratio from the Ocean Color Scanner bands was created to simulate the ratio that the Multichannel Ocean Color Sensor uses to calculate chlorophyll A concentrations. Using the October 15, 1980, data set, this ratio has a correlation coefficient of r = -0.739 with the log of the chlorophyll 'A' and the ratio of band 2 minus band 4 to band 2 plus band 4 of the Ocean Color Scanner. No correlation is found between the Ocean Color Scanner data and total suspended solid measurements made on October 15, 1980.

Ohlhorst, C. W.↗