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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Association of Presolar Grains with Molecular Cloud Material in IDPs

Anhydrous interplanetary dust particles (IDPs) collected in the stratosphere appear chemically, mineralogically, and texturally primitive in comparison to meteorites. Particles that escape significant atmospheric entry heating have highly unequilibrated mineralogy, are volatile element rich, and, overall, appear to have escaped significant parent body hydrothermal alteration. These IDPs are comprised of the building blocks of the solar system. The strongest evidence that anhydrous IDPs are primitive is that they contain abundant stardust and molecular cloud material. In particular, presolar silicates were first identified in IDPs and are present in abundances (450-5,500 ppm) that are well above that observed in primitive meteorites (less than 170 ppm). The most fragile (cluster) IDPs also commonly exhibit large H and N isotopic anomalies that likely originated by isotopic fractionation during extremely low temperature chemical reactions in a presolar cold molecular cloud. The D/H ratios exceed that of most primitive meteorites, and in rare cases reach values directly observed from simple gas phase molecules in cold molecular clouds. The most extreme D- and N-15-enrichments are usually observed at the finest spatial scales (0.5-2 microns) that can be measured. These observations suggest that D and N-15 hotspots are in fact preserved nuggets of molecular cloud material, and that the materials within them also have presolar origins. The advanced capabilities of the NanoSIMS ion microprobe now enable us to test this hypothesis. Here, we report two recent examples of presolar silicates found to be directly associated with molecular cloud material.

Messenger, S.↗
NASA NTRS1996

Feature Selection for Classification of Polar Regions Using a Fuzzy Expert System

Labeling, feature selection, and the choice of classifier are critical elements for classification of scenes and for image understanding. This study examines several methods for feature selection in polar regions, including the list, of a fuzzy logic-based expert system for further refinement of a set of selected features. Six Advanced Very High Resolution Radiometer (AVHRR) Local Area Coverage (LAC) arctic scenes are classified into nine classes: water, snow / ice, ice cloud, land, thin stratus, stratus over water, cumulus over water, textured snow over water, and snow-covered mountains. Sixty-seven spectral and textural features are computed and analyzed by the feature selection algorithms. The divergence, histogram analysis, and discriminant analysis approaches are intercompared for their effectiveness in feature selection. The fuzzy expert system method is used not only to determine the effectiveness of each approach in classifying polar scenes, but also to further reduce the features into a more optimal set. For each selection method,features are ranked from best to worst, and the best half of the features are selected. Then, rules using these selected features are defined. The results of running the fuzzy expert system with these rules show that the divergence method produces the best set features, not only does it produce the highest classification accuracy, but also it has the lowest computation requirements. A reduction of the set of features produced by the divergence method using the fuzzy expert system results in an overall classification accuracy of over 95 %. However, this increase of accuracy has a high computation cost.

Penaloza, Mauel A.↗
NASA NTRS1993

The effects of cloud inhomogeneities upon radiative fluxes, and the supply of a cloud truth validation dataset

A series of cloud and sea ice retrieval algorithms are being developed in support of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Science Team objectives. These retrievals include the following: cloud fractional area, cloud optical thickness, cloud phase (water or ice), cloud particle effective radius, cloud top heights, cloud base height, cloud top temperature, cloud emissivity, cloud 3-D structure, cloud field scales of organization, sea ice fractional area, sea ice temperature, sea ice albedo, and sea surface temperature. Due to the problems of accurately retrieving cloud properties over bright surfaces, an advanced cloud classification method was developed which is based upon spectral and textural features and artificial intelligence classifiers.

Welch, Ronald M.↗
NASA NTRS1990

The analysis of polar clouds from AVHRR satellite data using pattern recognition techniques

The cloud cover in a set of summertime and wintertime AVHRR data from the Arctic and Antarctic regions was analyzed using a pattern recognition algorithm. The data were collected by the NOAA-7 satellite on 6 to 13 Jan. and 1 to 7 Jul. 1984 between 60 deg and 90 deg north and south latitude in 5 spectral channels, at the Global Area Coverage (GAC) resolution of approximately 4 km. This data embodied a Polar Cloud Pilot Data Set which was analyzed by a number of research groups as part of a polar cloud algorithm intercomparison study. This study was intended to determine whether the additional information contained in the AVHRR channels (beyond the standard visible and infrared bands on geostationary satellites) could be effectively utilized in cloud algorithms to resolve some of the cloud detection problems caused by low visible and thermal contrasts in the polar regions. The analysis described makes use of a pattern recognition algorithm which estimates the surface and cloud classification, cloud fraction, and surface and cloudy visible (channel 1) albedo and infrared (channel 4) brightness temperatures on a 2.5 x 2.5 deg latitude-longitude grid. In each grid box several spectral and textural features were computed from the calibrated pixel values in the multispectral imagery, then used to classify the region into one of eighteen surface and/or cloud types using the maximum likelihood decision rule. A slightly different version of the algorithm was used for each season and hemisphere because of differences in categories and because of the lack of visible imagery during winter. The classification of the scene is used to specify the optimal AVHRR channel for separating clear and cloudy pixels using a hybrid histogram-spatial coherence method. This method estimates values for cloud fraction, clear and cloudy albedos and brightness temperatures in each grid box. The choice of a class-dependent AVHRR channel allows for better separation of clear and cloudy pixels than does a global choice of a visible and/or infrared threshold. The classification also prevents erroneous estimates of large fractional cloudiness in areas of cloudfree snow and sea ice. The hybrid histogram-spatial coherence technique and the advantages of first classifying a scene in the polar regions are detailed. The complete Polar Cloud Pilot Data Set was analyzed and the results are presented and discussed.

Smith, William L.↗
NASA NTRS2015

Interstellar and Solar System Organic Matter Preserved in Interplanetary Dust

Interplanetary dust particles (IDPs) collected in the Earth's stratosphere derive from collisions among asteroids and by the disruption and outgassing of short‐period comets. Chondritic porous (CP) IDPs are among the most primitive Solar System materials. CP‐IDPs have been linked to cometary parent bodies by their mineralogy, textures, C‐content, and dynamical histories. CP‐IDPs are fragile, fine‐grained (less than um) assemblages of anhydrous amorphous and crystalline silicates, oxides and sulfides bound together by abundant carbonaceous material. Ancient silicate, oxide, and SiC stardust grains exhibiting highly anomalous isotopic compositions are abundant in CP‐IDPs, constituting 0.01 - 1 % of the mass of the particles. The organic matter in CP‐IDPs is isotopically anomalous, with enrichments in D/H reaching 50x the terrestrial SMOW value and 15N/14N ratios up to 3x terrestrial standard compositions. These anomalies are indicative of low T (10‐100 K) mass fractionation in cold molecular cloud or the outermost reaches of the protosolar disk. The organic matter shows distinct morphologies, including sub‐um globules, bubbly textures, featureless, and with mineral inclusions. Infrared spectroscopy and mass spectrometry studies of organic matter in IDPs reveals diverse species including aliphatic and aromatic compounds. The organic matter with the highest isotopic anomalies appears to be richer in aliphatic compounds. These materials also bear similarities and differences with primitive, isotopically anomalous organic matter in carbonaceous chondrite meteorites. The diversity of the organic chemistry, morphology, and isotopic properties in IDPs and meteorites reflects variable preservation of interstellar/primordial components and Solar System processing. One unifying feature is the presence of sub‐um isotopically anomalous organic globules among all primitive materials, including IDPs, meteorites, and comet Wild‐2 samples returned by the Stardust mission.

Messenger, Scott↗
NASA NTRS1966

An analysis of eight flights by U-2 aircraft over severe storms in Oklahoma

The method and results of the analysis of photographic records of the summit areas of severe storms obtained from a U-2 aircraft flying at an altitude of 65,000 ft. over Oklahoma are described. Comparison of the heights of storm cells protruding through the main anvil cirrus deck of a squall line with the predictions of simple "parcel" theory suggests the latter may be used to indicate the maximum development attainable (but not necessarily attained) by a squall line on a given day. It is suggested that temperatures approaching -1OO°C. may occur in the domes of giant quasi-steady storm cells reaching altitudes of 55,000 - 60,000 ft. bounded by a thin shell of extremely stable air, which is not inconsistent with the rather smooth texture of dome summits sometimes shown by the U-2 photographs.

CLOUD PHOTOGRAPHY↗
NASA NTRS2008

An Automatic Cloud Mask Algorithm Based on Time Series of MODIS Measurements

Quality of aerosol retrievals and atmospheric correction depends strongly on accuracy of the cloud mask (CM) algorithm. The heritage CM algorithms developed for AVHRR and MODIS use the latest sensor measurements of spectral reflectance and brightness temperature and perform processing at the pixel level. The algorithms are threshold-based and empirically tuned. They don't explicitly address the classical problem of cloud search, wherein the baseline clear-skies scene is defined for comparison. Here, we report on a new CM algorithm which explicitly builds and maintains a reference clear-skies image of the surface (refcm) using a time series of MODIS measurements. The new algorithm, developed as part of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm for MODIS, relies on fact that clear-skies images of the same surface area have a common textural pattern, defined by the surface topography, boundaries of rivers and lakes, distribution of soils and vegetation etc. This pattern changes slowly given the daily rate of global Earth observations, whereas clouds introduce high-frequency random disturbances. Under clear skies, consecutive gridded images of the same surface area have a high covariance, whereas in presence of clouds covariance is usually low. This idea is central to initialization of refcm which is used to derive cloud mask in combination with spectral and brightness temperature tests. The refcm is continuously updated with the latest clear-skies MODIS measurements, thus adapting to seasonal and rapid surface changes. The algorithm is enhanced by an internal dynamic land-water-snow classification coupled with a surface change mask. An initial comparison shows that the new algorithm offers the potential to perform better than the MODIS MOD35 cloud mask in situations where the land surface is changing rapidly, and over Earth regions covered by snow and ice.

Lyapustin, Alexei↗
NASA NTRS2015

Predicting Clear-Sky Reflectance Over Snow/Ice in Polar Regions

Satellite remote sensing of clouds requires an accurate estimate of the clear-sky radiances for a given scene to detect clouds and aerosols and to retrieve their microphysical properties. Knowing the spatial and angular variability of clear-sky albedo is essential for predicting clear-sky radiance at solar wavelengths. The Clouds and the Earth's Radiant Energy System (CERES) Project uses the nearinfrared (NIR; 1.24, 1.6 or 2.13 micrometers), visible (VIS; 0.63 micrometers) and vegetation (VEG; 0.86 micrometers) channels available on the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) to help identify clouds and retrieve their properties in both snow-free and snow-covered conditions. Thus, it is critical to have reliable distributions of clear-sky albedo for all of these channels. In CERES Edition 4 (Ed4), the 1.24-micrometer channel is used to retrieve cloud optical depth over snow/ice-covered surfaces. Thus, it is especially critical to accurately predict the 1.24-micrometer clear-sky albedo alpha and reflectance rho for a given location and time. Snow albedo and reflectance patterns are very complex due to surface texture, particle shapes and sizes, melt water, and vegetation protrusions from the snow surface. To minimize those effects, this study focuses on the permanent snow cover of Antarctica where vegetation is absent and melt water is minimal. Clear-sky albedos are determined as a function of solar zenith angle (SZA) from observations over all scenes determined to be cloud-free to produce a normalized directional albedo model (DRM). The DRM is used to develop alpha(SZA=0 degrees) on 10 foot grid for each season. These values provide the basis for predicting r at any location and set of viewing & illumination conditions. This paper examines the accuracy of this approach for two theoretical snow surface reflectance models.

Chen, Yan↗
NASA NTRS2003

Martian and Terrestrial Rock Abrasion from Wind Tunnel and Field Studies

Earth and Mars exhibit ventifacts, rocks that have been abraded by saltating sand. Previous theoretical and laboratory studies have determined abrasion susceptibilities of rocks as a function of sand type and impact angle and rock material strengths. For the last two years we have been engaged in wind tunnel and field studies to better understand the fundamental factors which control and influence rock abrasion and ventifact formation on Earth and Mars. In particular, we are examining: 1) What types of rocks (composition, texture, and shape) preferentially erode and what are the relative rates of one type vs. another? 2) What are the controlling factors of the aeolian sand cloud (flux, particle speed, surface roughness, etc) which favor rock abrasion?, 3) How do specific ventifact characteristics tie into their mode of formation and rock properties? We find several important factors: 1) Initial rock shape controls the rate of abrasion, with steeper faces abrading faster than shallower ones. The relationship is partly dependent on angle-dependent flux (proportional to sin[theta]) but exhibits additional non-linear effects from momentum transfer efficiency and rebound effects that vary with incidence angle. 2) Irregular targets with pits or grooves abrade at greater rates than targets with smooth surfaces, with indentations generally enlarging with time. Surfaces become rougher with time. 3) Targets also abrade via slope retreat, which is roughly dependent on the slope of the front face. The formation of basal sills is common, as observed on terrestrial and Martian ventifacts.

Bridges, N. T.↗
NASA NTRS1991

Fuzzy set methods for object recognition in space applications

Progress on the following tasks is reported: (1) fuzzy set-based decision making methodologies; (2) feature calculation; (3) clustering for curve and surface fitting; and (4) acquisition of images. The general structure for networks based on fuzzy set connectives which are being used for information fusion and decision making in space applications is described. The structure and training techniques for such networks consisting of generalized means and gamma-operators are described. The use of other hybrid operators in multicriteria decision making is currently being examined. Numerous classical features on image regions such as gray level statistics, edge and curve primitives, texture measures from cooccurrance matrix, and size and shape parameters were implemented. Several fractal geometric features which may have a considerable impact on characterizing cluttered background, such as clouds, dense star patterns, or some planetary surfaces, were used. A new approach to a fuzzy C-shell algorithm is addressed. NASA personnel are in the process of acquiring suitable simulation data and hopefully videotaped actual shuttle imagery. Photographs have been digitized to use in the algorithms. Also, a model of the shuttle was assembled and a mechanism to orient this model in 3-D to digitize for experiments on pose estimation is being constructed.

Keller, James M.↗
NASA NTRS1981

Mechanical aggregation of enstatite chondrites from an inhomogeneous debris cloud

Enstatite chondrites have oxygen isotope ratios closer to those of the earth and moon than other meteorites. Their minerals are chemically reduced; metal contains Si, and some Ca, Ti, Mg and Mn are incorporated in sulphides rather than silicates. Clinoenstatite and olivine are virtually Fe-free. Two types of clinoenstatite in the Indarch enstatite chondrite have been reported, one luminescing blue and one red. Similar clinoenstatites in the Kota-Kota enstatite chondrite are associated with two distinct types of forsteritic olivine, one luminescing orange and the other blue. The textural relations and differences in chemical composition cannot be explained by progressive condensation from the solar nebula and require the mechanical mixing of material from at least two sources. It is suggested here that enstatite chondrites result from mechanical and chemical processes during aerodynamic sorting and gravitational settling of debris from a hot cloud of dust, liquid and gas produced during collision of planetesimals.

Leitch, C. A.↗
NASA NTRS2018

TPSAS-NF1676L-32014-DND

The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), on-board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) is a satellite-borne polarization sensitive lidar. It has been providing the vertical distributions of clouds and aerosols along with their microphysical and optical properties since 2006. One of its important Level 2 products, feature classification, has been determined using the lidar information from 532 nm parallel and perpendicular channels, and 1064 nm channel measurements of layer integrated backscatter. Deep machine learning methods which combine both the channel and texture information to recognize feature patterns is uniquely beneficial when applied to this data. In this study, we will use Convolutional Neural Network (CNN), a deep machine learning method, to classify lidar aerosol subtypes by using the lidar profile observations. This method uses additional information from the vertical texture of the feature instead of using only the layer information. Note that in the integrated layer properties, the texture information has been masked due to averaging. Our results will show how the texture information plays a role in the classification. This preliminary work explores the benefits and potential of deep machine learning methods for lidar retrievals and focuses on the aerosol subtype classification. The broader application extends to the classification of other feature types. Future applications include the developing deep machine learning methods with neural networks to retrieve properties of the features, and studies of indirect effect of cloud-aerosol interaction from lidar measurements.

Shan Zeng Kowalski↗
NASA NTRS1991

Terrestrial polarization imagery obtained from the Space Shuttle - Characterization and interpretation

An experiment to measure the polarization of land, sea, haze, and cloud areas from space was carried aboard the Space Shuttle in September 1985. Digitized polarimetric and photometric imagery in mutually perpendicular planes was derived in the red, green, and blue spectral regions from photographs taken with two synchronized Hasselblad cameras using type 5036 Ektachrome film. Digitization at the NASA Houston Video Digital Analysis Systems Laboratory permitted reduction of the imagery into equipolarimetric contours with a relative accuracy of + or - 20 percent for comparison to ground truth. The Island of Hawaii and adjacent sea and cloud areas were the objects of the specific imagery analyzed. Results show that cloud development is uniquely characterized using percent polarization without requiring precision photometric calibration. Furthermore, sea state and wind direction over the sea could be inferred as well as terrestrial soil texture.

Egan, Walter G.↗
NASA NTRS1977

Polarimetry of satellite surfaces

Available polarization observations of the satellites are reviewed, and the question of what information about satellite surfaces such observations contain is discussed critically. For satellites with negligible atmospheres, polarization measurements should provide useful information on surface texture and on the opacity of the surface materials. However, they are unlikely to yield specific compositional information. For Titan, the only satellite known to have an extensive atmosphere, polarimetry indicates the presence of optically thick clouds; it should be possible to deduce the cloud particle characteristics from such data, especially if more extensive observations, covering the full range of available phase angles and wavelengths, are obtained.

Veverka, J.↗
NASA NTRS1987

Satellite radars for geologic mapping in tropical regions

This paper presents interpretations of the satellite radar images of cloud-covered portions of Indonesia and Amazonia obtained from NASA's Shuttle imaging radar experiments in 1981 (SIR-A) and 1984 (SIR-B). It was found that different terrain categories observed from distinctive image textures correlate well with major lithologic associations. The images show geologic structures at regional and local scales. The SIR-B images of East Kalimantan, Indonesia, reveal structural features and terrain distributions that had been overlooked or not perceived in previous surface mapping. Variability in radar response from the vegetation cover is interpretable only in coastal areas or alluvial areas that are relatively level.

Ford, J. P.↗
NASA NTRS1993

Automated cloud classification with a fuzzy logic expert system

An unresolved problem in current cloud retrieval algorithms concerns the analysis of scenes containing overlapping cloud layers. Cloud parameterizations are very important both in global climate models and in studies of the Earth's radiation budget. Most cloud retrieval schemes, such as the bispectral method used by the International Satellite Cloud Climatology Project (ISCCP), have no way of determining whether overlapping cloud layers exist in any group of satellite pixels. One promising method uses fuzzy logic to determine whether mixed cloud and/or surface types exist within a group of pixels, such as cirrus, land, and water, or cirrus and stratus. When two or more class types are present, fuzzy logic uses membership values to assign the group of pixels partially to the different class types. The strength of fuzzy logic lies in its ability to work with patterns that may include more than one class, facilitating greater information extraction from satellite radiometric data. The development of the fuzzy logic rule-based expert system involves training the fuzzy classifier with spectral and textural features calculated from accurately labeled 32x32 regions of Advanced Very High Resolution Radiometer (AVHRR) 1.1-km data. The spectral data consists of AVHRR channels 1 (0.55-0.68 mu m), 2 (0.725-1.1 mu m), 3 (3.55-3.93 mu m), 4 (10.5-11.5 mu m), and 5 (11.5-12.5 mu m), which include visible, near-infrared, and infrared window regions. The textural features are based on the gray level difference vector (GLDV) method. A sophisticated new interactive visual image Classification System (IVICS) is used to label samples chosen from scenes collected during the FIRE IFO II. The training samples are chosen from predefined classes, chosen to be ocean, land, unbroken stratiform, broken stratiform, and cirrus. The November 28, 1991 NOAA overpasses contain complex multilevel cloud situations ideal for training and validating the fuzzy logic expert system.

Tovinkere, Vasanth↗
NASA NTRS2005

Neutral Mass Spectrometry for Venus Atmosphere and Surface

The assignment is to make precise (better than 1 %) measurements of isotope ratios and accurate (5-10%) measurements of abundances of noble gas and to obtain vertical profiles of trace chemically active gases from above the clouds all the way down to the surface. Science measurement objectives are as follows: 1) Determine the composition of Venus atmosphere, including trace gas species and light stable isotopes; 2) Accurately measure noble-gas isotopic abundance in the atmosphere; 3) Provide descent, surface, and ascent meteorological data; 4) Measure zonal cloud-level winds over several Earth days; 5) Obtain near-IR descent images of the surface from 10-km altitude to the surface; 6) Accurately measure elemental abundances & mineralogy of a core from the surface; and 7) Evaluate the texture of surface materials to constrain weathering environment.

Mahaffy, Paul↗
NASA NTRS2002

Using Multi-Angle Imaging SpectroRadiometer (MISR) bidirectional reflectances for land surface studies

MISR imagery enables new methods for cloud screening and retrieval of aerosol properties over a wide range of surface types, and these data are used to compensate for atmospheric effects in the surface retrievals. The derived surface angular signatures are governed, at least in part, by macroscopic texture at the subpixel scale. Such information complements spectral data in distinguishing different types of surface cover, retrieving land surface hemispherical reflectance,and determining biogeophysical properties such as leaf area index and fractional absorbed photosynthetically active radiation. Regional and global MISR data products will be presented to illustrate the capabilities of the instrument andassociated retrieval methodologies.

MISR bidirectional surface studies spectr↗