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NASA NTRS1992

Solar-type xenon - Isotopic abundances in Pesyanoe

Elemental and isotopic abundances of Ar and Xe were measured in three grain-size separates in the dark phase of the enstatite achondrite Pesyanoe by stepwise heating, using a combination of pyrolysis and combustion steps. The data reveal a low-temperature gas fraction with Ar/Xe ratios close to the solar ratio and isotopically similar to solar-type Ar and Xe observed in lunar samples. The gas released at intermediate temperature steps shows that Ar and Xe are isotopically fractionated, compared to the low-temperature components. Pesyanoe does not have excess Ar-40 and fission Xe, which means its parent body was too small for ion reimplantation. It is concluded that solar-type Xe isotopic abundances can be inferred from the low-temperature component. A comparison of Pesyanoe and lunar data shows that isotopic signatures of solar-wind Xe, as sampled at two different points in solar system space and time, are identical within experimental error.

Kim, J. S.↗

NASA NTRS2000

Measured Correlation Between Roll-Vortex Signatures and Radar-Inferred Sea Surface Roughness

This paper presents aircraft measurements of near-surface atmospheric boundary layer roll signatures and radar-derived sea surface roughness. These data are completely coincident in space and time and this unique feature supports attempts to definitively link SAR backscatter signatures to boundary layer roll impacts. The open-ocean data were collected at an altitude of 20 in from NOAA's Long-EZ aircraft using its turbulence probe and down-looking Ka-band radar scatterometer, Several flight legs of 20-30 km were flown with a heading across the wind direction, which is also roughly perpendicular to the roll vortices. We find remarkable correlation between measured modulations in the along-wind component of wind speed and radar backscatter for the spatial scale of I to 1.5 kin. Close agreement between normalized modulation amplitudes suggests the radar-inferred surface slope variance is changing linearly with wind speed. These data were collected within 30 minutes of a RADARSAT SAR overpass where apparent boundary layer impacts of the same orientation and spatial dimension are prevalent in the SAR backscatter image. Quantitative comparison between modulations in the aircraft and satellite radar data will be discussed.

Vandemark, Douglas↗

NASA NTRS2000

Signal Processing and Fault Detection with Application to CH-46 Helicopter Data

Central to Qualtech Systems' mission is its testability and maintenance software (TEAMS) and derivatives. Many systems comprise components equipped with self-testing capability; but, if the system is complex (and involves feedback and if the self-testing itself may occasionally be faulty) tracing faults to a single or multiple causes is difficult. However, even for systems involving many thousands of components the PC-based TEAMS provides essentially real-time system-state diagnosis. Until recently TEAMS operation was passive: its diagnoses were based on whatever data sensors could provide. Now, however, a signal-processing (SP) "frontend" matched to inference needs is available. Many standard signal processing primitives, such as filtering, spectrum analysis and multi-resolution decomposition are available; the SP toolbox is also equipped with a (supervised) classification capability based on a number of decision-making paradigms. This paper is about the SP toolbox. We show its capabilities, and demonstrate its performance on the CH-46 "Westland" data set.

Wen, Fang↗

NASA NTRS1981

The wind-speed measurement capability of spaceborne radar altimeters

This study represents an attempt to quantitatively assess the capability of a spaceborne radar altimeter to infer ocean surface wind speeds from a measurement of the backscattered power at vertical incidence. The study uses data acquired during 184 near overflights of NOAA data buoys with the GEOS-3 satellite radar altimeter and encompasses a wind-speed range from less than 1 to 18 m/s. An algorithm is derived from the data comparison for converting measurements of the normalized scattering cross section of the ocean surface at 13.9 GHz into estimates of the surface wind speed at the standard anemometer height of 10 m. The algorithm is straightforward and potentially useful for on-board processing of raw altimeter data for the purpose of providing real-time estimates of surface wind speed. For winds in the range of 1 to 18 m/s, the mean difference between the altimeter-inferred winds and the buoy measurements is negligible while the standard deviation of the difference is 1.74 m/s.

Brown, G. S.↗

NASA NTRS1989

Jupiter's Great Red Spot as a shallow water system

Voyager cloud-top velocity data for Jupiter's Great Red Spot (GRS) is used to derive the bottom topography up to a constant that depends on the unknown radius of deformation. The bottom topography is inferred from the Bernoulli streamfunction, kinetic energy per unit mass, and absolute vorticity values derived from the velocity data. The results are used to calculate potential vorticity versus latitude far from the vortex. It is found that the deep atmosphere is in differential motion and that the far-field potential vorticity gradient changes sign at several latitudes. Numerical experiments are conducted to study the time-dependent behavior of the shallow water analog of Jupiter's analog.

Dowling, Timothy E.↗

NASA NTRS1993

Electrification in winter storms and the analysis of thunderstorm overflight data

We have been focusing our study of electrification in winter storms on the lightning initiation process, making inferences about the magnitude of the electric fields from the initial pulses associated with breakdown, i.e., with the formation of the initial streamers. The essence of the most significant finding is as follows: (1) initial breakdown radiation pulses from stepped leaders prior to the first return stroke are very large, reaching values of 20-30 Volts/meter, comparable to return stroke radiation; and (2) the duration of the stepped leader, from the initial detectable radiation pulse to the return stroke onset, is very-short-ranging from a minimum 1.5 ms to a maximum of 4.5 ms. This past summer (June-August of 1991) we participated in the CAPE program at the Kennedy Space Center in order to acquire data on stepped leaders in summer storms with the same equipment used to get the winter storm data. We discovered that the vigorous leaders seen in winter so frequently were present in summer storms, although not as large in amplitude and certainly not as frequent.

Brook, Marx↗

NASA NTRS1991

The 10 January 1986 disconnection event in Comet Halley

The disconnection event (DE) in the plasma tail of Comet Halley on January 9-12, 1986 is examined. The distances between the comet head and the disconnected tail are measured for a series of images for that time period and then extrapolated to the nucleus to determine the disconnection time: January 9.60 +/- 0.2 days. The approximate solar-wind conditions at the time of the DE were obtained by corotation of IMP-8 satellite data in earth orbit to Comet Halley. At the time of the DE, Comet Halley is inferred to have been close to a magnetic-sector boundary and a high-speed stream-compression region. However, a heliographic latitude separation of 22 degrees (between the comet and IMP-8) and gaps in the IMP-8 data render a more definitive statement about the linkage of the DE to external conditions quite difficult. It is not possible to resolve the effects of magnetic changes associated with the sector boundary and plasma pressure in the compression region.

Niedner, Malcolm B., Jr.↗

NASA NTRS2019

Alternative Datasets for Identification of Earth Science Events and Data

Alternative, or non-traditional, data sources can be used to generate datasets which can in turn be analyzed for temporal, spatial and climatological patterns. Events and case studies inferred from the analysis of these patterns can be used by the remote sensing community to more effectively search for Earth observation data. In this paper, we present a new alternative Earth science dataset created from the National Weather Service’s Area Forecast Discussion (AFD) documents. We then present an exploratory methodology for identifying interesting climatological patterns within the AFD data and a corresponding motivating example as to how these data and patterns can be used to search for relevant events or case studies.

Alternative data↗

NASA NTRS2019

Alternative Datasets for Identification of Earth Science Events and Data

Bugbee, Kaylin↗

NASA NTRS1993

Compositional and textural information from the dual inversion of visible, near and thermal infrared remotely sensed data

A technique is presented that allows extraction of compositional and textural information from visible, near and thermal infrared remotely sensed data. Using a library of both emissivity and reflectance spectra, endmember abundances and endmember thermal inertias are extracted from AVIRIS (Airborne Visible and Infrared Imaging Spectrometer) and TIMS (Thermal Infrared Mapping Spectrometer) data over Lunar Crater Volcanic Field, Nevada, using a dual inversion. The inversion technique is motivated by upcoming Mars Observer data and the need for separation of composition and texture parameters from sub pixel mixtures of bedrock and dust. The model employed offers the opportunity to extract compositional and textural information for a variety of endmembers within a given pixel. Geologic inferences concerning grain size, abundance, and source of endmembers can be made directly from the inverted data. These parameters are of direct relevance to Mars exploration, both for Mars Observer and for follow-on missions.

Brackett, Robert A.↗

NASA NTRSDate not supplied

Interpretable Machine Learning Models for Autonomous Characterization of Analogue Ocean World Seawater Chemistry and Biosignature Potential Using Isotope Ratio Data

Background: Future missions to ocean worlds, such as Enceladus and Europa, will attempt to characterize the subsurface seawater chemistry and assess the potential for life. Such missions will be equipped with capabilities to precisely measure volatile isotopes in plumes, atmospheres, and exospheres. Motivation: While large isotopic fractionations can indicate a biological source, there are signatures resulting from abiotic geochemical processes that mimic isotopic biosignatures. While machine learning (ML) has the potential to disentangle competing effects and biotic mimicry, high-dimensional isotope ratio mass spectrometry (IRMS) data is likely to contain noise/irrelevant features and involve complex statistical interactions that make human inference and interpretation difficult. Further, ML predictions with as far-reaching implications as an extraterrestrial biosignature on an ocean world requires the use of interpretable models (i.e., not “black box” models) with physically and mathematically meaningful feature spaces along with false positive diagnostics. Methods: We use volatile CO2 IRMS data of analogue ocean world seawaters to validate an ML approach to provide biogeochemical context for biosignature detection. We employ a feature selection method called nearest-neighbor projected distance regression (NPDR) that detects statistical interactions and helps elucidate the mechanisms of the Random Forest classification models. Results: We train and validate predictive ML models on volatile CO2 IRMS data of analogue ocean world seawaters to predict major salt components (e.g., MgSO4, NaHCO3), pH, ionic strength, and the presence of biosignatures. Features derived from IRMS measurements are augmented with extracted time-series features. Our results show high test accuracy and interpretability, which is increased by interaction network visualization, sample-wise variable importance scores, and single-sample class probability estimates. We demonstrate an ML mission software solution that triggers autonomous data transmission and biogeochemical sample prediction.

geochemistry↗

NASA NTRS2020

H- and Dissociation in Ultra-hot Jupiters: A Retrieval Case Study of WASP-18b

Atmospheres of a number of ultra-hot Jupiters (UHJs) with temperatures gsim2000 K have been observed recently. Many of these planets show largely featureless thermal spectra in the near-infrared observed with the HST WFC3 spectrograph (1.1–1.7 μm) even though this spectral range contains strong H2O opacity. Recent works have proposed the possibility of H- opacity masking the H2O feature and/or thermal dissociation of H2O causing its apparent depletion at the high temperatures of UHJs. In this work, we test these hypotheses using observations of the exoplanet WASP-18b as a case study. We report detailed atmospheric retrievals of the planet using the HyDRA retrieval code, extended to include the effects of H- opacity and thermal dissociation. We report constraints on the H2O, CO, and H- abundances as well as the pressure–temperature profile of the dayside atmosphere for retrievals with and without H-/dissociation for each data set. We find that the H2O and H- abundances are relatively unconstrained given the featureless WFC3 spectra. We do not conclusively detect H- in the planet, contrary to previous studies that used equilibrium models to infer its presence. The constraint on the CO abundance depends on the combination of WFC3 and Spitzer data, ranging from solar to super-solar CO values. We additionally see signs of a thermal inversion from two of the data sets. Our study demonstrates the potential of atmospheric retrievals of UHJs, including the effects of H- and thermal dissociation of molecules.

Siddharth Gandhi↗

NASA NTRS2012

Parametric 3D Atmospheric Reconstruction in Highly Variable Terrain with Recycled Monte Carlo Paths and an Adapted Bayesian Inference Engine

We describe a method for accelerating a 3D Monte Carlo forward radiative transfer model to the point where it can be used in a new kind of Bayesian retrieval framework. The remote sensing challenge is to detect and quantify a chemical effluent of a known absorbing gas produced by an industrial facility in a deep valley. The available data is a single low resolution noisy image of the scene in the near IR at an absorbing wavelength for the gas of interest. The detected sunlight has been multiply reflected by the variable terrain and/or scattered by an aerosol that is assumed partially known and partially unknown. We thus introduce a new class of remote sensing algorithms best described as "multi-pixel" techniques that call necessarily for a 3D radaitive transfer model (but demonstrated here in 2D); they can be added to conventional ones that exploit typically multi- or hyper-spectral data, sometimes with multi-angle capability, with or without information about polarization. The novel Bayesian inference methodology uses adaptively, with efficiency in mind, the fact that a Monte Carlo forward model has a known and controllable uncertainty depending on the number of sun-to-detector paths used.

Monte Carlo↗

NASA NTRSDate not supplied

Communication Demand in the National Airspace - A Federated Learning Approach

Within the national airspace system (NAS), efficient use of spectrum remains a challenge; as UAS and UAM missions evolve, the amount of mission-critical aircraft communications are expected to significantly grow. To accommodate the increased demand, NASA Glenn Research Center is investigating artificial intelligence approaches that could dynamically allocate spectrum; however, these solutions are driven by communication and aviation data items, many of which are not directly available. One such cornerstone data item is communication demand, parameterizing the needs within a sector in terms that may directly inform spectrum allocation, such as channel access duration, bandwidth, and modulation type. This paper considers the complexity of predicting communication demand as a function of NAS behaviors. Unlike prior prediction work in communications, this research must consider how the NAS may be impacted by external factors - such as convective weather and closures - rather than estimating demand from time-series forecasting alone. Much of this research considers a federated learning design to predict communication events in terms of the type of event occurring (sector coordination, conflict resolution, etc.). To do so, an investigation of products from Sherlock Data Warehouse is conducted, identifying the trends, sufficiency, and correlations of each product to identified events. Additionally, a preliminary discussion for inferring associations between these event types and their communication parameters (duration, bandwidth, modulation) is presented. By utilizing federated learning, imbalances in the types of events and data present throughout the NAS can inform local models without impairing global training. Furthermore, the two-stage approach proposed allows for robust and speculative communication modelling, as communication techniques continue to evolve. As a result, this model enables a generalized approach to understanding NAS communications which is able to inform long-term changes to aviation spectrum management.

Nathan Schimpf↗

NASA NTRSDate not supplied

A Federated Learning Approach to Predicting Communication Demand in the National Airspace

Within the national airspace system (NAS), efficient use of spectrum remains a challenge; as UAS and UAM missions evolve, the amount of mission-critical aircraft communications are expected to significantly grow. To accommodate the increased demand, NASA Glenn Research Center is investigating artificial intelligence approaches that could dynamically allocate spectrum; however, these solutions are driven by communication and aviation data items, many of which are not directly available. One such cornerstone data item is communication demand, parameterizing the needs within a sector in terms that may directly inform spectrum allocation, such as channel access duration, bandwidth, and modulation type. This paper considers the complexity of predicting communication demand as a function of NAS behaviors. Unlike prior prediction work in communications, this research must consider how the NAS may be impacted by external factors - such as convective weather and closures - rather than estimating demand from time-series forecasting alone. Much of this research considers a federated learning design to predict communication events in terms of the type of event occurring (sector coordination, conflict resolution, etc). To do so, an investigation of products from Sherlock Data Warehouse is conducted, identifying the trends, sufficiency, and correlations of each product to identified events. Additionally, a preliminary discussion for inferring associations between these event types and their communication parameters (duration, bandwidth, modulation) is presented. By utilizing federated learning, imbalances in the types of events and data present throughout the NAS can inform local models without impairing global training. Furthermore, the two-stage approach proposed allows for robust and speculative communication modelling, as communication techniques continue to evolve. As a result, this model enables a generalized approach to understanding NAS communications which is able to inform long-term changes to aviation spectrum management.

Nathan Schimpf↗

NASA NTRS1995

Spectral and Temporal Characteristics of X-Ray-Bright Stars in the Pleiades

We follow up our deep ROSAT imaging survey of the Pleiades (Stauffer et al. 1994) with an analysis of the spectral and temporal characteristics of the X-ray-bright stars in the Pleiades. Raymond & Smith (1977) one and two-temperature models have been used to fit the position-sensitive proportional counter (PSPC) pulse-height spectra of the dozen or so brightest sources associated with late-type Pleiades members. The best-fit temperatures suggest hot coronal temperatures for K, M, and rapidly rotating G stars, and cooler temperatures for F and slowly rotating G stars. In order to probe the many less X-ray-luminous stars, we have generated composite spectra by combining net counts from all Pleiades members according to spectral type and rotational velocity. Model fits to the composite spectra confirm the trend seen in the individual spectral fits. Particularly interesting is the apparent dependence of coronal temperature on L(sub x)/L(sub bol). A hardness-ratio analysis also confirms some of these trends. The PSPC data have also revealed a dozen or so strong X-ray flares with peak X-ray luminosities in excess of approx. 10(exp 30) ergs/sec. We have modeled the brightest of these flares with a simple quasi-static cooling loop model. The peak temperature and emission measure and the inferred electron density and plasma volume suggest a very large scale flaring event. The PSPC data were collected over a period of approx. 18 months, allowing us to search for source variability on timescales ranging from less than a day (in the case of flares) to more than a year between individual exposures. On approximately year-long timescales, roughly 25% of the late-type stars are variable. Since the Pleiades was also intensively monitored by the imaging instruments on the Einstein Observatory, we have examined X-ray luminosity variations on the 10 yr timescale between Einstein and ROSAT and find that up to 40% of the late-type stars are X-ray variable. Since there is only marginal evidence for increased variability on decade-long timescales, the variability observed on long and short timescales may have a common physical origin.

Gagne, Marc↗

NASA NTRS2012

Mars Atmospheric CO2 Condensation Above the North and South Poles as Revealed by Radio Occultation, Climate Sounder, and Laser Ranging Observations

We study the condensation of CO2 in Mars atmosphere using temperature profilesretrieved from radio occultation measurements from Mars Global Surveyor (MGS) as wellas the climate sounding instrument onboard the Mars Reconnaissance Orbiter (MRO),and detection of reflective clouds by the MGS Mars Orbiter Laser Altimeter (MOLA). Wefind 11 events in 1999 where MGS temperature profiles indicate CO2 condensation andMOLA simultaneously detects reflective clouds. We thus provide causal evidence thatMOLA non-ground returns are associated with CO2 condensation, which strongly indicatestheir nature being CO2 clouds. The MGS and MRO temperature profiles together revealthe seasonal expansion and shrinking of the area and the vertical extent of atmosphericsaturation. The occurrence rate of atmospheric saturation is maximized at high latitudes inthe middle of winter. The atmospheric saturation in the northern polar region exhibits moreintense seasonal variation than in the southern polar region. In particular, a shrinking ofsaturation area and thickness from LS 270 to 300 in 2007 is found; this is probablyrelated to a planet-encircling dust storm. Furthermore, we integrate the condensation areaand the condensation occurrence rate to estimate cumulative masses of CO2 condensatesdeposited onto the northern and southern seasonal polar caps. The precipitation flux isapproximated by the particle settling flux which is estimated using the impulse responses ofMOLA filter channels. With our approach, the total atmospheric condensation mass canbe estimated from these observational data sets with average particle size as the onlyfree parameter. By comparison with the seasonal polar cap masses inferred from thetime-varying gravity of Mars, our estimates indicate that the average condensate particleradius is 822 mm in the northern hemisphere and 413 mm in the southern hemisphere.Our multi-instrument data analysis provides new constraints on modeling the global climateof Mars.

MOLA↗

NASA NTRS1990

Pioneer-Venus radio occultation (ORO) data reduction: Profiles of 13 cm absorptivity

In order to characterize possible variations in the abundance and distribution of subcloud sulfuric acid vapor, 13 cm radio occultation signals from 23 orbits that occurred in late 1986 and 1987 (Season 10) and 7 orbits that occurred in 1979 (Season 1) were processed. The data were inverted via inverse Abel transform to produce 13 cm absorptivity profiles. Pressure and temperature profiles obtained with the Pioneer-Venus night probe and the northern probe were used along with the absorptivity profiles to infer upper limits for vertical profiles of the abundance of gaseous H2SO4. In addition to inverting the data, error bars were placed on the absorptivity profiles and H2SO4 abundance profiles using the standard propagation of errors. These error bars were developed by considering the effects of statistical errors only. The profiles show a distinct pattern with regard to latitude which is consistent with latitude variations observed in data obtained during the occultation seasons nos. 1 and 2. However, when compared with the earlier data, the recent occultation studies suggest that the amount of sulfuric acid vapor occurring at and below the main cloud layer may have decreased between early 1979 and late 1986.

Steffes, Paul G.↗