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NASA NTRS1972

Large-scale solar magnetic fields and H-alpha patterns

Coronal and interplanetary magnetic fields computed from measurements of large-scale photospheric magnetic fields suffer from interruptions in day-to-day observations and the limitation of using only measurements made near the solar central meridian. Procedures were devised for inferring the lines of polarity reversal from H-alpha solar patrol photographs that map the same large-scale features found on Mt. Wilson magnetograms. These features may be monitored without interruption by combining observations from the global network of observatories associated with NOAA's Space Environment Services Center. The patterns of inferred magnetic fields may be followed accurately as far as 60 deg from central meridian. Such patterns will be used to improve predictions of coronal features during the next solar eclipse.

Mcintosh, P. S.↗
NASA NTRS2001

Portfolios in Stochastic Local Search: Efficiently Computing Most Probable Explanations in Bayesian Networks

Portfolio methods support the combination of different algorithms and heuristics, including stochastic local search (SLS) heuristics, and have been identified as a promising approach to solve computationally hard problems. While successful in experiments, theoretical foundations and analytical results for portfolio-based SLS heuristics are less developed. This article aims to improve the understanding of the role of portfolios of heuristics in SLS. We emphasize the problem of computing most probable explanations (MPEs) in Bayesian networks (BNs). Algorithmically, we discuss a portfolio-based SLS algorithm for MPE computation, Stochastic Greedy Search (SGS). SGS supports the integration of different initialization operators (or initialization heuristics) and different search operators (greedy and noisy heuristics), thereby enabling new analytical and experimental results. Analytically, we introduce a novel Markov chain model tailored to portfolio-based SLS algorithms including SGS, thereby enabling us to analytically form expected hitting time results that explain empirical run time results. For a specific BN, we show the benefit of using a homogenous initialization portfolio. To further illustrate the portfolio approach, we consider novel additive search heuristics for handling determinism in the form of zero entries in conditional probability tables in BNs. Our additive approach adds rather than multiplies probabilities when computing the utility of an explanation. We motivate the additive measure by studying the dramatic impact of zero entries in conditional probability tables on the number of zero-probability explanations, which again complicates the search process. We consider the relationship between MAXSAT and MPE, and show that additive utility (or gain) is a generalization, to the probabilistic setting, of MAXSAT utility (or gain) used in the celebrated GSAT and WalkSAT algorithms and their descendants. Utilizing our Markov chain framework, we show that expected hitting time is a rational function - i.e. a ratio of two polynomials - of the probability of applying an additive search operator. Experimentally, we report on synthetically generated BNs as well as BNs from applications, and compare SGSs performance to that of Hugin, which performs BN inference by compilation to and propagation in clique trees. On synthetic networks, SGS speeds up computation by approximately two orders of magnitude compared to Hugin. In application networks, our approach is highly competitive in Bayesian networks with a high degree of determinism. In addition to showing that stochastic local search can be competitive with clique tree clustering, our empirical results provide an improved understanding of the circumstances under which portfolio-based SLS outperforms clique tree clustering and vice versa.

Mengshoel, Ole J.↗
NASA NTRS1991

Using fuzzy logic to integrate neural networks and knowledge-based systems

Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems.

Yen, John↗
NASA NTRS1973

Velocity structure and evolution of the moon

Seismic data from the Apollo Passive Seismic Network stations are analyzed to determine the velocity structure and to infer the composition and physical properties of the lunar interior. Data from artificial impacts (SIBV booster and LM-ascent stage) cover a distance range of 9 to 1750 km. Travel times and amplitudes, as well as theoretical seismograms, are used to derive a velocity model for the outer 150 km of the moon. The P-wave velocity model confirms an earlier report of a lunar crust in the eastern part of Oceanus Procellarum. The crust is about 60 km thick and may consist of two layers in the mare regions. Possible values for the P-wave velocity in the uppermost mantle are between 7.6 and 9.0 km/sec. The 9 km/sec velocity represents either a localized heterogeneous unit, or a thin layer less than about 40 km in thickness. The elastic properties of the deep interior, as inferred from the seismograms of natural events (meteoroid impacts and moonquakes) occurring at great distances, indicate that there is an increase in attenuation and a possible decrease of velocity at depths below about 1000 km.

Toksoz, M. N.↗
NASA NTRS1989

Can cool loop flows explain 'transition region' redshifts?

Observations of solar active regions reveal extensive areas of redshifted emission lines in the extreme ultraviolet, indicative of down flowing plasma. Redshifts are seen also in the magnetic network of the quiet sun and in magnetically active stars. The inferred velocities are large and appear to represent steady flows. Redshifts are not observed at higher coronal temperatures. It is shown here that steady flow in 'cool' loops driven by the buoyancy force set up by a small heating asymmetry between the two legs of the loop, offers a possible theoretical explanation of the observed features.

Mcclymont, Alexander N.↗
NASA NTRS2000

Monitoring Global Sea Level: Eustatic Variations, Local Variations, and Solid Earth Effects

Project BIFROST (Baseline Inferences for Fennoscandian Rebound Observations. Sea-level and Tectonics) combines networks of continuously operating CPS receivers in Sweden and Finland to measure ongoing crustal deformation due to glacial isostatic adjustment, (CIA). We present an analysis of data collected in the years 1993-1998. We compare the CPS determinations of three-dimensional crustal motion to predictions calculated using the high resolution Fennoscandian deglaciation model recently proposed by Lambeck et al. We find that the the maximum observed uplift rate (approx. 10 mm/ yr) and the maximum predicted uplift rate agree to better than 1 mm/ yr. The patterns of uplift also agree quite well, although differences are discernible. The chi(exp 2) difference between predicted and GPS-observed radial rates is reduced by a factor of 5-6 compared to that for the "null" (no uplift) model, depending on whether a mean difference is first removed. The north components of velocity agree at about the same relative level. whereas the agreement for the east components is worse, a problem possibly related to the lack of bias fixing. We have also compared the values for the observed radial deformation rates to those based on sea-level rates from Baltic tide gauges. The weighted RMS difference between CPS and tide-gauge rates (after removing a mean) is 0.6 mm/ yr, giving an indication of the combined accuracy of the CPS and tide-gauge measurement systems. Spectral analysis of the time series of position estimates yields spectral indices in the range -1 to -2. An EOF analysis indicates, however, that much of this power is correlated among the sites. The correlation appears to be regional and falls off only slightly with distance. Some of this correlated noise is associated with snow accumulation on the antennas or, for those antennas with radomes, on the radomes. This problem has caused us to modify the radomes used several times, leading to one of our more significant sources of uncertainty.

Davis, James L.↗
NASA NTRS2018

Evaluation of Soil Moisture Estimated from IASI Measurements

A simple yet effective scheme to estimate volumetric soil moisture (VSM) using infrared (IR) land surface emissivity retrieved from satellite IR spectral radiance measurements was recently developed, assuming other parameters impacting the radiative transfer (e.g., temperature, vegetation coverage, and surface roughness) are known for an acceptable time and space reference location. This scheme was applied to a decade of global IR emissivity data retrieved from MetOp-A Infrared Atmospheric Sounding Interferometer measurements. Global datasets of monthly-mean 0.25° spatial-grid VSM, estimated from IR emissivity data (denoted as IR-VSM), are assembled to compare with those routinely provided by satellite microwave (MW) multi-sensor measurements (obtained from the ESA CCI website). The IR-VSM dataset is used to demonstrate its spatial variations as well as seasonal-cycles and inter-annual variability which are spatially coherent and consistent with that from MW measurements. Herein we report on some initial evaluation of the IR-VSM absolute values using in-situ measurements available from the Soil Climate Analysis Network of the USDA website. From the 114 ground station locations, we infer a mean bias between monthly-mean in-situ VSM and satellite derived IR-VSM of 0.005 (m3/m3) over the past decade, despite the differences between physical measurements. The absolute accuracy of IR-VSM is also evaluated using an independent source of soil moisture measurements.

Zhou, Daniel K.↗
NASA NTRS2013

An Investigation of the Kinematic and Microphysical Control of Lightning Rate, Extent and NOx Production using DC3 Observations and the NASA Lightning Nitrogen Oxides Model (LNOM)

The Deep Convective Clouds and Chemistry (DC3) experiment seeks to quantify the relationship between storm physics, lightning characteristics and the production of nitrogen oxides via lightning (LNOx). The focus of this study is to investigate the kinematic and microphysical control of lightning properties, particularly those that may govern LNOx production, such as flash rate, type and extent across Alabama during DC3. Prior studies have demonstrated that lightning flash rate and type is correlated to kinematic and microphysical properties in the mixed‐phase region of thunderstorms such as updraft volume and graupel mass. More study is required to generalize these relationships in a wide variety of storm modes and meteorological conditions. Less is known about the co‐evolving relationship between storm physics, morphology and three‐dimensional flash extent, despite its importance for LNOx production. To address this conceptual gap, the NASA Lightning Nitrogen Oxides Model (LNOM) is applied to North Alabama Lightning Mapping Array (NALMA) and Vaisala National Lightning Detection Network(TM) (NLDN) observations following ordinary convective cells through their lifecycle. LNOM provides estimates of flash rate, flash type, channel length distributions, lightning segment altitude distributions (SADs) and lightning NOx production profiles. For this study, LNOM is applied in a Lagrangian sense to multicell thunderstorms over Northern Alabama on two days during DC3 (21 May and 11 June 2012) in which aircraft observations of NOx are available for comparison. The LNOM lightning characteristics and LNOX production estimates are compared to the evolution of updraft and precipitation properties inferred from dual‐Doppler and polarimetric radar analyses applied to observations from a nearby radar network, including the UAH Advanced Radar for Meteorological and Operational Research (ARMOR). Given complex multicell evolution, particular attention is paid to storm morphology, cell mergers and possible dynamical, microphysical and electrical interaction of individual cells when testing various hypotheses.

Carey, Lawrence↗
NASA NTRS1999

A Diagnostic Analysis of the Kennedy Space Center LDAR Network: Data Characteristics - 1

An analytical framework is developed in which to analyze climatological VHF (66 MHz) radiation measurements taken by the Kennedy Space Center Lightning Detection and Ranging (LDAR) network. A 19-month noise-filtered sample of LDAR observations is examined using this framework. It is found that the climatological VHF source density as observed by LDAR falls off approximately 10 dB every 71 km of ground range away from the network centroid (a 31 km e-folding scale). From this framework it is inferred that the underlying source distribution is likely inverse exponential in amplitude, with a log-slope of 126-147 mW(sup -1/2). The underlying vertical distribution of VHF sources is approximately normally distributed with a mean altitude of 9 km and a standard deviation of 2.7 km; this implies that the loss of below-horizon sources has a negligible effect on column-integrated source densities within 200 km ground range. At medium to far ranges, location errors are primarily radial and have a slightly asymmetric distribution whose first moment increases as range squared. Error moments estimated from observed lightning are significantly higher than those from aircraft-based signal generator or analytic solution estimates, suggesting that timing errors arising from poor signal identification and discrimination may dominate over timing errors arising from nominal sensor resolution. The VHF source properties of individual LDAR-observed flashes are computed and an analytic expression for flash detection efficiency vs. range is derived. This reveals nearly constant flash detection efficiency to 80-94 km range from the network centroid.

Boccippio, D. J.↗
NASA NTRS2020

Basaltic Fissure Types on Earth: Suitable Analogs to Evaluate the Origins of Volcanic Terrains on the Moon and Mars?

Basaltic eruptive fissures of the Great Rift and surroundings on the eastern Snake River Plain of Idaho, USA, and selected volcanic features in Hawai`i, Iceland and northern Africa were surveyed for their relevancy as planetary analogs. Evaluated during field investigations and in satellite imagery for structures, physiography, and geologic setting, fissures were categorized into four broad types: (1) simple, monogenetic fissures with obvious volcanic constructs or deposits, (2) monogenetic fissures now obscured by low shields or relatively large cones, (3) polygenetic volcanic rift zones with multiple vents and deposits, and (4) compound regional fissure systems or dike swarms that comprise major rift zones or large volcanic terrains. Using this classification as an initial base, we surveyed imagery of volcanic features for likely fissure vents in two major geologic settings on the Moon: floor-fractured craters (FFCs) and mare and cryptomare provinces. Two major regions on Mars, the volcanic plains around Alba Mons and the greater Tharsis region, were also surveyed for fissure types and volcanic associations of fissure-like features. The planetary surveys suggest that the proposed classification provides a suitable analog starting point to interpret structures associated with fissure systems on the Moon and Mars. With few exceptions, our survey indicates that each of the studied terrains exhibits a dominant fissure type. Type 1 fissures, most with pyroclastic deposits, prevail in lunar FFCs and mare-like regions; whereas type 2 fissures are ubiquitous in the Tharsis region of Mars and a few exist on the Moon as low shields. Type 3 volcanic rift zones are not common on either the Moon or Mars, although they might become evident in future work on chemically evolved terrains. Type 4 fissures are inferred in mare terrains, often represented as the extensions of major linear rille networks or rimae, with possibly complex dike swarms that were buried beneath voluminous mare basalt lava flows. Likewise, numerous flood lavas on Mars are possibly associated with now-obscured or difficult to define type 4 fissure systems.

Scott S. Hughes↗
NASA NTRS2014

The Kinematic and Microphysical Control of Lightning Rate, Extent and NOX Production

The Deep Convective Clouds and Chemistry (DC3) experiment seeks to quantify the relationship between storm physics, lightning characteristics and the production of nitrogen oxides via lightning (LNOx). The focus of this study is to investigate the kinematic and microphysical control of lightning properties, particularly those that may govern LNOx production, such as flash rate, type and extent across Alabama during DC3. Prior studies have demonstrated that lightning flash rate and type is correlated to kinematic and microphysical properties in the mixed-phase region of thunderstorms such as updraft volume and graupel mass. More study is required to generalize these relationships in a wide variety of storm modes and meteorological conditions. Less is known about the co-evolving relationship between storm physics, morphology and three-dimensional flash extent, despite its importance for LNOx production. To address this conceptual gap, the NASA Lightning Nitrogen Oxides Model (LNOM) is applied to North Alabama Lightning Mapping Array (NALMA) and Vaisala National Lightning Detection Network(TM) (NLDN) observations following ordinary convective cells through their lifecycle. LNOM provides estimates of flash rate, flash type, channel length distributions, lightning segment altitude distributions (SADs) and lightning NOx production profiles. For this study, LNOM is applied in a Lagrangian sense to multicell thunderstorms over Northern Alabama on two days during DC3 (21 May and 11 June 2012) in which aircraft observations of NOx are available for comparison. The LNOM lightning characteristics and LNOX production estimates are compared to the evolution of updraft and precipitation properties inferred from dual-Doppler and polarimetric radar analyses applied to observations from a nearby radar network, including the UAH Advanced Radar for Meteorological and Operational Research (ARMOR). Given complex multicell evolution, particular attention is paid to storm morphology, cell mergers and possible dynamical, microphysical and electrical interaction of individual cells when testing various hypotheses.

Carey, Lawrence↗
NASA NTRS2014

A Corrected Formulation of the Multilayer Model (MLM) for Inferring Gaseous Dry Deposition to Vegetated Surfaces

The Multilayer Model (MLM) has been used for many years to infer dry deposition fluxes from measured trace species concentrations and standard meteorological measurements for national networks in the U.S., including the U.S. Environmental Protection Agency's Clean Air Status and Trends Network (CASTNet). MLM utilizes a resistance analogy to calculate deposition velocities appropriate for whole vegetative canopies, while employing a multilayer integration to account for vertically varying meteorology, canopy morphology and radiative transfer within the canopy. However, the MLM formulation, as it was originally presented and as it has been subsequently employed, contains a non-physical representation related to the leaf-level quasi-laminar boundary layer resistance that affects the calculation of the total canopy resistance. In this note, the non-physical representation of the canopy resistance as originally formulated in MLM is discussed and a revised, physically consistent, formulation is suggested as a replacement. The revised canopy resistance formulation reduces estimates of HNO3 deposition velocities by as much as 38% during mid-day as compared to values generated by the original formulation. Inferred deposition velocities for SO2 and O3 are not significantly altered by the change in formulation (less than 3%). Inferred deposition loadings of oxidized and total nitrogen from CASTNet data may be reduced by 10-20% and 5-10%, respectively, for the Eastern U. S. when employing the revised formulation of MLM as compared to the original formulation.

vegetated surfaces↗
NASA NTRS1990

3-D components of a biological neural network visualized in computer generated imagery. II - Macular neural network organization

Computer-assisted reconstructions of small parts of the macular neural network show how the nerve terminals and receptive fields are organized in 3-dimensional space. This biological neural network is anatomically organized for parallel distributed processing of information. Processing appears to be more complex than in computer-based neural network, because spatiotemporal factors figure into synaptic weighting. Serial reconstruction data show anatomical arrangements which suggest that (1) assemblies of cells analyze and distribute information with inbuilt redundancy, to improve reliability; (2) feedforward/feedback loops provide the capacity for presynaptic modulation of output during processing; (3) constrained randomness in connectivities contributes to adaptability; and (4) local variations in network complexity permit differing analyses of incoming signals to take place simultaneously. The last inference suggests that there may be segregation of information flow to central stations subserving particular functions.

Ross, Muriel D.↗
NASA NTRS2007

Variations in Stratospheric Inorganic Chlorine Between 1991 and 2006

So how quickly will the ozone hole recover? This depends on how quickly the chlorine content (Cl2) of the atmosphere will decline. The ozone hole forms over the Antarctic each southern spring (September and October). The extremely small ozone amounts in the ozone hole are there because of chemical reactions of ozone with chlorine. This chlorine originates largely from industrially produced chlorofluorocarbon (CFC) compounds. An international agreement, the Montreal Protocol, is drastically reducing the amount of chlorine-containing compounds that we are releasing into the atmosphere. To be able to attribute changes in stratospheric ozone to changes in chlorine we need to know the distribution of atmospheric chlorine. However, due to a lack of continuous observations of all the key chlorine gases, producing a continuous time series of stratospheric chlorine has not been achieved to date. We have for the first time devised a technique to make a 17-year time series for stratospheric chlorine that uses the long time series of HCl observations made from several space borne instruments and a neural network. The neural networks allow us to both inter-calibrate the various HCl instruments and to infer the total amount of atmospheric chlorine from HCl. These new estimates of Cl, provide a much needed critical test for current global models that currently predict significant differences in both Cl(sub y) and ozone recovery. These models exhibit differences in their projection of the recovery time and our chlorine content time series will help separate the good from the bad in these projections.

Lary, D. J.↗
NASA NTRS2019

Trends and Seasonal Variability of Atmospheric NO2 and HNO3 Concentrations Across Three Major African Biomes Inferred from Long-Term Series of Ground-Based and Satellite Measurements

In the framework of the INDAAF (International Network to study Deposition and Atmospheric chemistry in AFrica) programme, part of the ACTRIS (Aerosols, Clouds, and Trace gases Research InfraStructure), European Research Infrastructure for the long-term observation of Aerosol, Cloud, and Trace gases, this paper aims to study trends and seasonal variability of surface atmospheric NO2 and HNO3 concentrations, and OMI (Ozone Monitoring Instrument) NO2 over 6 sites in tropical Africa. Sites are located in west and central Africa to represent the major African biomes: dry savanna (Banizoumbou, Niger and Katibougou, Mali), wet savanna (Djougou, Benin and Lamto, Côte d’Ivoire) and forest (Bomassa, Republic of Congo and Zoétélé, Cameroon). Ground-based NO2 and HNO3 concentrations were obtained over the period 1998–2015 using INDAAF passive samplers at a monthly basis, and NO2 Vertical Column Densities (VCDs) from OMI for a 1-degree grid cell around each sites were obtained from 2005 to 2015. Mean annual NO2 concentrations ranged from 2.3 plus or minus 1.2 to 0.9 plus or minus 0.4 parts per billion (ppb) from dry savannas to forests, representing a north-south gradient. In dry savannas, we observe two concentration peaks of NO2 appearing at the beginning and the end of the wet season both for ground-based and satellite measurements, whereas at wet savannas and forest sites, NO2 concentrations are highest in the dry season. The seasonality of surface NO2 observations provide further evidence for a large role of microbial soil NOx emissions in dry savannas and of biomass burning NOx emissions in wet savanna and forest sites. Mean annual HNO3 concentrations ranged from 0.4 to 0.5 ppb in dry and wet savannas to 0.2 to 0.3 ppb in forest. In dry ecosystems, higher HNO3 concentrations are measured in the early wet season, consistent with NO2 results. The analysis of a long-term dataset of surface O3 concentrations indicates that HNO3 production can mainly be explained by the photo-oxidation of NOx. Mann-Kendall and Seasonal Kendall statistical tests showed that NO2 surface concentrations have a significant decreasing seasonal and annual trends at multiple sites (p-value less than 0.05) ranging from -2.96 percent per year (at Zoétélé) to -0.64 percent per year (at Banizoumbou). HNO3 results indicate no trends except at Bomassa (1.07 percent per year). The decreasing NO2 ground-based concentration trends observed in wet savannas sites are correlated with OMI NO2 decreasing trends at these sites. Trends obtained for NO2 concentrations in wet savanna and forest ecosystems are consistent with trends of NOₓ biomass burning emissions.

Trends↗
NASA NTRS2019

Using Deep Learning to Automate Inference of Meteoroid Pre-Entry Properties

Properly assessing the asteroid threat depends on the knowledge of asteroid pre-entry parameters, such as size, velocity, mass, density, and strength. Although a vast number of possible bodies to study exist, such characterization of asteroid populations is currently limited by substantial costs associated with space rendezvous missions and rare meteorite findings. As asteroids fragment, ablate, and decelerate in the atmosphere, they emit light detectable by ground-based and space-borne instruments. Earth’s atmosphere, thus, becomes an accessible laboratory that enables impactor risk assessments by facilitating inference of the pre-entry parameters. These asteroid pre-entry conditions are typically deduced by modeling the entry and breakup physics that best reproduce the observed light or energy deposition curve. However, this process requires extensive manual trial-and-error of uncertain modeling parameters. Automating meteor modeling and inference would improve property distributions used in risk assessments and enable population characterization as more light curves become more readily available through the presence of space assets and ground-based camera networks. We previously developed a genetic algorithm to automate meteor modeling by using the fragment-cloud model (FCM) to search for the values of the FCM input parameters (e.g., diameter) that generate energy deposition profiles that match the observed one. Now, we apply deep learning to infer asteroid diameter, velocity, and density from observed energy deposition curves. We trained and tested our neural network models with synthetic energy deposition curves modeled using the FCM rubble pile implementation. We present an application of a 1D convolutional neural network and compare its performance to other attempted regressors and machine learning techniques, such as a fully connected neural network and Random Forest regression, to demonstrate its capabilities. We validate our model weights and approach using the Chelyabinsk, Tagish Lake, Benešov, Košice, and Lost City meteors.

Tarano, Ana Maria↗