Results for “Source Term Analysis” — Page 26

NASA NTRS2001

Long-Term Variability of Airborne Asian Dust Observed from TOMS

Recent studies suggest that airborne Asian dust may not only play an important role in the regional radiation budget, but also influence the air quality over North America through long-range transport. In this paper, we use satellite data to investigate the long-term variability of airborne Asian dust as well as the daily variation of the dust aerosol distribution. By combining the Total Ozone Mapping Spectrometer (TOMS) aerosol index with National Centers for Environmental Prediction (NCEP) wind data, our analysis shows a strong correlation between the generation of dust storms in the region and the passage of springtime weather fronts. This is consistent with earlier studies performed by other researchers. According to both the Nimbus-7 and Earth-Probe TOMS data the Takla Makan desert, the Gobi desert, and the and region of Inner Mongolia are major sources of the eastward-flowing airborne Asian dust. Heavily populated areas in eastern China (e.g., Beijing) are often on the primary path of the dust storms originating in these desert regions. The increasing desertification north of the Beijing region has served to exacerbate problems stemming from these storms. The time series derived from 20 years of TOMS aerosol index data shows the first significant satellite evidence of the atmospheric effect of increasing desertification, indicating that the amount of dust blown eastward has increased strongly during the past few years including the year 2000.

Herman, J. R.↗

NASA NTRSDate not supplied

Efficient Parametric Uncertainty Analysis of an Earth Entry Vehicle Concept Using Least Angle Regression

The objective of this work was to outline and apply an efficient and accurate parametric un-certainty propagation approach to the analysis of convective heating on an Earth entry vehicle concept. The described approach was based on Least Angle Regression used to solve a sparse and underdetermined linear system in the point-collocation non-intrusive polynomial chaos surrogate method. This approach involved an iterative process to computing the non-zero terms of the underlying polynomial chaos model using only enough samples to converge uncertainty interval predictions and Sobol index values based global nonlinear sensitivity estimates. The Earth entry vehicle was analyzed at three points along a representative trajectory for a Mars return mission. 329 sources of uncertainty were identified in the computational fluid dynamics model used to predict the forebody convective heating. These included uncertainty in flow field chemical rates, collision integrals, heats of formation, surface finite rate char model reaction rates, wall roughness height, and the turbulent Schmidt number. Results from this study showed that convective heating uncertainty as high as 50% of the nominal was predicted with only about 50 evaluations of the computational model. This was far fewer than would be required for a sampling-based approach or a full basis polynomial chaos model, which would have required over 50,000 samples. Additionally, results showed that over 90% of the total convective heating uncertainty was due to uncertainty in the N2catalytic rate on the surface, while the remainder of the uncertainty was attributed to the turbulent Schmidt number and the wall roughness uncertainties.

Thomas K West IV↗

NASA NTRS2005

Separating the Chemical and Dynamical Contributions to the Ozone Change

Statistical analysis is used to extract the sensitivity of ozone to changes in stratospheric chlorine due to emissions of chlorofluorcarbons from the observed ozone record. The statistical analysis relies on a model that accounts for natural variations in ozone including the seasonal cycle, the solar cycle, variations in aerosols due to volcanic eruption, and the quasi- biennial oscillation, A noise term includes contributions to ozone variability due to inter-annual variability in the stratospheric circulation not due to the quasi-biennial oscillation, The residual circulation varies due to variability in planetary wave forcing, and studies using meteorological analyses show that the build-up of ozone over the winter is correlated with the planetary wave Eliassen-Palm flux. This variability in the residual circulation is not included in the statistical model, and contributes to the apparent ozone sensitivity to chlorine derived from observations for 1979 - 2000. We have investigated these relationships using multi-decadal simulations of our off-line chemistry and transport model (CTM). Our simulations use meteorological fields output from a 50 year simulation of a general circulation model (GCM). A 50 climatology specifies the sea surface temperatures to produce the GCM simulation. The CTM was used with these winds to produce two simulations, one in which the boundary conditions for chlorofluorcarbons and other source gases vary as specified for 1973-2022 by the Scenario A2 of the World Meteorological Organization ozone assessment, and the second with source gases fixed to their 1979 values. The same statistical analysis used to derive trends from observations is applied to the CTM output. When applied to the difference between the two simulations the statistical analysis provides a more precise measure of the ozone sensitivity to chlorine change. We are testing ways of including the interannual variability in the residual circulation in the statistical model so that we can derive the same result from the Scenario A2 simulation as is obtained when the statistical analysis is applied to the difference between the two simulations. This should provide direction as to how to account for the changes in ozone due to interannual variability in the residual circulation in the statistical model that is applied to the observed data record.

Douglass, Anne R.↗

NASA NTRS1980

Analysis of aging data on SAW oscillators

Test oscillators including delay lines and resonators in the 300-400 MHz range have been investigated with reference to the long-term stability (aging) characteristics. All devices were fabricated on rotated Y-cut quartz plates (40 deg), and either gold or platinum metallization was used; all packages were high-quality hermetic enclosures, and the mounting was strictly mechanical, with no organics or silicone rubbers used. It is shown that drift of less than 2 ppm in the first year can be obtained on a significant fraction of the devices when reasonably clean packages are used. The data also suggest that the transducer metallization (at least for aluminum) is very likely the source of the relaxation mechanism that causes the frequency drift.

Parker, T. E.↗

NASA NTRS1984

Io's sodium directional features - Evidence for a magnetospheric-wind-driven gas escape mechanism

Elongated features in Io's sodium cloud, directed away from Jupiter and inclined both to the north and to the south of the satellite's orbital plane, have been observed. The north/south directions of the features are correlated with Io's magnetic longitude, suggesting a formation mechanism involving the oscillating plasma torus. It is shown by means of a model analysis that the features can result from a source of high-velocity (about 20 km/s) sodium combined with the oscillating neutral sodium sink provided by the plasma. The phase relationship between the features' directions and Io's magnetic longitude can be understood if escaping sodium is initially directed at near right angles to Io's orbital motion. The directionality of the features requires that the sodium flux from equatorial regions be higher than that from the poles. The initial directions and speeds of sodium atoms escaping Io to form the directional features can be understood in terms of a magnetospheric-wind-driven escape mechanism. The one sequence of directional feature observations that has been analyzed in detail implies a high-speed sodium source rate of about 10 to the 26th atoms/s.

Pilcher, C. B.↗

NASA NTRSDate not supplied

Insights into Long-Term Global Precipitation from IMERG and GPCP

Multiple satellite-based datasets provide estimates of the long-term record of global precipitation. Each necessarily includes both the real atmospheric behavior and a collection of artifacts driven by the input data sources and design choices in the retrievals and dataset construction. For this presentation, the Integrated Multi-satellitE Retrievals for GPM (IMERG) products from the Global Precipitation Measurement (GPM) mission’s U.S. Science Team are designed as a modern High-Resolution Precipitation Product (HRPP), while the Global Precipitation Climatology Project’s (GPCP) products are designed as a modern Climate Data Record (CDR). Although they share some common input data sources, the computational process for each is rather different. Comparing the precipitation estimates from each algorithm gives us insights into likely artifacts and the natural variations that are common to both. The goal of an HRPP is to give the best estimate of precipitation at each time step in the dataset, generally meaning that “all possible available” data are used. In contrast, the CDR is intended to provide a precipitation record that has relatively homogeneous statistics, necessary for climate analysis. Each analysis strives for both goals, of course, but this means that the HRPP uses the disparate collection of satellites whose statistics don’t quite match the ideal record, while the CDR is computed from fewer satellites that provide a relatively homogeneous set of precipitation statistics. This presentation employs the current versions of the IMERG and GPCP products, V07B and V3.2, respectively. A variety of analyses have demonstrated that IMERG has more artifacts, as expected, which tend to result from changes in the source of intercalibration for the various input satellites, namely the Combined Radar-Radiometer Algorithm using TMI and PR during the TRMM era and using GMI and DPR-Ku during the GPM era. As well, a shift occurs when the altitudes of the TRMM and GPM satellites change as a result of orbit boosts. For the most part the mean precipitation shows good continuity across these boundaries, but there are noticeable changes in the respective histograms. The latter has taken on more importance in recent years due to increased scrutiny on extremes which implicitly focus on the upper end of the precipitation histograms. The GPCP product is more homogeneous, with only one major change in calibrator, transitioning from the SSMI series of satellites to the SSMIS series of satellites in 2009. The GPCP analysis does a good job of minimizing artifacts in the means at this boundary, while the histograms show a smaller, but still noticeable shift in the histograms, a result that is similar to data boundaries in IMERG. The GPCP Daily, which is a month-by-month rescaling of the daily accumulated IMERG Final product, shows more consistency than the equivalent daily IMERG, but inherits the histogram shifts from IMERG. Given this overlay of artifacts, there is enough consistency between the products to illustrate some important long-term variations, including interannual variations, not all of which are easily attributable to ENSO events, and trends that are large regionally, but comparatively small when averaged across the globe.

precipitation↗

NASA NTRS2010

Modeling Flight: The Role of Dynamically Scaled Free-Flight Models in Support of NASA's Aerospace Programs

The state of the art in aeronautical engineering has been continually accelerated by the development of advanced analysis and design tools. Used in the early design stages for aircraft and spacecraft, these methods have provided a fundamental understanding of physical phenomena and enabled designers to predict and analyze critical characteristics of new vehicles, including the capability to control or modify unsatisfactory behavior. For example, the relatively recent emergence and routine use of extremely powerful digital computer hardware and software has had a major impact on design capabilities and procedures. Sophisticated new airflow measurement and visualization systems permit the analyst to conduct micro- and macro-studies of properties within flow fields on and off the surfaces of models in advanced wind tunnels. Trade studies of the most efficient geometrical shapes for aircraft can be conducted with blazing speed within a broad scope of integrated technical disciplines, and the use of sophisticated piloted simulators in the vehicle development process permits the most important segment of operations the human pilot to make early assessments of the acceptability of the vehicle for its intended mission. Knowledgeable applications of these tools of the trade dramatically reduce risk and redesign, and increase the marketability and safety of new aerospace vehicles. Arguably, one of the more viable and valuable design tools since the advent of flight has been testing of subscale models. As used herein, the term "model" refers to a physical article used in experimental analyses of a larger full-scale vehicle. The reader is probably aware that many other forms of mathematical and computer-based models are also used in aerospace design; however, such topics are beyond the intended scope of this document. Model aircraft have always been a source of fascination, inspiration, and recreation for humans since the earliest days of flight. Within the scientific community, Leonardo da Vinci, George Cayley, and the Wright brothers are examples of early aviation pioneers who frequently used models during their scientific efforts to understand and develop flying machines. Progress in the technology associated with model testing in worldwide applications has firmly established model aircraft as a key element in new aerospace research and development programs. Models are now routinely used in many applications and roles, including aerodynamic data gathering in wind tunnel investigations for the analysis of full-scale aircraft designs, proof-of-concept demonstrators for radical aeronautical concepts, and problem-solving exercises for vehicles already in production. The most critical contributions of aerospace models are to provide confidence and risk reduction for new designs and to enhance the safety and efficiency of existing configurations.

Chambers, Joseph↗

NASA NTRS1973

Rotor noise due to inflow turbulence.

The interaction of turbulent velocity fluctuations of the inflow with rotor blades and the resulting acoustic radiation is examined in terms of: dipole sources due to blade loading, monopole sources due to thickness of the rotating blades, and quadrupole sources due to velocity fluctuations in the flow region. Depending upon the length and time scales of the turbulence, sufficient number of blades may interact with a coherent eddy to radiate noise with discrete peaks at blade passing frequency and its harmonics. Theoretical estimates of acoustic radiation to on-axis points from a typical subsonic rotor are presented, and it is noted that the discrete tone noise is primarily from dipole radiation. Discrete tone noise levels observed in narrow band analysis of test data show reasonable agreement with analytical predictions.

Rao, G. V. R.↗

NASA NTRS1976

Orbital precession in Hercules X-1

It is noted that the 35-day X-ray high-low cycle of Her X-1 could be explained in terms of periodic obscuration of the source by a tilted and precessing 'slaved' accretion disk, which would require precession of the mass-shedding star and, consequently, a precessing binary orbit. Four months of Uhuru data obtained in 1972 are analyzed to determine whether the orbital precession can be detected as a Doppler modulation in the arrival times of the 1.24-sec X-ray pulsations. The precession geometry is examined, the test for precession is outlined, and a marginally significant indication of orbital precession in the Hz Her/Her X-1 system is obtained. Possible biases in the analysis are evaluated, and the spin-axis misalignment of HZ Her is estimated from the measured tilt of the orbit.

Deeter, J. E.↗

NASA NTRS1990

Performance analysis of a generalized concurrent error detection procedure

A general procedure for error detection in complex systems, called the data block capture and analysis monitoring process, is described and analyzed. It is assumed that, in addition to being exposed to potential external fault sources, a complex system will in general always contain embedded hardware and software fault mechanisms which can cause the system to perform incorrect computations and/or produce incorrect output. Thus, in operation, the system continuously moves back and forth between error and no-error states. These external fault sources or internal fault mechanisms are extremely difficult to detect. The data block capture and analysis monitoring process is concerned with detecting deviations from the normal performance of the system, known as errors, which are symptomatic of fault conditions. The process consists of repeatedly recording a fixed amount of data from a set of predetermined observation lines of the system being monitored (i.e., capturing a block of data) and then analyzing the captured block in an attempt to determine whether the system is functioning correctly. The performances of linear, quadratic, and logarithmic data analysis algorithms are rigorously characterized in terms of the probability of correctly detecting an error, the expectation and variance of the number of false alarms per error, and the expectation and variance of the latency in detection of errors. Insight into the nature of the general problem of error detection is obtained.

Blough, Douglas M.↗

NASA NTRS1993

Threshold effects on V/V(max) for gamma-ray bursts

The interpretation of the observed gamma-ray burst V/V(max) statistic in terms of spatial distributions is model-dependent. Detection of gamma-ray bursts requires the counting rate in one or more detectors to exceed a threshold C(lim) determined from a time-dependent background rate B(t). The sampling depth of the burst detector is thus time-dependent, and, if burst sources are nonuniform in space, the observed V/V(max) distribution will be affected by B(t). We demonstrate this effect with a simple geometric distribution of standard candles and argue that V/V(max) statistic without information on threshold variations is insufficient for rigorous data analysis. Peak count rates and threshold values must be given separately for all events in order to facilitate a meaningful comparison of observations with theoretical distribution models.

Hartmann, D. H.↗

NASA NTRS1992

The EXOSAT database and archive

The EXOSAT database provides on-line access to the results and data products (spectra, images, and lightcurves) from the EXOSAT mission as well as access to data and logs from a number of other missions (such as EINSTEIN, COS-B, ROSAT, and IRAS). In addition, a number of familiar optical, infrared, and x ray catalogs, including the Hubble Space Telescope (HST) guide star catalog are available. The complete database is located at the EXOSAT observatory at ESTEC in the Netherlands and is accessible remotely via a captive account. The database management system was specifically developed to efficiently access the database and to allow the user to perform statistical studies on large samples of astronomical objects as well as to retrieve scientific and bibliographic information on single sources. The system was designed to be mission independent and includes timing, image processing, and spectral analysis packages as well as software to allow the easy transfer of analysis results and products to the user's own institute. The archive at ESTEC comprises a subset of the EXOSAT observations, stored on magnetic tape. Observations of particular interest were copied in compressed format to an optical jukebox, allowing users to retrieve and analyze selected raw data entirely from their terminals. Such analysis may be necessary if the user's needs are not accommodated by the products contained in the database (in terms of time resolution, spectral range, and the finesse of the background subtraction, for instance). Long-term archiving of the full final observation data is taking place at ESRIN in Italy as part of the ESIS program, again using optical media, and ESRIN have now assumed responsibility for distributing the data to the community. Tests showed that raw observational data (typically several tens of megabytes for a single target) can be transferred via the existing networks in reasonable time.

Reynolds, A. P.↗

NASA NTRS2000

Modeling Natural Space Ionizing Radiation Effects on External Materials

Predicting the effective life of materials for space applications has become increasingly critical with the drive to reduce mission cost. Programs have considered many solutions to reduce launch costs including novel, low mass materials and thin thermal blankets to reduce spacecraft mass. Determining the long-term survivability of these materials before launch is critical for mission success. This presentation will describe an analysis performed on the outer layer of the passive thermal control blanket of the Hubble Space Telescope. This layer had degraded for unknown reasons during the mission, however ionizing radiation (IR) induced embrittlement was suspected. A methodology was developed which allowed direct comparison between the energy deposition of the natural environment and that of the laboratory generated environment. Commercial codes were used to predict the natural space IR environment model energy deposition in the material from both natural and laboratory IR sources, and design the most efficient test. Results were optimized for total and local energy deposition with an iterative spreadsheet. This method has been used successfully for several laboratory tests at the Marshall Space Flight Center. The study showed that the natural space IR environment, by itself, did not cause the premature degradation observed in the thermal blanket.

Alstatt, Richard L.↗

NASA NTRSDate not supplied

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

J Lemery↗

NASA NTRS2024

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗

NASA NTRS2023

Modeling Dust Mineralogical Composition: Sensitivity to Soil Mineralogy Atlases and Their Expected Climate Impacts

Soil dust aerosols are a key component of the climate system, as they interact with short- and long-wave radiation, alter cloud formation processes, affect atmospheric chemistry and play a role in biogeochemical cycles by providing nutrient inputs such as iron and phosphorus. The influence of dust on these processes depends on its physicochemical properties, which, far from being homogeneous, are shaped by its regionally varying mineral composition. The relative amount of minerals in dust depends on the source region and shows a large geographical variability. However, many state-of-the-art Earth system models (ESMs), upon which climate analyses and projections rely, still consider dust mineralogy to be invariant. The explicit representation of minerals in ESMs is more hindered by our limited knowledge of the global soil composition along with the resulting size-resolved airborne mineralogy than by computational constraints. In this work we introduce an explicit mineralogy representation within the state-of-the-art Multiscale Online Nonhydrostatic AtmospheRe CHemistry (MONARCH) model. We review and compare two existing soil mineralogy datasets, which remain a source of uncertainty for dust mineralogy modeling and provide an evaluation of multiannual simulations against available mineralogy observations. Soil mineralogy datasets are based on measurements performed after wet sieving, which breaks the aggregates found in the parent soil. Our model predicts the emitted particle size distribution (PSD) in terms of its constituent minerals based on brittle fragmentation theory (BFT), which reconstructs the emitted mineral aggregates destroyed by wet sieving. Our simulations broadly reproduce the most abundant mineral fractions independently of the soil composition data used. Feldspars and calcite are highly sensitive to the soil mineralogy map, mainly due to the different assumptions made in each soil dataset to extrapolate a handful of soil measurements to arid and semi-arid regions worldwide. For the least abundant or more difficult-to-determine minerals, such as iron oxides, uncertainties in soil mineralogy yield differences in annual mean aerosol mass fractions of up to ∼ 100 %. Although BFT restores coarse aggregates including phyllosilicates that usually break during soil analysis, we still identify an overestimation of coarse quartz mass fractions (above 2 µm in diameter). In a dedicated experiment, we estimate the fraction of dust with undetermined composition as given by a soil map, which makes up ∼ 10 % of the emitted dust mass at the global scale and can be regionally larger. Changes in the underlying soil mineralogy impact our estimates of climate-relevant variables, particularly affecting the regional variability of the single-scattering albedo at solar wavelengths or the total iron deposited over oceans. All in all, this assessment represents a baseline for future model experiments including new mineralogical maps constrained by high-quality spaceborne hyperspectral measurements, such as those arising from the NASA Earth Surface Mineral Dust Source Investigation (EMIT) mission.

Soil dust↗

NASA NTRS1986

Review of methodology and technology available for the detection of extrasolar planetary systems

Anyone undertaking an interstellar voyage might wish to be assured of the existence of a safe planetary harbor at the other end! Aside from the obvious interest of the participants in this Symposium, astronomers and astrophysicists are also eager to detect and study other planetary systems in order to better understand the formation of our own Solar System. Scientists involved in the search for extraterrestrial intelligence argue that planets suitable for the evolution of life may abound elsewhere within our own Milky Way Galaxy. On theoretical grounds, they are probably correct, but they lack any observational support. For in spite of decades of claimed astrometric detections of planetary companions and the recent exciting and tantalizing observations from the IRAS satellite and the IR speckle observations of Van Biesbroeck 8 and other cool stars, there is no unambiguous proof for the existence of another planetary system beyond our own. In this paper we review the various methods for detecting extrasolar planets and briefly describe the Earth and space based technology currently available and discuss the near-term plans to implement these different search techniques. In each case an attempt is made to identify the limiting source of systematic error inherent to the methodology and to assess the potential for technological improvements.

Non-NASA Center↗

NASA NTRS1985

Long term monitoring of methane in the atmosphere by multiplex gas chromatography

Methane is of interest in the study of the Earth's atmosphere because of its implication in the future global warming of the surface. This warming is produced by the absorption of infrared energy by trace gases. It has been estimated that in the next 40 to 50 years, methane could contribute 20 to 25 pct. as much atmospheric warming as that expected from carbon dioxide increases. Studies to examine sources, sinks, and cycles of methane will require analytical methods capable of continuous unattended measurement with temporal resolution of an hour or less for weeks at a time. Gas chromatography (GC) is one of the most practical methods available to conduct the analysis of air, but limitations in this technique still exist which can be alleviated with multiplex GC (MGC). MGC is a technique where many samples are pseudo-randomly introduced to the chromatograph without regard to the length of time required for an analysis. The resulting data must then be reduced using computational methods such as cross correlation. In the technique reported, a tube packed with silver oxide was used at the inlet of the GC column to create concentration pulses of methane in a sample stream of air. By using only one carrier, i.e., ambient air, an effective and accurate method to monitor the variations in concentration of methane in the atmosphere over long periods of time was developed. Methane in ambient air was monitored for an eight day period and an interesting temporal variability was found. This work has shown the utility of a relatively simple MGC for the analysis of a real environmental sample.

Valentin, Jose R.↗