Building modern cometary models using ancient Chinese data
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Building Modern Cometary Models Using Ancient Chinese Data
For more than two millennia, ancient Chinese court astronomers maintained a rather comprehensive record of cometary sightings.
cometary models astrological predictions periodic ↗
Building access and community standards for opacity data at the onset of next-generation atmosphere observations
The characterization of a diverse set of exoplanet atmosphere observations, ranging from hot gas giants to small temperate rocky worlds, will be one of the legacies of upcoming facilities such as the James Webb Space Telescope (JWST). Our understanding and interpretation of such observations will hinge on our ability to link observations with atmospheric theoretical studies that critically rely on fundamental molecular and atomic opacities. Computing such opacities is a highly non-trivial and inaccessible process which requires several terabytes of available disk space, hours of CPU time per pressure-temperature combination, and requires users to carefully aggregate line lists data from various sources, which limits access and intercomparison of opacity data in the exoplanet community. Here we present MAESTRO (Molecules and Atoms in Exoplanet Science: Tools and Resources for Opacities) an opacity database that can be accessed by the community via a web interface and python API. MAESTRO was built with community input to create a version-controlled opacity database that is easily queryable, includes informative metadata to ensure reproducibility, and exports relevant citations for inclusion in publications. Scheduled for community release in 2022, MAESTRO will prove to be an invaluable community resource in the era of JWST and beyond.
Natasha Batalha↗
Human and Robotic Mission to Small Bodies: Mapping, Planning and Exploration
This study investigates the requirements, performs a gap analysis and makes a set of recommendations for mapping products and exploration tools required to support operations and scientific discovery for near- term and future NASA missions to small bodies. The mapping products and their requirements are based on the analysis of current mission scenarios (rendezvous, docking, and sample return) and recommendations made by the NEA Users Team (NUT) in the framework of human exploration. The mapping products that sat- isfy operational, scienti c, and public outreach goals include topography, images, albedo, gravity, mass, density, subsurface radar, mineralogical and thermal maps. The gap analysis points to a need for incremental generation of mapping products from low (flyby) to high-resolution data needed for anchoring and docking, real-time spatial data processing for hazard avoidance and astronaut or robot localization in low gravity, high dynamic environments, and motivates a standard for coordinate reference systems capable of describing irregular body shapes. Another aspect investigated in this study is the set of requirements and the gap analysis for exploration tools that support visualization and simulation of operational conditions including soil interactions, environment dynamics, and communications coverage. Building robust, usable data sets and visualisation/simulation tools is the best way for mission designers and simulators to make correct decisions for future missions. In the near term, it is the most useful way to begin building capabilities for small body exploration without needing to commit to specific mission architectures.
Neffian, Ara V.↗
A global change data base using Thematic Mapper data - Earth Monitoring Educational System (EMES)
Some of the main directions in creating an education program in earth system science aimed at combining top science and technology with high academic performance are presented. The creation of an Earth Monitoring Educational System (EMES) integrated with the research interests of the NASA Ames Research Center and one or more universities is proposed. Based on the integration of a global network of cooperators to build a global data base for assessments of global change, EMES would promote degrees at all levels in global ecology at associated universities and colleges, and extracurricular courses for multilevel audiences. EMES objectives are to: train specialists; establish a tradition of solving regional problems concerning global change in a systemic manner, using remote sensing technology as the monitoring tool; and transfer knowledge on global change to the national and world communities. South America is proposed as the pilot continent for the project.
D'Antoni, Hector L.↗
Monitoring Regional Forest Disturbances across the US with near Real Time MODIS NDVI Products Resident to the ForWarn Forest Threat Early Warning System
Forest threats across the US have become increasingly evident in recent years. Sometimes these have resulted in regionally evident disturbance progressions (e.g., from drought, bark beetle outbreaks, and wildfires) that can occur across multiyear durations and have resulted in extensive forest overstory mortality. In addition to stand replacement disturbances, other forests are subject to ephemeral, sometimes yearly defoliation from various insects and varying types and intensities of ephemeral damage from storms. Sometimes, after prolonged severe disturbance, signs of recovery in terms of Normalized Difference Vegetation Index (NDVI) can occur. The growing prominence and threat of forest disturbances in part have led to the formation and implementation of the 2003 Healthy Forest Restoration Act which mandated that national forest threat early warning system be developed and deployed. In response, the US Forest Service collaborated with NASA, DOE Oakridge National Laboratory, and the USGS Eros Data Center to build and roll-out the near real time ForWarn early warning system for monitoring regionally evident forest disturbances. Given the diversity of disturbance types, severities, and durations, ForWarn employs multiple historical baselines that are used with current NDVI to derive a suite of six forest change products that are refreshed every 8 days. ForWarn employs daily quarter kilometer MODIS NDVI data from the Aqua and Terra satellites, including MOD13 data for deriving historical baseline NDVIs and eMODIS 7 NDVI for compiling current NDVI. In doing so, the Time Series Product Tool and the Phenological Parameters Estimation Tool are used to temporally de-noise, fuse, and aggregate current and historical MODIS NDVIs into 24 day composites refreshed every 8 days with 46 dates of products per year. The 24 day compositing interval enables disturbances to be detected, while minimizing the frequency of residual atmospheric contamination. Forest change products are computed versus the previous 1, previous 3, and all previous years in the MODIS record for a given 24 day interval. Other "weekly" forest change products include one computed using an adaptive length compositing method for quicker detection of disturbances, two others that adjust for seasonal fluctuations in normal vegetation phenology (e.g., early versus late springs). This overall approach enables forest disturbance dynamics from a variety of regionally evident biotic and abiotic forest disturbances to be viewed and assessed through the calendar year. The change products are also being utilized for forest change trend analysis and for developing regional forest overstory mortality products. ForWarn's forest change products are used to alert forest health specialists about new forest disturbances. Such alerts are also typically based on available Landsat, aerial, and ground data as well as communications with forest health specialists and previous experience. ForWarn products have been used to detect and track many types of regional disturbances to multiple forest types, including defoliation from caterpillars and severe storms, as well as mortality from both biotic and abiotic agents (e.g., bark beetles, drought, fire, anthropogenic clearing). ForWarn offers products that could be combined with other geospatial data on forest biomass to assess forest disturbance carbon impacts within the conterminous US.
Spruce, Joseph P.↗
Ensemble Statistical Post-Processing of the National Air Quality Forecast Capability: Enhancing Ozone Forecasts in Baltimore, Maryland
An ensemble statistical post-processor (ESP) is developed for the National Air Quality Forecast Capability (NAQFC) to address the unique challenges of forecasting surface ozone in Baltimore, MD. Air quality and meteorological data were collected from the eight monitors that constitute the Baltimore forecast region. These data were used to build the ESP using a moving-block bootstrap, regression tree models, and extreme-value theory. The ESP was evaluated using a 10-fold cross-validation to avoid evaluation with the same data used in the development process. Results indicate that the ESP is conditionally biased, likely due to slight overfitting while training the regression tree models. When viewed from the perspective of a decision-maker, the ESP provides a wealth of additional information previously not available through the NAQFC alone. The user is provided the freedom to tailor the forecast to the decision at hand by using decision-specific probability thresholds that define a forecast for an ozone exceedance. Taking advantage of the ESP, the user not only receives an increase in value over the NAQFC, but also receives value for An ensemble statistical post-processor (ESP) is developed for the National Air Quality Forecast Capability (NAQFC) to address the unique challenges of forecasting surface ozone in Baltimore, MD. Air quality and meteorological data were collected from the eight monitors that constitute the Baltimore forecast region. These data were used to build the ESP using a moving-block bootstrap, regression tree models, and extreme-value theory. The ESP was evaluated using a 10-fold cross-validation to avoid evaluation with the same data used in the development process. Results indicate that the ESP is conditionally biased, likely due to slight overfitting while training the regression tree models. When viewed from the perspective of a decision-maker, the ESP provides a wealth of additional information previously not available through the NAQFC alone. The user is provided the freedom to tailor the forecast to the decision at hand by using decision-specific probability thresholds that define a forecast for an ozone exceedance. Taking advantage of the ESP, the user not only receives an increase in value over the NAQFC, but also receives value for
ozone↗
Security Vulnerability Profiles of NASA Mission Software: Empirical Analysis of Security Related Bug Reports
NASA develops, runs, and maintains software systems for which security is of vital importance. Therefore, it is becoming an imperative to develop secure systems and extend the current software assurance capabilities to cover information assurance and cybersecurity concerns of NASA missions. The results presented in this report are based on the information provided in the issue tracking systems of one ground mission and one flight mission. The extracted data were used to create three datasets: Ground mission IVV issues, Flight mission IVV issues, and Flight mission Developers issues. In each dataset, we identified the software bugs that are security related and classified them in specific security classes. This information was then used to create the security vulnerability profiles (i.e., to determine how, why, where, and when the security vulnerabilities were introduced) and explore the existence of common trends. The main findings of our work include:- Code related security issues dominated both the Ground and Flight mission IVV security issues, with 95 and 92, respectively. Therefore, enforcing secure coding practices and verification and validation focused on coding errors would be cost effective ways to improve mission's security. (Flight mission Developers issues dataset did not contain data in the Issue Category.)- In both the Ground and Flight mission IVV issues datasets, the majority of security issues (i.e., 91 and 85, respectively) were introduced in the Implementation phase. In most cases, the phase in which the issues were found was the same as the phase in which they were introduced. The most security related issues of the Flight mission Developers issues dataset were found during Code Implementation, Build Integration, and Build Verification; the data on the phase in which these issues were introduced were not available for this dataset.- The location of security related issues, as the location of software issues in general, followed the Pareto principle. Specifically, for all three datasets, from 86 to 88 the security related issues were located in two to four subsystems.- The severity levels of most security issues were moderate, in all three datasets.- Out of 21 primary security classes, five dominated: Exception Management, Memory Access, Other, Risky Values, and Unused Entities. Together, these classes contributed from around 80 to 90 of all security issues in each dataset. This again proves the Pareto principle of uneven distribution of security issues, in this case across CWE classes, and supports the fact that addressing these dominant security classes provides the most cost efficient way to improve missions' security. The findings presented in this report uncovered the security vulnerability profiles and identified the common trends and dominant classes of security issues, which in turn can be used to select the most efficient secure design and coding best practices compiled by the part of the SARP project team associated with the NASA's Johnson Space Center. In addition, these findings provide valuable input to the NASA IVV initiative aimed at identification of the two 25 CWEs of ground and flight missions.
vulnerability↗
Digital Surface Models and Building Extraction: A Comparison of IFSAR and LIDAR Data
In this paper the task to extract significant built structures in Digital Surface Models is analyzed.
Digital↗
Cloud Impacts on Photochemistry: Building a Climatology of Photolysis Rates from the Atmospheric Tomography Mission
Measurements from actinic flux spectroradiometers on board the NASA DC-8 during the Atmospheric Tomography (ATom) mission provide an extensive set of statistics on how clouds alter photolysis rates (J values) throughout the remote Pacific and Atlantic Ocean basins. J values control tropospheric ozone and methane abundances, and thus clouds have been included for more than three decades in tropospheric chemistry modeling. ATom made four profiling circumnavigations of the troposphere capturing each of the seasons during 2016–2018. This work examines J values from the Pacific Ocean flights of the first deployment, but publishes the complete Atom-1 data set (29 July to 23 August 2016). We compare the observed J values (every 3 s along flight track) with those calculated by nine global chemistry– climate/transport models (globally gridded, hourly, for a mid-August day). To compare these disparate data sets, we build a commensurate statistical picture of the impact of clouds on J values using the ratio of J -cloudy (standard, sometimes cloudy conditions) to J -clear (artificially cleared of clouds). The range of modeled cloud effects is inconsistently large but they fall into two distinct classes: (1) models with large cloud effects showing mostly enhanced J values aloft and or diminished at the surface and (2) models with small effects having nearly clear-sky J values much of the time. The ATom-1 measurements generally favor large cloud effects but are not precise or robust enough to point out the best cloud-modeling approach. The models here have resolutions of 50–200 km and thus reduce the occurrence of clear sky when averaging over grid cells. In situ measurements also average scattered sunlight over a mixed cloud field, but only out to scales of tens of kilometers. A primary uncertainty remains in the role of clouds in chemistry, in particular, how models average over cloud fields, and how such averages can simulate measurements.
actinic flux spectroradiometers↗
Business is Beaming
Terabeam has developed a Fiberless Optical(TM) Network that transmits broadband data from office buildings to the nation's wide-area networks (WANs), without digging up the streets. A key component of Terabeam's Fiberless Network is Large Aperture Holographic Optic technology, developed by Ralcon Development Lab, of Paradise, Utah. Ralcon developed the Holographic Optical Element (HOE) technology with assistance from a NASA Goddard Space Flight Center Small Business Innovation Research (SBIR) contract. Terabeam further developed the HOE technology and incorporated it into its Fiberless Optical Network-sending an immeasurable amount of information soaring overhead. Terabeam developed its Fiberless Optical Network using a proprietary HOE to transmit data. The unit is mounted near an office window and has the ability to beam safe, low-power, invisible data through the air at gigabits-per-second speeds to anywhere in the service area. Gigabits-per-second speeds are thousands of times faster than the speeds of current broadband transmissions. This allows businesses to connect to local-area networks (LANs) as well as WANs, in a quick and affordable manner.
Source record↗
Building Aerodynamic Databases for the SLS Design Process
NASA's new Space Launch System (SLS) will be the first rocket since the Saturn V (1967-1973) to carry astronauts beyond low earth orbit-and will carry 10% more payload than Saturn V and three times the payload of the space shuttle. The SLS configuration consists of a center core and two solid rocket boosters that separate from the core as their fuel is exhausted two minutes after lift-off. During these first two minutes of flight, the vehicle powers its way through strong shock waves as it accelerates past the speed of sound, then pushes beyond strong aerodynamic loads at the maximum dynamic pressure, and is ultimately enveloped by gaseous plumes from the booster-separation motors. The SLS program relies on computational fluid dynamic (CFD) simulations to provide much of the data needed to build aerodynamic databases describing the structural load distribution, surface pressures, and aerodynamic forces on the vehicle.
Aerodynamic↗
Pushing the Application Limit of Hyperspectral IR Sounder Remote Sensing for Frontiers of Weather and Climate Studies
Since the launch of AQUA Atmospheric Infrared Sounder (AIRS) in 2002, space-borne hyperspectral infrared (IR) sounders have provided information-rich, climate quality, and time-continuous spectral radiance measurements at the top of the atmosphere for more than two decades. Satellite based hyperspectral IR sensors, including AIRS, Cross-track Infrared Sounder (CrIS), and Infrared Atmospheric Sounding Interferometer (IASI), have demonstrated their critical roles for various weather applications and climate related studies. New use of hyperspectral IR sounder data will focus on underexplored areas. Current use of those data is generally limited by the capability of fully exploring and exploiting high spectral resolution information content, achieving radiometric accuracy and stability defined by the instruments, and merging data from different sensors to build a consistent, long-term climate data record. We will present the on-going research efforts at NASA Langley Research Center (LaRC) to develop novel hyperspectral IR sounder data products to address those potentials yet to be fully realized. Some applications of these products, including the study of Planetary Boundary Layer (PBL), the construction of climate data record over polar region, and the fusion of AIRS and CrIS data via the radiometric consistent climate fingerprinting method will be discussed.
Wan Wu↗
Development of Machine Learning Algorithms to Segment and Study Images of Astromaterial Samples
Introduction: Micrometer-scale chemical analyses of chondritic meteorites and mission-returned asteroid samples can reveal details of the physical and chemical processes operating in the early solar system, including processes that gave rise to planets, moons, and minor bodies. These primitive astromaterials are comprised of chondrules, calcium- and aluminum-rich inclusions (CAI), and many other silicates, oxides, metals, sulfides, and fine-grained materials. The chemical and mineralogical complexity of these samples, vast populations of different components, and heterogeneity across mm to km scales, all limit our understanding of the origin and evolution of these materials. Here, we describe recent efforts to use machine learning techniques to automate the segmentation of chemical maps of chondritic meteorites, designed to aid studies of asteroid samples returned by spacecraft. By automating the task of segmentation it will become possible to rapidly analyze and interpret the sizes, shapes, mineralogy, chemistry, and other properties of every chondrule, calcium- and aluminum-rich inclusion (CAI) and other clast within and between asteroid samples. Sample return missions significantly accelerate and heighten the need to develop such new data analysis techniques, and associated data repositories. Techniques: Neural networks require abundant training data, i.e. images which have been segmented by a human user. We have manually segmented data available from previous petrologic and chemical work at NASA Johnson Space Center and the American Museum of Natural History [1-4]. These data were derived from energy- and wavelength-dispersive X-ray spectroscopy (EDS, WDS) mapping of samples from many chondrite groups. The Deeplabv3+ [5] neural network architecture was trained on human-labeled masks and used to create machine-labeled masks. Several different algorithms were investigated, with inputs ranging from common RGB image formats through to hyperspectral datasets, with raw data comprising greyscale maps of Mg, Ca, and Al, with or without Si, Fe, Ti for both EDS and WDS data, and extending to other elements in EDS only. Each greyscale image was paired with a binary mask for each labelled particle type. Results: The trained algorithms can segment (Fig 1), classify, and measure the dimensions of thousands of particles in chemical maps of a standard 1-inch round petrographic section in seconds to minutes, rather than many hours needed by a human. Accuracy of the algorithms varied from chondrite to chondrite and across particle types. Further results and details of the algorithms will be presented at the workshop. Future directions: Machine learning has the potential to revolutionize our understanding of complex particle populations contained within primitive astromaterial, with segmentation being a critical first step. Example applications include better understanding of particle transport, nebular reservoirs, parent body accretion, and a deeper understanding of the relationships between particle populations and bulk rock elemental and isotopic compositions. In addition to benefits that machine learning can bring to individual researchers, building a community data repository of thousands to millions of particles across hundreds of samples will open up many other possibilities. For example, with a large enough dataset it will be possible to search for exceptionally closely matching particles across disparate samples. Such a capability would enable a single CAI from OSIRISREx or Hayabusa/II samples to be matched to chondritic CAIs that exhibit near-identical size, texture, and mineralogy, down to the level of similar core phenocrysts, zonation, and rim sequences. Such comparative analyses will help to disentangle precursor chemistry, chronology, gas/dust reservoirs during heating, and accretion. Such an endeavor would be impossible without machine learning and a large community data repository of astromaterial chemical/mineralogic maps.
Machine Learning↗
Template Matching Approach to Signal Prediction
A new approach to signal prediction and prognostic assessment of spacecraft health resolves an inherent difficulty in fusing sensor data with simulated data. This technique builds upon previous work that demonstrated the importance of physics-based transient models to accurate prediction of signal dynamics and system performance. While models can greatly improve predictive accuracy, they are difficult to apply in general because of variations in model type, accuracy, or intended purpose. However, virtually any flight project will have at least some modeling capability at its disposal, whether a full-blown simulation, partial physics models, dynamic look-up tables, a brassboard analogue system, or simple hand-driven calculation by a team of experts. Many models can be used to develop a predict, or an estimate of the next day s or next cycle s behavior, which is typically used for planning purposes. The fidelity of a predict varies from one project to another, depending on the complexity of the simulation (i.e. linearized or full differential equations) and the level of detail in anticipated system operation, but typically any predict cannot be adapted to changing conditions or adjusted spacecraft command execution. Applying a predict blindly, without adapting the predict to current conditions, produces mixed results at best, primarily due to mismatches between assumed execution of spacecraft activities and actual times of execution. This results in the predict becoming useless during periods of complicated behavior, exactly when the predict would be most valuable. Each spacecraft operation tends to show up as a transient in the data, and if the transients are misaligned, using the predict can actually harm forecasting performance. To address this problem, the approach here expresses the predict in terms of a baseline function superposed with one or more transient functions. These transients serve as signal templates, which can be relocated in time and space against the signal background. One then has the ability to reconstruct a signal regardless of the precise timing of the transients. During operation, one applies the actual start times of spacecraft activities as they occur, and produces a reconstructed, accurate predict in real-time. This general approach is valid under two important conditions. First, the transients themselves must be time-invariant. Second, the transients must be reasonably consistent with respect to different operating points. Both of these assumptions are generally valid, but in the case of a complicated system with numerous types of overlapping transients, this approach may not be effective. Fortunately, there tend to be few transients in spacecraft telemetry of sensor quantities or low-level health and status information because these signals rarely reflect multiple different types of operation. Furthermore, if the predict is at least reasonably close to actual operation, the shift either in time, or in the operating point when the transient occurs - is likely to be small. The proposed approach considers three ways to recognize a transient. The first and most reliable is to use a different signal that identifies operating mode - often the transient will be correlated to a change in operating mode, and this change can usually be detected positively from discrete signals in spacecraft telemetry. If there is no useful mode signal, the second option is to identify the transient template by hand, and detect the onset of the transient using a curve-fitting approach. Finally, if one elects not to choose by hand, there is an option for automatic selection. This technique has been applied to sensor data from several JPL missions and industrial applications, demonstrating an improvement in accurate prediction of future behavior and early detection of system problems. This technique is applicable to practically any time-varying, quantitative sensor measurement.
Mackey, Ryan↗
Manpower management information system /MIS/
System of programs capable of building and maintaining data bank provides all levels of management with regular manpower evaluation reports and data source for special management exercises on manpower.
Gravette, M. C.↗
Two systems analyses of SETI
The problem of receiving and identifying a single microwave signal transmitted by extraterrestrial intelligent beings is analyzed in the cases where the signal is designed to catch our attention and the signal is designed for internal purposes of another civilization. Six variables which yield uncertainty as to the exact signal which should be searched for are described: polarization, modulation, flux level, direction, frequency (including bandwidth and drift rate), and time. It is shown that if all reasonable variations of these parameters are to be examined sequentially for 1000 seconds, the search would take over a million times longer than the age of the Universe. Ways to simplify the search are considered, including widening the frequency bin, selecting specific targets, cutting the observation time, using a Fourier transform device for data processing, and building larger antennas as well as better low-noise receivers.
Machol, R. E.↗
Systems integration of marketable subsystems: A collection of progress reports
Monthly progress reports are given in the areas of marketable subsystems integration; development, design, and building of site data acquisition subsystems and data processing systems; operation of the solar test facility and a systems analysis.
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