Search NASA⌕ Search

SEARCH · Search NASA

Results for “uncertainty characterization”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 343 records · Page 19

Uncertainty Analysis in Multi‐Sector Systems: Considerations for Risk Analysis, Projection, and Planning for Complex Systems

Abstract Simulation models of multi‐sector systems are increasingly used to understand societal resilience to climate and economic shocks and change. However, multi‐sector systems are also subject to numerous uncertainties that prevent the direct application of simulation models for prediction and planning, particularly when extrapolating past behavior to a nonstationary future. Recent studies have developed a combination of methods to characterize, attribute, and quantify these uncertainties for both single‐ and multi‐sector systems. Here, we review challenges and complications to the idealized goal of fully quantifying all uncertainties in a multi‐sector model and their interactions with policy design as they emerge at different stages of analysis: (a) inference and model calibration; (b) projecting future outcomes; and (c) scenario discovery and identification of risk regimes. We also identify potential methods and research opportunities to help navigate the tradeoffs inherent in uncertainty analyses for complex systems. During this discussion, we provide a classification of uncertainty types and discuss model coupling frameworks to support interdisciplinary collaboration on multi‐sector dynamics (MSD) research. Finally, we conclude with recommendations for best practices to ensure that MSD research can be properly contextualized with respect to the underlying uncertainties.

54 ENVIRONMENTAL SCIENCES↗

High-resolution modeling of indoor radon exposure with uncertainty quantification in Utah

Indoor radon accounts for 37% of population-level exposure to ionizing radiation in the United States. However, radon metrics are typically reported at coarse spatial scales, potentially obscuring meaningful local variation. We developed a high-resolution modeling framework to estimate indoor radon concentrations across Utah while explicitly quantifying predictive uncertainty. A total of 19,497 residential radon measurements collected between 2006 and 2017 were combined with environmental and housing characteristics and analyzed using a geospatial neural network that accommodates spatial dependence and nonlinear associations. Predictions were generated on a uniform hexagonal grid at 0.73 km2 resolution (H3 level 8). Out-of-sample predictions aggregated to the H3 level 8 grid showed good agreement with observed concentrations (Pearson r=0.64), while household-level predictions exhibited more moderate agreement (r=0.45). The model produced well-calibrated uncertainty estimates, with 24.1% of held-out observations exceeding the predicted 75th-percentile threshold. Maps of predicted radon concentrations and the probability of exceeding the U.S. EPA action level of 148 Bq/m3 (4 pCi/L) revealed substantial fine-scale spatial heterogeneity that was not apparent in conventional coarse-resolution summaries, with greater local variability observed in densely monitored urban counties than in sparsely sampled regions. High-resolution radon models that explicitly quantify uncertainty provide a useful framework for characterizing the spatial distribution of indoor radon and identifying areas of elevated exceedance risk. These findings highlight the value of fine-scale monitoring data and uncertainty-aware modeling approaches for radon exposure assessment, environmental risk characterization, and radon-related health research.

Wu, Yunhan [ORNL] (ORCID:0000000178842994)↗

Exploring Explicit Uncertainty for Binary Analysis (EUBA)

Reverse engineering (RE) analysts struggle to address critical questions about the safety of binary code accurately and promptly, and their supporting program analysis tools are simply wrong sometimes. The analysis tools have to approximate in order to provide any information at all, but this means that they introduce uncertainty into their results. And those uncertainties chain from analysis to analysis. We hypothesize that exposing sources, impacts, and control of uncertainty to human binary analysts will allow the analysts to approach their hardest problems with high-powered analytic techniques that they know when to trust. Combining expertise in binary analysis algorithms, human cognition, uncertainty quantification, verification and validation, and visualization, we pursue research that should benefit binary software analysis efforts across the board. We find a strong analogy between RE and exploratory data analysis (EDA); we begin to characterize sources and types of uncertainty found in practice in RE (both in the process and in supporting analyses); we explore a domain-specific focus on uncertainty in pointer analysis, showing that more precise models do help analysts answer small information flow questions faster and more accurately; and we test a general population with domain-general sudoku problems, showing that adding "knobs" to an analysis does not significantly slow down performance. This document describes our explorations in uncertainty in binary analysis.

97 MATHEMATICS AND COMPUTING↗

Mineral Dust Optical Properties for Remote Sensing and Global Modeling: A Review

Dust plays a key role in many Earth system processes and is ubiquitous in the Martian atmosphere. Various intensive field campaigns, laboratory analyses, space-based remote sensing missions, and global modeling efforts aim to characterize dust optical properties. This is a bountiful time for dust scientists, and yet the interpretation of retrievals and comparison to models remains complicated by various conflicting assumptions that are part of each algorithm. For example, the conversion of satellite radiance measurements into products like aerosol optical depth for model evaluation depends upon aerosol properties like particle size and shape that are often prescribed and not part of the retrieval. Conversely, the model calculation of aerosol optical depth often uses different assumptions. The goal of this review is to first document algorithmic assumptions by various satellite retrieval products and models, and identify where there is consistency and where there are differences. In general, the differences documented in this paper reflect uncertainties resulting from incomplete observational characterization of dust aerosols and limitations in our understanding. Second, we note what observations might reduce uncertainties in our knowledge and bring greater consistency to retrievals and models, allowing for a more rigorous and harmonious comparison. The lack of comprehensive and realistic shape models for dust is an outstanding issue, such that closure between forward modeling from particle refractive index, shape, and size and observed optical properties cannot be achieved. Limitations in the computational methods that must be applied to model scattering from complex shapes also makes accurate optical modeling for dust challenging. Field observations indicate the persistence of coarse and giant dust particles at higher altitudes and farther downwind from their source than previously expected. Remote sensing retrieval algorithms based on observations at visible wavelengths have limited sensitivity to these particles and generally do not consider them, although a recent product based on longwave radiances is encouraging. Current measurements of the refractive index of bulk dust and fundamental dust minerology components such as hematite vary widely, inhibiting attempts to represent the variability in dust optical properties and forcing, as expected from different major dust source regions on Earth that have varying mineralogical composition. Some remote sensing retrieval algorithms allow for limited refractive index variability in their inversion solutions through mixing with other fine mode aerosol models, or optimizing the single scattering albedo, but Earth system models surveyed for this paper assume a globally uniform, size-invariant refractive index. Although no Martian dust samples have yet been returned to Earth, remote sensing observations indicate that Martian dust is globally homogenous in composition, and a single spectral refractive index assumption has been widely adopted to represent Martian dust. The lack of comprehensive, statistically representative measurements of dust particle microphysical properties (size distribution, morphology, complex index of refraction spectra, internal structure heterogeneity), and the resulting optical properties, limits our ability to verify the fidelity of these assumptions. A chain of measurements is needed, ranging from characterizing individual dust mineralogy components (e.g., pure hematite and goethite) to in situ sampling of complex atmospheric aerosol mixtures. Such results could be applied to both remote sensing retrievals that characterize the optical properties of the total aerosol burden in the atmosphere from total radiance measurements, and to global models that represent the total aerosol burden in the atmosphere by building it up from the balance of individual aerosol sources and sinks.

Aeolian dust↗

Calibrating CERES and VIIRS using CLARREO Pathfinder: Adjustment for the Viewing Geometry Mismatch

The Climate Absolute Radiance and Refractivity Observatory Pathfinder (CPF) mission on the International Space Station (ISS) will provide, for the first-time, an SI-traceable on-orbit calibration reference with a reflectance uncertainty of 0.3% (1 sigma). One major mission objective is to demonstrate the capability of transferring CPF’s radiometric accuracy to other satellite-based instruments, such as Clouds and Earth's Radiant Energy System (CERES) and Visible Infrared Imaging Radiometer Suite (VIIRS) on NOAA-20. CPF inter-calibration measurements will be planned to obtain inter-calibration samples that closely match satellite-based target sensor observations in time, space, angle, and wavelength. The inter-calibration event observations cover a wide range of viewing geometry angles with potential angular mismatch between CPF and the target sensors which can introduce non-negligible errors against the mission’s inter-calibration uncertainty budget. We will introduce the CPF approach to adjust for angular mismatch differences, thereby reducing the incurred uncertainties to within the inter-calibration uncertainty budget. The angular adjustment algorithm has been developed and validated based on the high-fidelity simulations of the CPF spectra using the Principal Component based Radiative Transfer Model (PCRTM). The implementation of the algorithm on event simulation data and the characterization for the angular adjustment uncertainty will be presented.

Wan Wu↗

Disentangling the Distant Stellar Halo Using K Giants in DESI Year 3 Data

We present a sample of 88,959 K giants from Dark Energy Spectroscopic Instrument Milky Way Survey Year 3 data, which we use to characterize the chemo-dynamical properties of the stellar halo at Galactocentric distances of 12 to ∼100 kpc. Using HDBSCAN, we identify five prominent stellar halo substructures: Aleph, the Sagittarius stream, Gaia-Sausage-Enceladus (GSE), Cetus-Palca, and the Orphan–Chenab stream. We present the properties of each of these structures as they appear in our catalog, and examine how uncertainties on distance affect the characterization of substructure with this approach. We also examine regions associated with previously reported overdensities (such as the Virgo Overdensities and the Sagittarius spur) that we do not recover with HDBSCAN. The size and distance range of our catalog allows us to explore in detail the residual stellar halo, comprising stars that we do not associate with any substructure. We find that samples of ∼2000 outer halo stars with both highly prograde and highly retrograde angular momenta have similar metallicity distribution functions (MDFs), which do not resemble the MDFs of either GSE or Sagittarius. Both the prograde and retrograde residual halo MDFs are bimodal, with a metal-poor peak at [Fe/H] ∼ −2 and a metal-rich peak at [Fe/H] ∼ −1.3 (prograde) or −1.5 (retrograde). The MDF for lower-angular-momentum residual halo K giants does not show clear evidence for a metal-poor peak, and broadly resembles the MDF of GSE, even at much lower binding energies than GSE itself. We discuss possible interpretations of these findings for GSE accretion scenarios.

79 ASTRONOMY AND ASTROPHYSICS↗

Development of Multi-Edge Slant Target for Unlocalized MTF Measurement of Airborne Imaging System Payloads

Calibration of imaging instruments is a critical step in effectively characterizing imaging data. A comprehensive characterization of an imaging system provides valuable insight into the overall uncertainties in the results derived from the raw data collected. The Scientifically Calibrated In-Flight Imagery (SCIFLI) team (based at NASA Langley Research Center) has designed, built, tested, and successfully flown the optical payload known as the SCIFLI Airborne Multispectral Imager (SAMI). SAMI is a multispectral imaging payload, complete with sensor configurations for the Ultraviolet-Visible spectrum as well as Near- Infrared, Shortwave Infrared, and Midwave Infrared bands and is fitted with various optical hardware designed to turn, split, and focus light beams to the respective in-band sensors. Radiometric calibrations are performed for SAMI such that the accuracy and uncertainty of thermal and spectral measurements are effectively characterized. However, SAMI does not regularly undergo spatial characterizations to evaluate the performance of the imager at high spatial frequencies. This is essential, as understanding the spatial sensor response of discrete sensors is just as important as the spectral sensor response. The payload currently utilizes a calibration cart for lab testing. Equipped with a modular setup and several radiation sources, it is perfect for performing high-quality radiometric calibrations. This work focuses on using the calibration cart to perform spatial calibrations. A custom designed calibration target is being used to evaluate the optical performance of SAMI. Specifically, the target is designed to measure the Modulation Transfer Function (MTF) of the imager in discrete locations around the focal plane. The results of this characterization will provide insight into the spatial resolution capabilities of the SAMI payload in practice. Future work includes using the results of the study to correct optical imperfections in imagery taken with SAMI.

SAMI↗

Uncertainty Analysis of Inhalation Dose Coefficients for Nuclear Incident Response

This study addressed the need to characterize variability in inhalation dose coefficients due to uncertainties in respiratory tract deposition, systemic biokinetics, and physiological parameters. A Python-based implementation of the International Commission on Radiological Protection Publication 66 Human Respiratory Tract Model was developed called the Radiological Exposure Dose Calculator (REDCAL) to propagate parameter uncertainty.

61 RADIATION PROTECTION AND DOSIMETRY↗

Developing a Spectral Correlation based Method for mitigating Angular Mismatch Effects in Intercalibration Using a Benchmark Hyperspectral Sensor

The CLARREO Pathfinder (CPF) mission will implement a state-of-the-art intercalibration method for transferring CPF’s in-orbit Système Internationale (SI)-traceable reference to the shortwave channel of the Clouds and the Earth’s Radiance Energy System (CERES) and the reflective solar bands of the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the NOAA-20 satellite platform with a targeted intercalibration methodology uncertainty of 0.3% (k=1). In order to achieve such a high intercalibration accuracy, the CPF intercalibration measurements to be scheduled need to closely match those from CERES and VIIRS in time, space, angles, and wavelength. Despite’s CPF’s two-axis pointing capability to match its boresight line of sight to that of a target sensor, there will be finite residual differences in angular samplings of the two instruments. The potential angular mismatch between CPF and the target sensors can introduce systematic errors in the intercalibration results, and therefore needs to be corrected using a rigorously designed algorithm. The CPF intercalibration team has been developing a correction method for mitigating the impact of these angular anisotropic effects in the CPF-VIIRS and CPF-CERES intercalibration. The method explores the spectral correlation relationship between the reflected solar radiances from the same surface target that would be measured by CPF at two adjacent angles. Our studies have shown that the spectral information based on CPF measurements can be used to accurately predict the spectral radiance or reflectance difference due to a given mismatch in the viewing and solar geometry. The hyper-spectral information can also be used to provide scene stratification without using any auxiliary data, which is critical to reduce the angular correction uncertainty. The angular correction relationship can be well established using simulated CPF-like top-of-atmosphere spectral radiances observed at all sorts of viewing geometry and solar angles and for different scenes. Intensive radiative transfer simulations have been completed using a state-of-art radiative transfer model developed by the CPF team members. The implementation of the algorithm on high-fidelity event simulation data and the characterization for the angular adjustment uncertainty will be presented.

Wan Wu↗

Impact of AMS-02 Measurements on Reducing GCR Model Uncertainties

For vehicle design, shield optimization, mission planning, and astronaut risk assessment, the exposure from galactic cosmic rays (GCR) poses a significant and complex problem both in low Earth orbit and in deep space. To address this problem, various computational tools have been developed to quantify the exposure and risk in a wide range of scenarios. Generally, the tool used to describe the ambient GCR environment provides the input into subsequent computational tools and is therefore a critical component of end-to-end procedures. Over the past few years, several researchers have independently and very carefully compared some of the widely used GCR models to more rigorously characterize model differences and quantify uncertainties. All of the GCR models studied rely heavily on calibrating to available near-Earth measurements of GCR particle energy spectra, typically over restricted energy regions and short time periods. In this work, we first review recent sensitivity studies quantifying the ions and energies in the ambient GCR environment of greatest importance to exposure quantities behind shielding. Currently available measurements used to calibrate and validate GCR models are also summarized within this context. It is shown that the AMS-II measurements will fill a critically important gap in the measurement database. The emergence of AMS-II measurements also provides a unique opportunity to validate existing models against measurements that were not used to calibrate free parameters in the empirical descriptions. Discussion is given regarding rigorous approaches to implement the independent validation efforts, followed by recalibration of empirical parameters.

Slaba, T. C.↗

Approach to Analyzing VTR Core Performance Sensitivities to Fuel Manufacturing Uncertainties and Application to Fuel Slug Diameter

This report documents the initial effort to characterize sensitivity of VTR core performance to fuel manufacturing uncertainties. The effects of variation in the as-fabricated fuel slug diameter on VTR neutronics performance are evaluated, using both high-fidelity Monte Carlo (OpenMC) and the main core design solver (DIF3D) for all cases. This work and follow-on work will inform the VTR Project’s fuel manufacturing processes and associated acceptable criteria, as well as detailed uncertainty analysis in later stages of VTR development and deployment. In addition, this work demonstrates the approach to performing such evaluations and provides a basis for reducing the scope of calculations in future sensitivity analysis.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Optimization of Picosecond Laser Parameters for Surface Treatment of Composites Using a Design of Experiments (DOE) Approach

Based on guidelines from the Federal Aviation Administration, research supported by the NASA Advanced Composites Project is investigating methods to improve process control for surface preparation and pre-bond surface characterization on aerospace composite structures. The overall goal is to identify high fidelity, rapid, and reproducible surface treatments and surface characterization methods to reduce the uncertainty associated with the bonding process. The desired outcome is a more reliable bonded airframe structure, and to reduce time to achieve certification. In this work, a design of experiments (DoE) approach was conducted to determine optimum laser ablation conditions using a pulsed laser source with a nominal pulse width of 10 picoseconds. The laser power, frequency, scan speed, and number of passes (1 or 2) were varied within the laser system operating boundaries. Aerospace structural carbon fiber reinforced composites (Torayca® 3900-2/T800H) were laser treated, then characterized for contamination, and finally bonded for mechanical testing. Pre-bond characterization included water contact angle (WCA) using a handheld device, ablation depth measurement using scanning electron microscopy (SEM), and silicone contamination measurement using laser induced breakdown spectroscopy (LIBS). In order to accommodate the large number of specimens in the DoE, a rapid-screening, double cantilever beam (DCB) test specimen configuration was devised based on modifications to ASTM D5528. Specimens were tested to assess the failure modes observed under the various laser surface treatment parameters. The models obtained from this DoE indicated that results were most sensitive to variation in the average laser power. Excellent bond performance was observed with nearly 100% cohesive failure for a wide range of laser parameters. Below about 200 mW, adhesive failure was observed because contamination was left on the surface. For laser powers greater than about 600 mW, large amounts of fiber were exposed, and the failure mode was predominately fiber tear.

Palmieri, Frank L.↗

Improving Subsurface Stress Characterization for Carbon Dioxide Storage Projects by Incorporating Machine Learning Techniques

The overall objective of this project is to develop a framework for reliable characterization and prediction of the state of stress in the overburden and underburden (including the basement) in CO 2 storage reservoirs using machine learning and integrated geomechanics and geophysical methods. Specifically, we propose to develop workflow encompassing of technologies and/or methods to predict stress and pressure changes due to CO 2 injection in an active tertiary recovery site and their impacts on subtle fault activation, fractures and occurrence of microseismic events and compare responses to field observations. In this project, we anticipate using dataset from the Farnsworth field Unit (FWU) which is operated by Purdure Petroleum. A novel elastic-waveform VSP inversion technique will be used to estimate high-resolution spatial and temporal changes of elastic moduli in CO 2 storage reservoirs, which will be combined with velocity-stress relationship derived from laboratory tests to obtain subsurface pressure and stress. Clustered microseismic data will be jointly inverted for improved focal mechanisms. Least-squares reverse-time migration of microseismic waveform data will be performed to directly image fracture/fault zones. Additionally, a deep neural network machine learning technique with convolutional and recurrent layers will be used for learning the spectro-temporal structures in microseismic waveforms. The results of this geotechnical data analysis will be integrated to develop a high-resolution 3D mechanical earth model extending from the overburden sealing formations to the underburden including the basement. Mechanical properties will be derived through integration of mechanical logs, tests, available results from chemo-mechanical laboratory tests, and elastic inversion of seismic data using a combination of Bayesian and stochastic methods as well as machine learning technique. Failure features (faults/fractures) will be represented and/or modeled based on seismic and core data analysis. A transient hydrodynamic-geomechanical model will be developed through coupling with the calibrated FWU reservoir simulation model. The full physics coupled model will be used to train a reduced order proxy model using machine learning algorithm for estimating stress which will then be used with appropriate constitutive relationships and forward seismological models to simulate pressure changes and induced microseismicity. An advanced optimization framework will be developed to perform a history match to minimize error between field observations and simulated. The history matched proxy model will be verified against the full-physics equivalent. The field observations that will be used in the coupled model calibration process include pressure/stress inverted from VSP, moment magnitude from microseismic analysis, real time downhole pressure measurements, production and injection data. Parameter sensitivity and uncertainty analysis will be performed to characterize the impact of model parameter uncertainty on stress estimates. The proposed project will have significant impact on future field implementation of the proposed technology. Because the project field site is an ongoing CO 2 EOR development, the value of the new technology will be demonstrated in an operational context and evaluated as a viable risk mitigation strategy. Cost/benefit will be evaluated together with the various commercial incentives for CO 2 sequestration available to oil and gas operators. The extensive available dataset and ongoing data acquisition under the SWP Phase III work plan provides flexibility for investigation of multiple approaches and reduces technical risk.

58 GEOSCIENCES↗

Risk Importance Ranking of Fire Data Parameters to Enhance Fire PRA Model Realism

Fire is historically and analytically a significant contributor to nuclear power plant risk. The level of fire risk and the methods, tools and data for modeling this risk is highly debated by experts. One area of debate is the input data used in fire modeling and how to deal with this data’s high uncertainty. This report outlines initial work performed for determining the key parameters causing this uncertainty and how it propagates into nuclear power plant models. This research paves the way for the development of methods to reduce fire data uncertainty used in modeling. The Nuclear Regulatory Commission has mandated that nuclear power plants perform fire risk modeling. However, there are several issues with the current risk modeling implementation that affect the results. Approved modeling methods can be overly conservative and often do not match plant experience. Also, the data used in the modeling can have high uncertainties and is influenced by expert judgement. To evaluate input data uncertainty, researchers performed an initial review of several fire experiments done at Sandia National Laboratories. Uncertainties for fire data can come from many sources, such as experiment design constraints, environmental conditions, or other plant-specific aspects. There are many different significant and insignificant parameters driving the uncertainty. Additionally, the uncertainty of the different input data used in the fire modeling could have a significant or insignificant effect on the entire plant risk. A four-step methodology was developed to perform Integrated Probabilistic Risk Assessment Importance Ranking. A demonstration case using these steps was set up and three of the four steps were completed in fiscal year (FY) 2019 and the fourth step done FY 2020. These steps are: 1. The qualitative analysis of potential sources was conducted with the following items identified for the demonstration. • Maximum heat release rate • Time to maximum heat release rate • Duration of max heat release rate • Time to decay • Thermal conductivity of concrete • Specific heat of concrete • Density of concrete • Cable jacket thickness 2. A quantitative characterization of dominant sources of uncertainty was performed. A list of distributions and determined values of the dominant sources is shown in Appendix A. 3. A quantitative screening of the potential sources of uncertainty using Morris Elementary Effects Analysis was completed. An experimental model using the physics-based fire modeling tool Fire Dynamics Simulator was developed and coupled with the Risk Analysis Virtual Environment. The Morris analysis identified at least two parameters that can be eliminated as significant contributors (specific heat of concrete and cable jacket thickness). 4. Global importance measure (Global IM) analysis to generate a comprehensive ranking based on their influence on the plant risk. In this research, a moment-independent Global IM is used since it can address (a) uncertainty in the input parameters of the fire model, (b) uncertainty in the risk outputs, and (c) non-linearity and interactions among input parameters in the fire model, more accurately than the correlation-based and variance-based global methods. The observations from the research showed that, depending on the initial and boundary conditions of the fire scenarios, fire-induced damage could have a very small probability and could be dominated by the tail of the uncertainty distribution; hence, the accuracy of the correlation-based and variance-based methods is questionable. The moment-independent Global IM analysis in this research provides a better understanding of how experimental uncertainty data affects industry’s plant models and where improvements in that data will have the largest benefit for improving fire modeling accuracy in causing core damage. Among the five unscreened parameters obtained from the Morris EE analysis, the Global IM analysis results for the case study indicated that max heat release rate and fire location are the most important parameters. The report also outlines benefits of using a unified computational platform that integrates the underlying simulations (e.g., a fire progression model), quantitative screening (using the Morris EE method), and the Global IM analysis. A unified platform can (i) facilitate the ranking of input parameters considering multiple key fire scenarios simultaneously, rather than considering one scenario at a time, (ii) contribute to more explicit and accurate treatment of dependencies at multiple levels of Fire PRA, (iii) facilitate the sampling-based uncertainty quantification for Fire PRA, and (iv) help generating both “industry-wide” and "plant-specific" ranking of uncertainty sources in Fire PRA. Future research should be done to include additional parameters such as detection/suppression or cable fire spread. Adding a PRA software such as SAPHIRE to the RAVEN platform would help with plant model integration and improve treatment of fire-induced dependency. The I-PRA risk importance ranking methodology offered in this report can provide valuable information for efficiently (a) enhancing the realism of Fire PRA for existing plants and (b) supporting the development of Dynamic Fire PRA for advanced reactors and new plants.

97 MATHEMATICS AND COMPUTING↗

Predicting the Earth encounters of (99942) Apophis

Arecibo delay-Doppler measurements of (99942) Apophis in 2005 and 2006 resulted in a five standard-deviation trajectory correction to the optically predicted close approach distance to Earth in 2029. The radar measurements reduced the volume of the statistical uncertainty region entering the encounter to 7.3% of the pre-radar solution, but increased the trajectory uncertainty growth rate across the encounter by 800% due to the closer predicted approach to the Earth. A small estimated Earth impact probability remained for 2036. With standard-deviation plane-of-sky position uncertainties for 2007-2010 already less than 0.2 arcsec, the best near-term ground-based optical astrometry can only weakly affect the trajectory estimate. While the potential for impact in 2036 will likely be excluded in 2013 (if not 2011) using ground-based optical measurements, approximations within the Standard Dynamical Model (SDM) used to estimate and predict the trajectory from the current era are sufficient to obscure the difference between a predicted impact and a miss in 2036 by altering the dynamics leading into the 2029 encounter. Normal impact probability assessments based on the SDM become problematic without knowledge of the object's physical properties; impact could be excluded while the actual dynamics still permit it. Calibrated position uncertainty intervals are developed to compensate for this by characterizing the minimum and maximum effect of physical parameters on the trajectory. Uncertainty in accelerations related to solar radiation can cause between 82 and 4720 Earth-radii of trajectory change relative to the SDM by 2036. If an actionable hazard exists, alteration by 2-10% of Apophis' total absorption of solar radiation in 2018 could be sufficient to produce a six standard-deviation trajectory change by 2036 given physical characterization; even a 0.5% change could produce a trajectory shift of one Earth-radius by 2036 for all possible spin-poles and likely masses. Planetary ephemeris uncertainties are the next greatest source of systematic error, causing up to 23 Earth-radii of uncertainty. The SDM Earth point-mass assumption introduces an additional 2.9 Earth-radii of prediction error by 2036. Unmodeled asteroid perturbations produce as much as 2.3 Earth-radii of error. We find no future small-body encounters likely to yield an Apophis mass determination prior to 2029. However, asteroid (144898) 2004 VD17, itself having a statistical Earth impact in 2102, will probably encounter Apophis at 6.7 lunar distances in 2034, their uncertainty regions coming as close as 1.6 lunar distances near the center of both SDM probability distributions.

Orbit determination↗

Prospects for Habitable World Detections Using James Webb Space Telescope (JWST)

Doppler and transit surveys are finding extrasolar planets of ever smaller mass and radius, and are now sampling the domain of superEarths. Recent results from the Doppler surveys suggest that discovery of a transiting superEarth in the habitable zone of a lower main sequence star may be possible. We evaluate the prospects for an all-sky transit survey targeted to the brightest stars I that would find the most favorable cases for photometric and spectroscopic characterization using the James Webb Space Telescope. We use the proposed Transiting Exoplanet Survey Satellite (TESS) as representative of an all-sky survey. We couple the simulated TESS yield to a sensitivity model for the MIRI and NIRSpec instruments on JWST. Our sensitivity model includes all currently known and anticipated sources of random and systematic error for these instruments. We focus on the TESS planets with radii between Earth and Neptune. Our simulations consider secondary eclipse filter photometry using JWST/MIRI, comparing the 11- and 15- micron bands to measure carbon dioxide absorption in superEarths, as well as JWST!NIRSpec spectroscopy of water absorption from 1.7-3.0 microns, and carbon dioxide absorption at 4.3 microns. We find that JWST will be capable of characterizing dozens of TESS superEarths with temperatures above the habitable range, using both MIRI and NIRspec. We project that TESS will discover about eight nearby habitable transiting superEarths, all orbiting lower main sequence stars. The principal sources of uncertainty in the prospects for JWST characterization of habitable superEarths are superEarth frequency and the nature of superEarth atmospheres. Based on our estimates of these uncertainties, we project that JWST will be able to measure the temperature, and identify molecular absorptions (water, carbon dioxide) in one to four nearby habitable TESS superEarths orbiting lower main sequence stars.

Deming, Drake↗

Comparison of VIIRS Prelaunch RVS Among Independent Studies

The Visible Infrared Imaging Radiometer Suite (VIIRS) is a key sensor carried on the NPOESS (National Polar-orbiting Operational Environmental Satellite System), upgraded and developed recently from heritage instruments including AVHRR, OLS, MODIS, and SeaWiFS. It has on-board calibration components including a solar diffuser (SD) and a solar diffuser stability monitor (SDSM) for the reflective solar bands (RSB), a V-groove blackbody for the thermal emissive bands (TEB), and a space view (SV) port for background subtraction. These on-board calibrators are located at fixed scan angles. The VIIRS response versus scan angle (RVS) was characterized prelaunch in lab ambient conditions and will be used on-orbit to characterize the response for the all scan angles relative to the calibrator scan angle (SD for RSB and blackbody for TEB). Since the RVS is vitally important to the quality of calibrated radiance products, several independent studies were performed and their results were compared and validated. This document provides RVS results from three groups: the NPP Instrument Calibration Support Team (NICST), Raytheon, and the Aerospace Corporation. A comparison of the RVS results obtained using a 2nd order polynomial fit to measurement data is conducted for each band, detector, and half angle mirror (HAM) side. The associated RVS fitting residuals are examined and compared with the relative differences in RVS found between independent studies. Results show that the agreement is within 0.1% and comparable with fitting residuals for all bands except for RSB band M9, where a difference of 0.2% results from the application of the atmospheric water vapor correction for laboratory conditions during the test by Raytheon. NICST has slightly larger RSB RVS uncertainties but still well within the 0.3% total uncertainty allowed for the RVS characterization defined in the Performance Verification Plan.

Wu, Aisheng↗

Addressing and Presenting Quality of Satellite Data via Web-Based Services

With the recent attention to climate change and proliferation of remote-sensing data utilization, climate model and various environmental monitoring and protection applications have begun to increasingly rely on satellite measurements. Research application users seek good quality satellite data, with uncertainties and biases provided for each data point. However, different communities address remote sensing quality issues rather inconsistently and differently. We describe our attempt to systematically characterize, capture, and provision quality and uncertainty information as it applies to the NASA MODIS Aerosol Optical Depth data product. In particular, we note the semantic differences in quality/bias/uncertainty at the pixel, granule, product, and record levels. We outline various factors contributing to uncertainty or error budget; errors. Web-based science analysis and processing tools allow users to access, analyze, and generate visualizations of data while alleviating users from having directly managing complex data processing operations. These tools provide value by streamlining the data analysis process, but usually shield users from details of the data processing steps, algorithm assumptions, caveats, etc. Correct interpretation of the final analysis requires user understanding of how data has been generated and processed and what potential biases, anomalies, or errors may have been introduced. By providing services that leverage data lineage provenance and domain-expertise, expert systems can be built to aid the user in understanding data sources, processing, and the suitability for use of products generated by the tools. We describe our experiences developing a semantic, provenance-aware, expert-knowledge advisory system applied to NASA Giovanni web-based Earth science data analysis tool as part of the ESTO AIST-funded Multi-sensor Data Synergy Advisor project.

Leptoukh, Gregory↗