Results for “event data models” — Page 18

NASA NTRS1982

A possible common origin for the rare gases on Venus, earth, and Mars

The rare gas concentrations in the terrestrial planets and the carbonaceous chondrites could have been derived from a single source an early solar wind. The fractionation processes responsible for the varying concentration patterns in this model are differential ionization and the relative separation of neutrals from ions due to interaction with the solar magnetic field. If species dependent effects are small relative to radius dependent effects, this process can explain in detail both the relative and absolute abundances in the terrestrial planets and carbonaceous chondrites. The electron temperatures predicted by the data are consistent with astrophysical constraints previously derived for the solar nebula, and the timeline of events necessary for this model is consistent with accretion in a gas free environment. If species dependent fractionations are small, or approximately cancel each other, the earth is found to be outgassed by an order of magnitude more efficiently than Venus or Mars.

Hostetler, C. J.↗

NASA NTRS1989

Light scattering characteristics of various aerosol types derived from multiple wavelength lidar observations

The present study demonstrates the potential of a multiple-wavelength lidar for discriminating between several aerosol types on the basis of the wavelength dependence of the aerosol backscatter coefficient. The two-component lidar equation was solved under the assumption of similarity in the derived profiles of backscatter coefficients for each wavelength. It is shown that a three-wavelength lidar system operating at 300, 600, and 1064nm can provide unique information for discriminating between various aerosol types (continental, maritime, Saharan-dust, stratospheric aerosols in a tropopause fold event, and tropical forest aerosols). Mie calculations were made using in situ aerosol data and aerosol models to compare with the lidar results. The disagreement between the theoretical and empirical results in some cases was substantial. These differences may be partly due to uncertainties in the lidar data analysis and aerosol characteristics and also due to the conventional assumption of aerosol sphericity for the aerosol Mie calculations.

Sasano, Yasuhiro↗

NASA NTRS1994

Reliability analysis in the Office of Safety, Environmental, and Mission Assurance (OSEMA)

The technical personnel in the SEMA office are working to provide the highest degree of value-added activities to their support of the NASA Langley Research Center mission. Management perceives that reliability analysis tools and an understanding of a comprehensive systems approach to reliability will be a foundation of this change process. Since the office is involved in a broad range of activities supporting space mission projects and operating activities (such as wind tunnels and facilities), it was not clear what reliability tools the office should be familiar with and how these tools could serve as a flexible knowledge base for organizational growth. Interviews and discussions with the office personnel (both technicians and engineers) revealed that job responsibilities ranged from incoming inspection to component or system analysis to safety and risk. It was apparent that a broad base in applied probability and reliability along with tools for practical application was required by the office. A series of ten class sessions with a duration of two hours each was organized and scheduled. Hand-out materials were developed and practical examples based on the type of work performed by the office personnel were included. Topics covered were: Reliability Systems - a broad system oriented approach to reliability; Probability Distributions - discrete and continuous distributions; Sampling and Confidence Intervals - random sampling and sampling plans; Data Analysis and Estimation - Model selection and parameter estimates; and Reliability Tools - block diagrams, fault trees, event trees, FMEA. In the future, this information will be used to review and assess existing equipment and processes from a reliability system perspective. An analysis of incoming materials sampling plans was also completed. This study looked at the issues associated with Mil Std 105 and changes for a zero defect acceptance sampling plan.

Kauffmann, Paul J.↗

NASA NTRS2003

A statistical model of diurnal variation in human growth hormone

The diurnal pattern of growth hormone (GH) serum levels depends on the frequency and amplitude of GH secretory events, the kinetics of GH infusion into and clearance from the circulation, and the feedback of GH on its secretion. We present a two-dimensional linear differential equation model based on these physiological principles to describe GH diurnal patterns. The model characterizes the onset times of the secretory events, the secretory event amplitudes, as well as the infusion, clearance, and feedback half-lives of GH. We illustrate the model by using maximum likelihood methods to fit it to GH measurements collected in 12 normal, healthy women during 8 h of scheduled sleep and a 16-h circadian constant-routine protocol. We assess the importance of the model components by using parameter standard error estimates and Akaike's Information Criterion. During sleep, both the median infusion and clearance half-life estimates were 13.8 min, and the median number of secretory events was 2. During the constant routine, the median infusion half-life estimate was 12.6 min, the median clearance half-life estimate was 11.7 min, and the median number of secretory events was 5. The infusion and clearance half-life estimates and the number of secretory events are consistent with current published reports. Our model gave an excellent fit to each GH data series. Our analysis paradigm suggests an approach to decomposing GH diurnal patterns that can be used to characterize the physiological properties of this hormone under normal and pathological conditions.

Non-NASA Center↗

NASA NTRS2008

Radiometric Modeling and Calibration of the Geostationary Imaging Fourier Transform Spectrometer (GIFTS)Ground Based Measurement Experiment

The ultimate remote sensing benefits of the high resolution Infrared radiance spectrometers will be realized with their geostationary satellite implementation in the form of imaging spectrometers. This will enable dynamic features of the atmosphere s thermodynamic fields and pollutant and greenhouse gas constituents to be observed for revolutionary improvements in weather forecasts and more accurate air quality and climate predictions. As an important step toward realizing this application objective, the Geostationary Imaging Fourier Transform Spectrometer (GIFTS) Engineering Demonstration Unit (EDU) was successfully developed under the NASA New Millennium Program, 2000-2006. The GIFTS-EDU instrument employs three focal plane arrays (FPAs), which gather measurements across the long-wave IR (LWIR), short/mid-wave IR (SMWIR), and visible spectral bands. The GIFTS calibration is achieved using internal blackbody calibration references at ambient (260 K) and hot (286 K) temperatures. In this paper, we introduce a refined calibration technique that utilizes Principle Component (PC) analysis to compensate for instrument distortions and artifacts, therefore, enhancing the absolute calibration accuracy. This method is applied to data collected during the GIFTS Ground Based Measurement (GBM) experiment, together with simultaneous observations by the accurately calibrated AERI (Atmospheric Emitted Radiance Interferometer), both simultaneously zenith viewing the sky through the same external scene mirror at ten-minute intervals throughout a cloudless day at Logan Utah on September 13, 2006. The accurately calibrated GIFTS radiances are produced using the first four PC scores in the GIFTS-AERI regression model. Temperature and moisture profiles retrieved from the PC-calibrated GIFTS radiances are verified against radiosonde measurements collected throughout the GIFTS sky measurement period. Using the GIFTS GBM calibration model, we compute the calibrated radiances from data collected during the moon tracking and viewing experiment events. From which, we derive the lunar surface temperature and emissivity associated with the moon viewing measurements.

Tian, Jialin↗

NASA NTRS2012

Time Manager Software for a Flight Processor

Data analysis is a process of inspecting, cleaning, transforming, and modeling data to highlight useful information and suggest conclusions. Accurate timestamps and a timeline of vehicle events are needed to analyze flight data. By moving the timekeeping to the flight processor, there is no longer a need for a redundant time source. If each flight processor is initially synchronized to GPS, they can freewheel and maintain a fairly accurate time throughout the flight with no additional GPS time messages received. How ever, additional GPS time messages will ensure an even greater accuracy. When a timestamp is required, a gettime function is called that immediately reads the time-base register.

Zoerne, Roger↗

NASA NTRS2018

Introduction to NASA Goddard Workshop on Artificial Intelligence

Artificial Intelligence (AI) is a collection of advanced technologies that allows machines to think and act, both humanly and rationally, through sensing, comprehending, acting and learning. AI's foundations lie at the intersection of several traditional fields Philosophy, Mathematics, Economics, Neuroscience, Psychology and Computer Science. Although the inception of AI started in the 1950's, it has recently made a strong comeback in all aspects of society and all over the world; this is mainly due to the timely combination of increased data volumes, advanced and mature algorithms, and improvements in computing power and storage. Current AI applications include big data analytics, robotics, intelligent sensing, assisted decision making, and speech recognition just to name a few.This workshop will be investigating how AI technologies can be adapted or developed to address the following challenges: Discover events of interest and correlations in large amounts of science data; improve the outcomes of science modeling and data assimilation using improved data processing, integration, and analysis. Design advisors for mission planning and operations, including anomaly detection and spacecraft health monitoring. Develop tools for engineering support, including advanced manufacturing, orbit determination, new component design and system engineering. Customize intelligent user interfaces, including visual analytics and natural language processing.

Le Moigne, Jacqueline↗

NASA NTRSDate not supplied

SAGE III/ISS v5.3 Level 2 Data Product Changes and Improvements

The Stratospheric Aerosol and Gas Experiment on the International Space Station (SAGE III/ISS) is an occultation instrument that acquires measurements of aerosols and gases within the Earth’s stratosphere and upper troposphere. SAGE III/ISS provides level 2 solar species products for aerosol extinction (9 channels), nitrogen dioxide (NO2), ozone (O3), and water vapor (H2O). The level 2 products currently provide three O3 profiles based on differing retrievals. The first O3 profile is based on measurements at short wavelengths within the Hartley-Huggins band (MesO3), the second O3 profile is based on measurements made at visible wavelengths within the Chappius band (MLR O3), and the final profile is found using a more SAGE II like approach (AO3). The SAGE III/ISS also provides level 2 lunar species products for ozone (O3), nitrogen dioxide (NO2), and nitrogen trioxide (NO3). Version 5.3 of the SAGE III/ISS retrieval algorithm introduces improvements that affect the level 2 data products. The largest change to the solar algorithm is the implementation of disturbance monitoring package (DMP) corrections to improve pointing accuracy. The DMP is comprised of a miniature inertial measurement unit that measures rotation in inertial space using ring laser gyroscopes oriented about three orthogonal axes which can be used to correct pointing errors caused by mechanical disturbances. A major change common to solar and lunar algorithms is meteorological input from the coarser 42 level Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) model data to the 72 level MERRA-2 model data. The final major change involves improving the automated quality assurance (QA) algorithm to recover events that were withheld from the public release because differences between the AO3 and MLR O3 for some events after the eruption of Tonga–Hunga Haʻapai. Other changes for v5.3 include minor bug fixes as well as restoration of some data quality flags that were removed in v5.21. This presentation presents the impacts of these changes as well as overall observations of interest from v5.3 level 2 data products.

Carter Hulsey↗

NASA NTRSDate not supplied

SAGE III/ISS v5.3 Level 2 Data Product Changes and Improvements

The Stratospheric Aerosol and Gas Experiment on the International Space Station (SAGE III/ISS) is an occultation instrument that acquires measurements of aerosols and gases within the Earth’s stratosphere and upper troposphere. SAGE III/ISS provides level 2 solar species products for aerosol extinction (9 channels), nitrogen dioxide (NO2), ozone (O3), and water vapor (H2O). The level 2 products currently provide three O3 profiles based on differing retrievals. The first O3 profile is based on measurements at short wavelengths within the Hartley-Huggins band (MesO3), the second O3 profile is based on measurements made at visible wavelengths within the Chappius band (MLR O3), and the final profile is found using a more SAGE II like approach (AO3). The SAGE III/ISS also provides level 2 lunar species products for ozone (O3), nitrogen dioxide (NO2), and nitrogen trioxide (NO3). Version 5.3 of the SAGE III/ISS retrieval algorithm introduces improvements that affect the level 2 data products. The largest change to the solar algorithm is the implementation of disturbance monitoring package (DMP) corrections to improve pointing accuracy. The DMP is comprised of a miniature inertial measurement unit that measures rotation in inertial space using ring laser gyroscopes oriented about three orthogonal axes which can be used to correct pointing errors caused by mechanical disturbances. A major change common to solar and lunar algorithms is meteorological input from the coarser 42 level Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) model data to the 72 level MERRA-2 model data. The final major change involves improving the automated quality assurance (QA) algorithm to recover events that were withheld from the public release because differences between the AO3 and MLR O3 for some events after the eruption of Tonga–Hunga Haʻapai. Other changes for v5.3 include minor bug fixes as well as restoration of some data quality flags that were removed in v5.21. This presentation presents the impacts of these changes as well as overall observations of interest from v5.3 level 2 data products.

Carter Hulsey↗

NASA NTRSDate not supplied

Capturing Complex Multivariate Time Series Interactions to Detect High-Risk Adverse Events During Flight

The reduction of aviation safety metrics below target thresholds continue to drive down the number of aviation fatalities and accidents. To meet future safety demands, sustained efforts by aviation agencies promoting safety assurance processes and systems have prompted ongoing research on identifying and mitigating in-flight risks. With the projected increase in passenger load factor and rollout of more autonomous systems into the national airspace, the need to detect high-risk events in-time or ahead-of-time is becoming increasingly crucial. New anomaly detection and precursor identification algorithms will need to scale to different airframes, levels of autonomy, and system complexity. While the pervasiveness of deep learning has resulted in the development of performant anomaly detection methods, these sophisticated models currently suffer from low end-user interpretability. Building off our previous work on identifying adverse events in multivariate flight data during descent, we propose a data-driven approach for detecting in-flight adverse events caused by the complex interplay of flight variables. Our approach utilizes ordinal patterns of important aircraft stability variables (e.g., airspeed and descent rate) to capture multivariate flight dynamics that can be used to predict the onset of unstable approaches, a high-risk adverse event that can occur during approach. Through the use of ordinal patterns, we aim to create more interpretable detection models of in-flight adverse events that can be translated to future autonomous systems without difficulty. Our analysis shows the presence of distinct ordinal pattern distributions that can be used to predict unstable approaches 1 minute ahead of time with an accuracy of 0.69 and a recall of 0.73 and 30 seconds ahead with an accuracy of 0.70 and a recall of 0.86.

Risk detection↗

NASA NTRSDate not supplied

Capturing Multivariate Time Series Interactions to Detect High‑Risk Instability During Approach

The reduction of aviation safety metrics below target thresholds continue to drive down the number of aviation fatalities and accidents. To meet future safety demands, sustained efforts by aviation agencies promoting safety assurance processes and systems have prompted ongoing research on identifying and mitigating in-flight risks. With the projected increase in passenger load factor and rollout of more autonomous systems into the national airspace, the need to detect high-risk events in-time or ahead-of-time is becoming increasingly crucial. New anomaly detection and precursor identification algorithms will need to scale to different airframes, levels of autonomy, and system complexity. While the pervasiveness of deep learning has resulted in the development of performant anomaly detection methods, these sophisticated models currently suffer from low end-user interpretability. Building off our previous work on identifying adverse events in multivariate flight data during descent, we propose a data-driven approach for detecting in-flight adverse events caused by the complex interplay of flight variables. Our approach utilizes ordinal patterns of important aircraft stability variables (e.g., airspeed and descent rate) to capture multivariate flight dynamics that can be used to predict the onset of unstable approaches, a high-risk adverse event that can occur during approach. Through the use of ordinal patterns, we aim to create more interpretable detection models of in-flight adverse events that can be translated to future autonomous systems without difficulty. Our analysis shows the presence of distinct ordinal pattern distributions that can be used to predict unstable approaches 1 minute ahead of time with an accuracy of 0.69 and a recall of 0.73 and 30 seconds ahead with an accuracy of 0.70 and a recall of 0.86.

Risk detection↗

NASA NTRSDate not supplied

A Historical Overview of the NASA Orbital Debris Program Office’s Laboratory Optical Measurements

The NASA Orbital Debris Program Office (ODPO) has used laboratory measurements to help bring ground-based measurements together with models to ascertain Earth-orbiting target parameters of interest to support various orbital debris models. In 2005, the Optical Measurement Center (OMC) was established to simulate space-based illumination conditions using equipment and techniques that recreate telescopic observations, particularly source-target-sensor orientations. The intent was to recreate light curves using known aspect angles of known targets and phase angles (angle is defined by the vertex between illumination source-object-detector) to complement telescopic observations that could be used to update the current optical size estimation model (OSEM) – a model that converts object brightness into size for orbital debris models. To support the above goals, the laboratory has undergone several equipment upgrades to increase capabilities over almost 20 years of operation. The primary instrumentation acquires reflectance measurements and includes a solar-like light source, CCD camera with astrometric filters, and robotic arm. A rotary arm was added approximately five years after full operation to allow acquisition through a full 360° range of phase angles. Another part of the OMC instrumentation is a field spectrometer, predominately used for field operations to acquire pre- and post-flight spacecraft material spectral measurements. Additionally, reflectance spectroscopy of various materials is also of interest resulting from hypervelocity impact tests, pristine spacecraft materials, or samples of materials that are used in spacecraft design. These measurements are stored in NASA’s Spectral Material Database, a resource that is still being populated today. The study of spectral measurements also enabled the development of spectral unmixing routines to support the identification of spacecraft materials from spectral data gathered by ground based telescopes. Preliminary OMC investigations focused on feasibility studies to acquire 360° rotation light curves of simple shapes at a single-phase angle and extended to measurements of representative fragments from ground-based explosion tests. To correlate the light curves with ground-based optical measurements, a focused study on high area to mass materials was conducted in support of a newly identified population (at the time) in geosynchronous orbit (GEO) consisting of multi-layered insulation. To further characterize orbital debris, a larger selection of materials was analyzed using laboratory photometric measurements that included representative targets from pristine spacecraft materials and ground-based impact tests. Around 2012, an initiative was requested to understand the feasibility of active debris removal (ADR) of larger targets using grappling methods for spent rocket bodies. Using a priori information on selected targets, scaled-down versions of rocket bodies were generated thanks to improvements in 3D printing technology and machining. These targets were studied in the OMC to understand rotation characteristics. These were compared with telescopic data to determine if the tumble and rotation angles would allow ADR. In 2013, the OMC focused on combining spectral measurements with photometric data to characterize GEO orbital debris. Several years later, NASA acquired a Titan III Transtage test article from “The Boneyard” with a high-resemblance to on-orbit Titan III Transtage rocket bodies, allowing physical access to a representative rocket body that suffered fragmentations in GEO. This prompted the creation of 3D models using lidar technology and spectral measurements of the materials. Focused research also transitioned to specific materials (i.e., solar cells) when telescopic surveys requested characterization of specific GEO targets. In the different research products presented, the focus has been to understand the various parameters that influence optical size estimation, including albedo, phase functions, and brightness variations. Work in this area continues with newer sources of data, including DebriSat, a high-fidelity 56-kg spacecraft replica representative of a modern low Earth orbit (LEO) satellite subjected to a laboratory hypervelocity impact test to understand fragmentation events and to support updates to satellite breakup models and size estimation models. Utilizing the vast population of fragments from DebriSat and prior laboratory impact experiments, the ODPO has focused on acquiring bidirectional reflectance distribution function (BRDF) data to characterize targets in the laboratory, thus removing aspect angle dependencies. Additionally, the DebriSat project has provided improved processes for measuring size via image acquisition, such that a true fragment size can be directly compared to the derived size using the OSEM. The team continues to assess BRDFs and use spectral measurement data to investigate the parameters used in the OSEM, specifically magnitudes, albedo, and phase functions.

Heather Cowardin↗

NASA NTRS2019

Analysis of the DebriSat Fragments and Comparison to the NASA Standard Satellite Breakup Model

Existing DOD and NASA satellite breakup models are based on a key laboratory test, the 1992 Satellite Orbital debris Characterization Impact Test, which has supported many applications and matched on-orbit events involving older satellite designs reasonably well over the years. In 2014, the NASA Orbital Debris Program Office, in collaboration with the Air Force Space and Missile Systems Center, The Aerospace Corporation, and the University of Florida, conducted a hypervelocity impact test using a high-fidelity, mock-up satellite, DebriSat, in controlled and instrumented laboratory conditions to update and improve these models. DebriSat is representative of present-day, low Earth orbit satellites, having been constructed with modern spacecraft materials and techniques. The DebriSat fragment ensemble provided a variety of shapes, bulk densities, and dimensions. Fragments down to 2 mm in size are being characterized by their physical and derived properties. The data will inform updates to the current NASA Standard Satellite Breakup Model (SSBM), which was formulated using laboratory and ground-based measurements of on-orbit fragmentation events to describe an average breakup for spacecraft and upper-stage collisions and explosions. Although individual fragment collection and characterization is still ongoing, this paper will present the status of the DebriSat fragment data analysis including cumulative characteristic length and cumulative mass distributions, area-to-mass distributions, and characteristic length versus mass distributions. Additionally, comparisons to the NASA SSBM will be presented.

Murray, James↗

NASA NTRS1994

FNAS UAH UVSP and complimentary data analysis and modeling

Several data sets obtained by the UVSP and HXIS instruments on board SMM were studied. From these data, several events of sequences of events were selected that are specially representative of the plethora of active region energetic phenomena that was simultaneously observed by UVSP and HXIS. The selection criteria was to have available both types of observations in active region flares that are not so bright that the UVSP instrument was switched off for protection, and at the same time flares that are not so dim in X-Rays that HXIS will have collected significant data. Another criteria of secondary importance was to select events where auxiliary optical data is available.

Fontenla, Juan M.↗

NASA NTRS2000

Significant Findings: Tracking the SeaWiFS Record with a Coupled Physical/Biogeochemical/Radiative Model of the Global Oceans

The Sea-Viewing Wide Field-of-view Sensor (SeaWiFS) has observed 2.5 years of routine global chlorophyll observations from space. The mission was launched into a record El Nino event, which eventually gave way to one of the most intensive and longest-lasting La Nina events ever recorded. The SeaWiFS chlorophyll record captured the response of ocean phytoplankton to these significant events in the tropical Indo-Pacific basins, but also indicated significant interannual variability unrelated to the El Nino/La Nina events. This included large variability in the North Atlantic and Pacific basins, in the North Central and equatorial Atlantic, and milder patterns in the North Central Pacific. This SeaWiFS record was tracked with a coupled physical/biogeochemical/radiative model of the global oceans using near-real-time forcing data such as wind stresses, sea surface temperatures, and sea ice. This provided an opportunity to offer physically and biogeochemically meaningful explanations of the variability observed in the SeaWiFS data set, since the causal mechanisms and interrelationships of the model are completely understood. The coupled model was able to represent the seasonal distributions of chlorophyll during the SeaWiFS era, and was capable of differentiating among the widely different processes and dynamics occurring in the global oceans. The model was also reasonably successful in representing the interannual signal, especially when it was large, such as, the El Nino and La Nina events in the tropical Pacific and Indian Oceans. The model provided different phytoplankton group responses for the different events in these regions: diatoms were predominant in the tropical Pacific during the La Nina but other groups were predominant during El Nino. The opposite condition occurred in the tropical Indian Ocean. Both situations were due to the different responses of the basins to El Nino. The interannual variability in the North Atlantic, which was exhibited in SeaWiFS data as a decline in the spring/summer bloom in 1999 relative to 1998, resulted in the model from a more slowly shoaling mixed layer, allowing herbivore populations to keep pace with increasing phytoplankton populations. However, several aspects of the interannual cycle were not well-represented by the model. Explanations ranged from inherent model deficiencies, to monthly averaging of forcing fields, to biases in SeaWiFS atmospheric correction procedures.

Watson, Gregg W.↗

NASA NTRS2012

Validation of Satellite-Based Objective Overshooting Cloud-Top Detection Methods Using CloudSat Cloud Profiling Radar Observations

Two satellite infrared-based overshooting convective cloud-top (OT) detection methods have recently been described in the literature: 1) the 11-mm infrared window channel texture (IRW texture) method, which uses IRW channel brightness temperature (BT) spatial gradients and thresholds, and 2) the water vapor minus IRW BT difference (WV-IRW BTD). While both methods show good performance in published case study examples, it is important to quantitatively validate these methods relative to overshooting top events across the globe. Unfortunately, no overshooting top database currently exists that could be used in such study. This study examines National Aeronautics and Space Administration CloudSat Cloud Profiling Radar data to develop an OT detection validation database that is used to evaluate the IRW-texture and WV-IRW BTD OT detection methods. CloudSat data were manually examined over a 1.5-yr period to identify cases in which the cloud top penetrates above the tropopause height defined by a numerical weather prediction model and the surrounding cirrus anvil cloud top, producing 111 confirmed overshooting top events. When applied to Moderate Resolution Imaging Spectroradiometer (MODIS)-based Geostationary Operational Environmental Satellite-R Series (GOES-R) Advanced Baseline Imager proxy data, the IRW-texture (WV-IRW BTD) method offered a 76% (96%) probability of OT detection (POD) and 16% (81%) false-alarm ratio. Case study examples show that WV-IRW BTD.0 K identifies much of the deep convective cloud top, while the IRW-texture method focuses only on regions with a spatial scale near that of commonly observed OTs. The POD decreases by 20% when IRW-texture is applied to current geostationary imager data, highlighting the importance of imager spatial resolution for observing and detecting OT regions.

Bedka, Kristopher M.↗

NASA NTRS2013

Analysis of Runway Incursion Data

A statistical analysis of runway incursion (RI) events was conducted to ascertain relevance to the top ten challenges of the National Aeronautics and Space Administration Aviation Safety Program (AvSP). The information contained in the RI database was found to contain data that may be relevant to several of the AvSP top ten challenges. When combined with other data from the FAA documenting air traffic volume from calendar year 2000 through 2011, the structure of a predictive model emerges that can be used to forecast the frequency of RI events at various airports for various classes of aircraft and under various environmental conditions.

Runway incursions↗

NASA NTRS2013

TPSAS-NF1676L-17064-DND

A statistical analysis of runway incursion (RI) events was conducted to ascertain relevance to the top ten challenges of the National Aeronautics and Space Administration Aviation Safety Program (AvSP). The information contained in the RI database was found to contain data that may be relevant to several of the AvSP top ten challenges. When combined with other data from the FAA documenting air traffic volume from calendar year 2000 through 2011, the structure of a predictive model emerges that can be used to forecast the frequency of RI events at various airports for various classes of aircraft and under various environmental conditions.

Runway incursions↗