Results for “data analysis, visualization” — Page 98

NASA NTRSDate not supplied

Natural Language Processing (NLP) Analysis of NOTAMs for Air Traffic Management Optimization

With new emerging technologies in the field of NLP, we explore their applications to digitize and analyze heritage Air Traffic Management (ATM) documents for planning and optimizing airspace operations. Specifically, this research focuses on harvesting semi-structured or un-structured information contained in Notices to Airmen (NOTAMs). Using NLP and other advanced data analytics, we will construct a data-driven framework which facilitates finding language patterns and the use of pretrained language models for classification and extraction of useful airspace constraints and restrictions. These may lead to tools that assist airspace users in understanding the constraints more efficiently, contributing to better route planning and safer execution. This paper explores three workflows entailing different NLP tasks. First, unsupervised techniques like word embedding and topic modeling are used for pattern finding and document classification. Second, a dataset is created by extracting information from the semi-structured NOTAM format as metadata for categorizing, visualizing, and extracting key entities driving NOTAM content. Third, modern pre-built deep learning based transformer models such as BERT, RoBERTa, and XLNet are evaluated on the question answering task, an even more robust approach to information extraction, as well as their respective fine-tuning tasks. In this work we include various performance metrics for the trained models to evaluate both accuracy and precision and we show that the models can be generalized for their respective tasks. The research work developed shows promise in uncovering trends in digital NOTAMs in the NAS and also offers a new framework for digitizing and inferring insights from free-form legacy NOTAMs, that are yet to be digitized. Video is an mp4 download, with a play time of 9 min 35 secs.

Natural Language Processing↗

NASA NTRS1981

Analytical and experimental study of flow phenomena in noncavitating rocket pump inducers

The flow processes in rocket pump inducers are summarized. The experimental investigations were carried out with air as the test medium. The major characteristics features of the rocket pump inducers are low flow coefficient (0.05 to 0.2) large stagger angle (70 deg to 85 deg) and high solidity blades of little or no camber. The investigations are concerned with the effect of viscosity not the effects of cavitation. Flow visualization, conventional and hot wire probe measurement inside and at the exit of the blade passage, were the analytical methods used. The experiment was carried out using four three and two bladed inducers with cambered blades. Both the passage and the exit flow were measured. The basic research and boundary layer investigation was carried out using a helical flat plate (of some dimensions as the inducer blades tested), and flat plate helical inducer (four bladed). Detailed mean and turbulence flow field inside the passage as well as the exit of the rotor were derived from these measurement. The boundary layer, endwall, and other passage data reveal extremely complex nature of the flow, with major effects of viscosity present across the entire passage. Several analyses were carried out to predict the flow field in inducers. These included an approximate analysis, the shear pumping analysis, and a numerical solution of exact viscous equations with approximate modeling for the viscous terms.

Lakshminarayana, B.↗

NASA NTRS2006

Fluorescent Applications to Crystallization

By covalently modifying a subpopulation, less than or equal to 1%, of a macromolecule with a fluorescent probe, the labeled material will add to a growing crystal as a microheterogeneous growth unit. Labeling procedures can be readily incorporated into the final stages of purification, and tests with model proteins have shown that labeling u to 5 percent of the protein molecules does not affect the X-ray data quality obtained . The presence of the trace fluorescent label gives a number of advantages. Since the label is covalently attached to the protein molecules, it "tracks" the protein s response to the crystallization conditions. The covalently attached probe will concentrate in the crystal relative to the solution, and under fluorescent illumination crystals show up as bright objects against a darker background. Non-protein structures, such as salt crystals, do not show up under fluorescent illumination. Crystals have the highest protein concentration and are readily observed against less bright precipitated phases, which under white light illumination may obscure the crystals. Automated image analysis to find crystals should be greatly facilitated, without having to first define crystallization drop boundaries as the protein or protein structures is all that shows up. Fluorescence intensity is a faster search parameter, whether visually or by automated methods, than looking for crystalline features. Preliminary tests, using model proteins, indicates that we can use high fluorescence intensity regions, in the absence of clear crystalline features or "hits", as a means for determining potential lead conditions. A working hypothesis is that more rapid amorphous precipitation kinetics may overwhelm and trap more slowly formed ordered assemblies, which subsequently show up as regions of brighter fluorescence intensity. Experiments are now being carried out to test this approach using a wider range, of proteins. The trace fluorescently labeled crystals will also emit with sufficient intensity to aid in the automation of crystal alignment using relatively low cost optics, further increasing throughput at synchrotrons.

Pusey, Marc L.↗

NASA NTRS2021

Dense 3D-Reconstruction from Monocular Image Sequences for Computationally Constrained UAS

The ability to find safe landing sites over complex 3D terrain is an essential safety feature for fully autonomous small unmanned aerial systems (UAS), which requires on-board perception for 3D reconstruction and terrain analysis if the overflown terrain is unknown. This is a challenge for UAS that are limited in size, weight and computational power, such as small rotorcrafts executing autonomous missions on Earth, or in planetary applications such as the Mars Helicopter. For such a computationally constraint system, we propose a structure from motion approach that uses inputs from a single downward facing camera to produce dense point clouds of the overflown terrain in real time. In contrast to existing approaches, our method uses metric pose information from a visual-inertial odometry algorithm as camera pose priors, which allows deploying a fast pose refinement step to align camera frames such that a conventional stereo algorithm can be used for dense 3D reconstruction. We validate the performance of our approach with extensive evaluations in simulation, and demonstrate the feasibility with data from UAS flights.

Brockers, Roland↗

NASA NTRSDate not supplied

Natural Language Processing Analysis of Notices to Airmen for Air Traffic Management Optimization

With new emerging technologies in the field of NLP, we explore their applications to digitize and analyze heritage Air Traffic Management (ATM) documents for planning and optimizing airspace operations. Specifically, this research focuses on harvesting semi-structured or un-structured information contained in Notices to Airmen (NOTAMs). Using NLP and other advanced data analytics, we will construct a data-driven framework which facilitates finding language patterns and the use of pretrained language models for classification and extraction of useful airspace constraints and restrictions. These may lead to tools that assist airspace users in understanding the constraints more efficiently, contributing to better route planning and safer execution. This paper explores three workflows entailing different NLP tasks. First, unsupervised techniques like word embedding and topic modeling are used for pattern finding and document classification. Second, a dataset is created by extracting information from the semi-structured NOTAM format as metadata for categorizing, visualizing, and extracting key entities driving NOTAM content. Third, modern pre-built deep learning based transformer models such as BERT, RoBERTa, and XLNet are evaluated on the question answering task, an even more robust approach to information extraction, as well as their respective fine-tuning tasks. In this work we include various performance metrics for the trained models to evaluate both accuracy and precision and we show that the models can be generalized for their respective tasks. The research work developed shows promise in uncovering trends in digital NOTAMs in the NAS and also offers a new framework for digitizing and inferring insights from free-form legacy NOTAMs, that are yet to be digitized.

Natural Language Processing↗

NASA NTRS2012

System-of-Systems Technology-Portfolio-Analysis Tool

Advanced Technology Life-cycle Analysis System (ATLAS) is a system-of-systems technology-portfolio-analysis software tool. ATLAS affords capabilities to (1) compare estimates of the mass and cost of an engineering system based on competing technological concepts; (2) estimate life-cycle costs of an outer-space-exploration architecture for a specified technology portfolio; (3) collect data on state-of-the-art and forecasted technology performance, and on operations and programs; and (4) calculate an index of the relative programmatic value of a technology portfolio. ATLAS facilitates analysis by providing a library of analytical spreadsheet models for a variety of systems. A single analyst can assemble a representation of a system of systems from the models and build a technology portfolio. Each system model estimates mass, and life-cycle costs are estimated by a common set of cost models. Other components of ATLAS include graphical-user-interface (GUI) software, algorithms for calculating the aforementioned index, a technology database, a report generator, and a form generator for creating the GUI for the system models. At the time of this reporting, ATLAS is a prototype, embodied in Microsoft Excel and several thousand lines of Visual Basic for Applications that run on both Windows and Macintosh computers.

O'Neil, Daniel↗

NASA NTRS2014

Are Medications Involved in Vision and Intracrancial Pressure Changes Seen in Spaceflight?

The Food and Drug Association Adverse Event Reports (FDA AER) from 2009-2011 were used to create a database from millions of known and suspected medication-related adverse events among the general public. Vision changes, sometimes associated with intracranial pressure changes (VIIP), have been noted in some long duration crewmembers. Changes in vision and blood pressure (which can subsequently affect intracranial pressure) are fairly common side effects of medications. The purpose of this study was to explore the possibility of medication involvement in crew VIIP symptoms. Reports of suspected medication-related adverse events may be filed with the Food and Drug Administration (FDA) by medical professionals or consumers. Quarterly compilations of these reports are available for public download. Adverse Event Reporting System (AERS) reports from 1/1/2009- 6/30/2012 were downloaded and compiled into a searchable database for this study. Reports involving individuals under the age of 18 and older than 65 were excluded from this analysis. Case reports involving chronic diseases such as cancer, diabetes, multiple sclerosis and other serious conditions were also excluded. A scan of the medical literature for medication-related VIIP-like adverse events was used to create a list of suspect medications. These medications, as well as certain medications used frequently by ISS crew, were used to query the database. Queries for use of suspected medications were run, and the nature of the symptoms reported in those cases were tabulated. Symptoms searched in the FDA AERS were chosen to include the typical symptoms noted in crewmembers with VIIP. Vision symptoms searched were: visual acuity reduced, visual impairment, and vitreous floaters. Pressure changes included: abnormal sensation in eye, intracranial pressure increased, intraocular pressure increased, optic neuritis, optic neuropathy, and papilloedema. Limited demographic information is included with the FDA AERS; relevant data were also sorted by age and sex from each report. RESULTS Steroid-containing oral contraceptives had the highest number of reports associated with vision (n=166) and pressure symptoms (n=54). Corticosteroid-containing medications were also high; prednisone, for example, had 137 reports of vision issues and 79 of pressure issues. Pain relievers were also a medication class with vision and pressure-related adverse events reported. Common over-the-counter medications such as acetaminophen, aspirin and ibuprofen each had multiple reports for both vision and pressure symptoms. Antimicrobial medications ciprofloxacin and diflucan were also associated with a number of vision and pressure-related AERS. Unexpectedly, pseudoephedrine and promethazine were mentioned in fewer than 20 reports each over the 3.5 years of data examined. The FDA AERS represents a wealth of data, but there are several limitations to its use. The data are entered by the public or medical professionals, but are not checked for accuracy or completeness and may even be entered multiple times. The causal relationship between a particular adverse event and a particular medication is not tested. The cases represent a broad spectrum of demographics, occupations, and health histories, and thus do not model the astronaut population well. There is no information on the frequency of use of a medication for comparison purposes; it is not possible to assign a rate for any particular adverse event. Nonetheless, there are compelling trends. Use of corticosteroid-containing medications, pain relievers (even over-the-counter), and oral contraceptives were associated with higher numbers of vision- or intracranial pressure-related adverse events. In general, there were more vision problems than pressure problems reported. Certain medications that were once suspected of playing a role in the crew VIIP syndrome, namely pseudoephedrine and promethazine, were found to have extremely low numbers of VIIP-like AERS in the FDA data. However, crew use of corticosteroid-containing medications and pain relievers may warrant additional investigation

Faust, K. M.↗

NASA NTRS2019

A Segmentation Algorithm for Characterizing Rise and Fall Segments in Seasonal Cycles: An Application to XCO2 to Estimate Benchmarks and Assess Model Bias

There is more useful information in the time series of satellite-derived column-averaged carbon dioxide (XCO2) than is typically characterized. Often, the entire time series is treated at once without considering detailed features at shorter timescales, such as nonstationary changes in signal characteristics – amplitude, period and phase. In many instances, signals are visually and analytically differentiable from other portions in a time series. Each rise (increasing) and fall (decreasing) segment in the seasonal cycle is visually discernable in a graph of the time series. The rise and fall segments largely result from seasonal differences in terrestrial ecosystem production, which means that the segment's signal characteristics can be used to establish observational benchmarks because the signal characteristics are driven by similar underlying processes. We developed an analytical segmentation algorithm to characterize the rise and fall segments in XCO2 seasonal cycles. We present the algorithm for general application of the segmentation analysis and emphasize here that the segmentation analysis is more generally applicable to cyclic time series. We demonstrate the utility of the algorithm with specific results related to the comparison between satellite- and model-derived XCO2 seasonal cycles (2009–2012) for large bioregions across the globe. We found a seasonal amplitude gradient of 0.74–0.77 ppm for every 10∘ of latitude in the satellite data, with similar gradients for rise and fall segments. This translates to a south–north seasonal amplitude gradient of 8 ppm for XCO2, about half the gradient in seasonal amplitude based on surface site in situ CO2 data (∼19 ppm). The latitudinal gradients in the period of the satellite-derived seasonal cycles were of opposing sign and magnitude (−9 d per 10∘ latitude for fall segments and 10 d per 10∘ latitude for rise segments) and suggest that a specific latitude (∼2∘ N) exists that defines an inversion point for the period asymmetry. Before (after) the point of asymmetry inversion, the periods of rise segments are lesser (greater) than the periods of fall segments; only a single model could reproduce this emergent pattern. The asymmetry in amplitude and the period between rise and fall segments introduces a novel pattern in seasonal cycle analyses, but, while we show these emergent patterns exist in the data, we are still breaking ground in applying the information for science applications. Maybe the most useful application is that the segmentation analysis allowed us to decompose the model biases into their correlated parts of biases in amplitude, period and phase independently for rise and fall segments. We offer an extended discussion on how such information about model biases and the emergent patterns in satellite-derived seasonal cycles can be used to guide future inquiry and model development.

Calle, Leonardo↗

NASA NTRS2019

A Segmentation Algorithm for Characterizing Rise and Fall Segments in Seasonal Cycles: an Application to Xco2 to Estimate Benchmarks and Assess Model Bias

There is more useful information in the time series of satellite-derived column-averaged carbon dioxide (XCO2) than is typically characterized. Often, the entire time series is treated at once without considering detailed features at shorter timescales, such as nonstationary changes in signal characteristics – amplitude, period and phase. In many instances, signals are visually and analytically differentiable from other portions in a time series. Each rise (increasing) and fall (decreasing) segment in the seasonal cycle is visually discernable in a graph of the time series. The rise and fall segments largely result from seasonal differences in terrestrial ecosystem production, which means that the segment's signal characteristics can be used to establish observational benchmarks because the signal characteristics are driven by similar underlying processes. We developed an analytical segmentation algorithm to characterize the rise and fall segments in XCO2 seasonal cycles. We present the algorithm for general application of the segmentation analysis and emphasize here that the segmentation analysis is more generally applicable to cyclic time series. We demonstrate the utility of the algorithm with specific results related to the comparison between satellite- and model-derived XCO2 seasonal cycles (2009–2012) for large bioregions across the globe. We found a seasonal amplitude gradient of 0.74–0.77 ppm for every 10∘ of latitude in the satellite data, with similar gradients for rise and fall segments. This translates to a south–north seasonal amplitude gradient of 8 ppm for XCO2, about half the gradient in seasonal amplitude based on surface site in situ CO2 data (∼19 ppm). The latitudinal gradients in the period of the satellite-derived seasonal cycles were of opposing sign and magnitude (−9 d per 10∘ latitude for fall segments and 10 d per 10∘ latitude for rise segments) and suggest that a specific latitude (∼2∘ N) exists that defines an inversion point for the period asymmetry. Before (after) the point of asymmetry inversion, the periods of rise segments are lesser (greater) than the periods of fall segments; only a single model could reproduce this emergent pattern. The asymmetry in amplitude and the period between rise and fall segments introduces a novel pattern in seasonal cycle analyses, but, while we show these emergent patterns exist in the data, we are still breaking ground in applying the information for science applications. Maybe the most useful application is that the segmentation analysis allowed us to decompose the model biases into their correlated parts of biases in amplitude, period and phase independently for rise and fall segments. We offer an extended discussion on how such information about model biases and the emergent patterns in satellite-derived seasonal cycles can be used to guide future inquiry and model development.

segmentation algorithm↗

NASA NTRS2015

Flight Deck Interval Management Avionics: Eye-Tracking Analysis

Interval Management (IM) is one NexGen method for achieving airspace efficiencies. In order to initiate IM procedures, Air Traffic Control provides an IM clearance to the IM aircraft's pilots that indicates an intended spacing from another aircraft (the target to follow - or TTF) and the point at which this should be achieved. Pilots enter the clearance in the flight deck IM (FIM) system; and once the TTF's Automatic Dependent Surveillance-Broadcast signal is available, the FIM algorithm generates target speeds to meet that IM goal. This study examined four Avionics Conditions (defined by the instrumentation and location presenting FIM information) and three Notification Methods (defined by the visual and aural alerts that notified pilots to IM-related events). Current commercial pilots flew descents into Dallas/Fort-Worth in a high-fidelity commercial flight deck simulation environment with realistic traffic and communications. All 12 crews experienced each Avionics Condition, where order was counterbalanced over crews. Each crew used only one of the three Notification Methods. This paper presents results from eye tracking data collected from both pilots, including: normalized number of samples falling within FIM displays, normalized heads-up time, noticing time, dwell time on first FIM display look after a new speed, a workload-related metric, and a measure comparing the scan paths of pilot flying and pilot monitoring; and discusses these in the context of other objective (vertical and speed profile deviations, response time to dial in commanded speeds, out-of-speed-conformance and reminder indications) and subjective measures (workload, situation awareness, usability, and operational acceptability).

Latorella, Kara↗

NASA NTRS1993

Theory for noise of propellers in angular inflow with parametric studies and experimental verification

This report presents the derivation of a frequency domain theory and working equations for radiation of propeller harmonic noise in the presence of angular inflow. In applying the acoustic analogy, integration over the tangential coordinate of the source region is performed numerically, permitting the equations to be solved without approximation for any degree of angular inflow. Inflow angle is specified in terms of yaw, pitch, and roll angles of the aircraft. Since these can be arbitrarily large, the analysis applies with equal accuracy to propellers and helicopter rotors. For thickness and loading, the derivation is given in complete detail with working equations for near and far field. However, the quadrupole derivation has been carried only far enough to show feasibility of the numerical approach. Explicit formulas are presented for computation of source elements, evaluation of Green's functions, and location of observer points in various visual and retarded coordinate systems. The resulting computer program, called WOBBLE has been written in FORTRAN and follows the notation of this report very closely. The new theory is explored to establish the effects of varying inflow angle on axial and circumferential directivity. Also, parametric studies were performed to evaluate various phenomena outside the capabilities of earlier theories, such as an unsteady thickness effect. Validity of the theory was established by comparison with test data from conventional propellers and Prop Fans in flight and in wind tunnels under a variety of operating conditions and inflow angles.

Propeller acoustics↗

NASA NTRS2005

STS-114: Discovery Return to Flight Imagery Briefing

George Diller, from NASA Public Affairs, introduces Bob Page who is the chairman of the NASA Intercenter Photo Working Group. Page discusses the purpose of the group which is to perform an analysis of all imagery captured of spacecraft launches. He presents charts to discuss return to flight optics. The charts consist of: 1) Overall Shuttle Imagery Plan; 2) Baseline Configuration Imagery; 3) Upgraded Operational Television (OTV) Cameras; 4) Mobile Launch Platform Cameras; 5) Fixed Service Structure Cameras; 6) Kimeto Tracking Mount (KMT); 7) MOTS (Mobile Optical Tracking System); 8) Intermediate Focal Length Optical Tracker (IFLOT); 9) Distant Object Attitude Measurement System (DOAMS); 10) Advanced Transportable Optical Tracking System (ATOTS); 11) STS-114 Pad Fixed Tracker Camera Configuration; 12) STS-114 Camera Medium and Long Range Tracker Configuration; 13) WB-57 Ascent Visualization Experiment (WAVE); 14) WAVE Profile, Plan View, CAD Flight Training, and Test Flight; 15) Minimum STS-114 Flight Cameras Configuration; 16) External Tank (ET) Separation Cameras; 17) ET Imagery; Umbilical and Handheld; 18) On-Orbit Imagery; and 19) Imagery Data Distribution Plan. The presentation ends with a brief question and answer period.

Source record↗

NASA NTRS2021

EnDEVR: An Environment for Data Engineering in VR

Organizations within the public and private sectors are looking to improve their data science operations for research, development, and operational purposes. As this interest in data science grows, so too does interest in tools and programs that facilitating such operations. This paper presents EnDEVR, the Environment for Data Engineering in Virtual Reality, a user-extensible system that allows for the execution of custom data science functions. We describe the user-centered design process for the EnDEVR system, based on the needs of subject matter experts within the aerospace and aviation industries. Fundamental to this design is a method for users to integrate their existing analysis code (that may leverage third-party vendors) as usable objects in the virtual environment; this allows organizations to operate without the price tag and lifetime obligations associated with commercial data science toolkits, a concept known as “vendor lock-in”. We then present our system design with iterative implementations, and conclude with lessons learned. The presented work may provide insight into future user-centered design processes for VR applications, particularly for digital transformation stakeholders to circumvent vendor lock-in.

Human-centered computing↗

NASA NTRS2011

Locomotor Adaptation Improves Balance Control, Multitasking Ability and Reduces the Metabolic Cost of Postural Instability

During exploration-class missions, sensorimotor disturbances may lead to disruption in the ability to ambulate and perform functional tasks during the initial introduction to a novel gravitational environment following a landing on a planetary surface. The overall goal of our current project is to develop a sensorimotor adaptability training program to facilitate rapid adaptation to these environments. We have developed a unique training system comprised of a treadmill placed on a motion-base facing a virtual visual scene. It provides an unstable walking surface combined with incongruent visual flow designed to enhance sensorimotor adaptability. Greater metabolic cost incurred during balance instability means more physical work is required during adaptation to new environments possibly affecting crewmembers? ability to perform mission critical tasks during early surface operations on planetary expeditions. The goal of this study was to characterize adaptation to a discordant sensory challenge across a number of performance modalities including locomotor stability, multi-tasking ability and metabolic cost. METHODS: Subjects (n=15) walked (4.0 km/h) on a treadmill for an 8 -minute baseline walking period followed by 20-minutes of walking (4.0 km/h) with support surface motion (0.3 Hz, sinusoidal lateral motion, peak amplitude 25.4 cm) provided by the treadmill/motion-base system. Stride frequency and auditory reaction time were collected as measures of locomotor stability and multi-tasking ability, respectively. Metabolic data (VO2) were collected via a portable metabolic gas analysis system. RESULTS: At the onset of lateral support surface motion, subj ects walking on our treadmill showed an increase in stride frequency and auditory reaction time indicating initial balance and multi-tasking disturbances. During the 20-minute adaptation period, balance control and multi-tasking performance improved. Similarly, throughout the 20-minute adaptation period, VO2 gradually decreased following an initial increase after the onset of support surface motion. DISCUSSION: Resu lts confirmed that walking in discordant conditions not only compromises locomotor stability and the ability to multi-task, but comes at a quantifiable metabolic cost. Importantly, like locomotor stability and multi-tasking ability, metabolic expenditure while walking in discordant sensory conditions improved during adaptation. This confirms that sensorimotor adaptability training can benefit multiple performance parameters central to the successful completion of critical mission tasks.

Bloomberg, J. J.↗

NASA NTRS2018

Mitigation of Polystyrene Microsphere Surface Contamination for Wind Tunnel Applications

Polystyrene latex (PSL) microspheres have been utilized as seed material for flow visualization in wind tunnels. However, PSL microspheres have been observed to strongly adhere to wind tunnel and model surfaces. Surface contamination on the cleaning screens that remove vorticity and provide laminar flow in the test section, is particularly problematic. Agglomeration of particles on these screens cause constriction of the airflow through the screen resulting in inconsistent airflow properties in the test section. The adhesion mechanism of PSL microspheres to wind tunnel screens and 316 stainless steel flat plates, were evaluated in a contamination apparatus where small sections of screen material were exposed to PSL-seeded airflow. Using a design of experiments (DOE) methodology airflow seeding parameters were changed to evaluate how these modifications affected the degree of surface contamination. The solution composition, comprised of ethanol and water, was determined to be the most significant factor in particle adhesion. Utilizing image analysis software, data were collected from the contaminated surfaces and incorporated to generate predictive particle contamination models. A relationship was identified between the solvent evaporation rate, and the morphology and magnitude of PSL contaminants on the test surfaces. This analysis can be extended to other solvent mixtures to provide insight into simultaneously improving wind tunnel testing capabilities while diminishing facility contamination.

Wohl, Christopher J.↗

NASA NTRS2019

Cluster Analysis of Spectroscopic Line Profiles and EUV Emission in RMHD Simulations and Observations of the Solar Atmosphere

Spatially-resolved observations from the IRIS, SDO/AIA, and other space mission and ground-based telescopes, coupled with realistic 3D RMHD simulations, are a powerful tool for analysis of processes in the solar atmosphere. To better understand the dynamical and thermodynamic properties in the simulation data and their connection to observations, it is essential to determine similarities in the behaviors of the synthesized and observed emission. However, the complexity of observational data and physical processes makes comparison of observations and modeling results difficult. In this work, we show the initial results of application of K-Means clustering (unsupervised machine learning) algorithm to two different problems: 1) recognition of the typical spectroscopic line profiles observed by IRIS during solar flares and their typical dynamic behavior; 2) recognition of shocks and heating events in synthetic AIA emission data obtained from StellarBox quiet-Sun simulations. The average silhouette width technique for the KMeans algorithm is utilized in different ways to obtain optimal numbers of clusters. We discuss application of the emission clustering to visualizations of the computational volume, understanding its evolutionary trends and behavior patterns, and inversion (reconstruction) of physical properties of the solar atmosphere from synthesizes emission data.

Sadykov, Viacheslav↗

NASA NTRS2009

Satellite Proving Ground for the GOES-R Geostationary Lightning Mapper (GLM)

The key mission of the Satellite Proving Ground is to demonstrate new satellite observing data, products and capabilities in the operational environment to be ready on Day 1 to use the GOES-R suite of measurements. Algorithms, tools, and techniques must be tested, validated, and assessed by end users for their utility before they are finalized and incorporated into forecast operations. The GOES-R Proving Ground for the Geostationary Lightning Mapper (GLM) focuses on evaluating how the infusion of the new technology, algorithms, decision aids, or tailored products integrate with other available tools (weather radar and ground strike networks; nowcasting systems, mesoscale analysis, and numerical weather prediction models) in the hands of the forecaster responsible for issuing forecasts and warning products. Additionally, the testing concept fosters operation and development staff interactions which will improve training materials and support documentation development. Real-time proxy total lightning data from regional VHF lightning mapping arrays (LMA) in Northern Alabama, Central Oklahoma, Cape Canaveral Florida, and the Washington, DC Greater Metropolitan Area are the cornerstone for the GLM Proving Ground. The proxy data will simulate the 8 km Event, Group and Flash data that will be generated by GLM. Tailored products such as total flash density at 1-2 minute intervals will be provided for display in AWIPS-2 to select NWS forecast offices and national centers such as the Storm Prediction Center. Additional temporal / spatial combinations are being investigated in coordination with operational needs and case-study proxy data and prototype visualizations may also be generated from the NASA heritage Lightning Imaging Sensor and Optical Transient Detector data. End users will provide feedback on the utility of products in their operational environment, identify use cases and spatial/temporal scales of interest, and provide feedback to the developers for adjusted or new products.

Goodman, Steven J.↗

NASA NTRS2011

Radar and Lidar Radar DEM

Using radar and lidar data, the aim is to improve 3D rendering of terrain, including digital elevation models (DEM) and estimates of vegetation height and biomass in a variety of forest types and terrains. The 3D mapping of vegetation structure and the analysis are useful to determine the role of forest in climate change (carbon cycle), in providing habitat and as a provider of socio-economic services. This in turn will lead to potential for development of more effective land-use management. The first part of the project was to characterize the Shuttle Radar Topography Mission DEM error with respect to ICESat/GLAS point estimates of elevation. We investigated potential trends with latitude, canopy height, signal to noise ratio (SNR), number of LiDAR waveform peaks, and maximum peak width. Scatter plots were produced for each variable and were fitted with 1st and 2nd degree polynomials. Higher order trends were visually inspected through filtering with a mean and median filter. We also assessed trends in the DEM error variance. Finally, a map showing how DEM error was geographically distributed globally was created.

remote sensing↗