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NASA NTRS2005

Force Limited Vibration Testing

This slide presentation reviews the concept and applications of Force Limited Vibration Testing. The goal of vibration testing of aerospace hardware is to identify problems that would result in flight failures. The commonly used aerospace vibration tests uses artificially high shaker forces and responses at the resonance frequencies of the test item. It has become common to limit the acceleration responses in the test to those predicted for the flight. This requires an analysis of the acceleration response, and requires placing accelerometers on the test item. With the advent of piezoelectric gages it has become possible to improve vibration testing. The basic equations have are reviewed. Force limits are analogous and complementary to the acceleration specifications used in conventional vibration testing. Just as the acceleration specification is the frequency spectrum envelope of the in-flight acceleration at the interface between the test item and flight mounting structure, the force limit is the envelope of the in-flight force at the interface . In force limited vibration tests, both the acceleration and force specifications are needed, and the force specification is generally based on and proportional to the acceleration specification. Therefore, force limiting does not compensate for errors in the development of the acceleration specification, e.g., too much conservatism or the lack thereof. These errors will carry over into the force specification. Since in-flight vibratory force data are scarce, force limits are often derived from coupled system analyses and impedance information obtained from measurements or finite element models (FEM). Fortunately, data on the interface forces between systems and components are now available from system acoustic and vibration tests of development test models and from a few flight experiments. Semi-empirical methods of predicting force limits are currently being developed on the basis of the limited flight and system test data. A simple two degree of freedom system is shown and the governing equations for basic force limiting results for this system are reviewed. The design and results of the shuttle vibration forces (SVF) experiments are reviewed. The Advanced Composition Explorer (ACE) also was used to validate force limiting. Test instrumentation and supporting equipment are reviewed including piezo-electric force transducers, signal processing and conditioning systems, test fixtures, and vibration controller systems. Several examples of force limited vibration testing are presented with some results.

random vibration testing↗
NASA NTRSDate not supplied

Firesense: Supporting Operational Partners Through Co-Development

Fire is a natural disturbance and fundamental to many ecosystems, but, across the United States and many areas globally, fires have become more common, larger and more likely to occur at the same time. This has the potential to challenge resource allocation and response and requires coordinated integration of technology and tools at the spatial and temporal scales required by practitioners who make wildland fire management decisions. In the US wildfire is currently managed across state, federal, and tribal agencies who leverage various datasets to guide management decisions. FireSense, a NASA Science Mission Directorate project, aims to develop and deliver trailblazing technology and tools for use before, during, and after wildland fires by working together with land managers and practitioners. FireSense is focused on turning information into solutions by expanding partnerships and collaborations with operational agencies to inform and deliver data and technology to support decisions for wildland fire management. Through implementation across the US and with campaign activities in a variety of landscapes, coordinated research and development work will build upon adaptive and use-inspired approaches that can be put into operation. By leveraging and complementing partner activities through coordinated investment, we support a more comprehensive and cohesive response that can advance fire science to better understand fire behavior and effects through integrated measurement, monitoring and modeling. We present updates from in-progress technology development and field campaigns within the US which include sampling and application across scales with information from multiple sensors collocated with field measurements and highlighting the importance of cross-scale observations during the fire lifecycle. Coordinated investment in new science and technology for the complete fire lifecycle supports a comprehensive and cohesive fire response that help to overcome barriers and anticipate and manage the new reality of extreme fires in a warming world.

Jacquelyn Shuman↗
NASA NTRS2004

Virtual Sensors: Using Data Mining to Efficiently Estimate Spectra

Detecting clouds within a satellite image is essential for retrieving surface geophysical parameters, such as albedo and temperature, from optical and thermal imagery because the retrieval methods tend to be valid for clear skies only. Thus, routine satellite data processing requires reliable automated cloud detection algorithms that are applicable to many surface types. Unfortunately, cloud detection over snow and ice is difficult due to the lack of spectral contrast between clouds and snow. Snow and clouds are both highly reflective in the visible wavelen,ats and often show little contrast in the thermal Infrared. However, at 1.6 microns, the spectral signatures of snow and clouds differ enough to allow improved snow/ice/cloud discrimination. The recent Terra and Aqua Moderate Resolution Imaging Spectro-Radiometer (MODIS) sensors have a channel (channel 6) at 1.6 microns. Presently the most comprehensive, long-term information on surface albedo and temperature over snow- and ice-covered surfaces comes from the Advanced Very High Resolution Radiometer ( AVHRR) sensor that has been providing imagery since July 1981. The earlier AVHRR sensors (e.g. AVHRR/2) did not however have a channel designed for discriminating clouds from snow, such as the 1.6 micron channel available on the more recent AVHRR/3 or the MODIS sensors. In the absence of the 1.6 micron channel, the AVHRR Polar Pathfinder (APP) product performs cloud detection using a combination of time-series analysis and multispectral threshold tests based on the satellite's measuring channels to produce a cloud mask. The method has been found to work reasonably well over sea ice, but not so well over the ice sheets. Thus, improving the cloud mask in the APP dataset would be extremely helpful toward increasing the accuracy of the albedo and temperature retrievals, as well as extending the time-series of albedo and temperature retrievals from the more recent sensors to the historical ones. In this work, we use data mining methods to construct a model of MODIS channel 6 as a function of other channels that are common to both MODIS and AVHRR. The idea is to use the model to generate the equivalent of MODIS channel 6 for AVHRR as a function of the AVHRR equivalents to MODIS channels. We call this a Virtual Sensor because it predicts unmeasured spectra. The goal is to use this virtual channel 6. to yield a cloud mask superior to what is currently used in APP . Our results show that several data mining methods such as multilayer perceptrons (MLPs), ensemble methods (e.g., bagging), and kernel methods (e.g., support vector machines) generate channel 6 for unseen MODIS images with high accuracy. Because the true channel 6 is not available for AVHRR images, we qualitatively assess the virtual channel 6 for several AVHRR images.

Srivastava, Ashok↗
NASA NTRS2005

Near-infrared (0.67-4.7 micron) Optical Constants Estimated for Montmorillonite

Various models of the reflectance from particulate surfaces are used for interpretation of remote sensing data of solar system objects. These models rely upon the real (n) and imaginary (k) refractive indices of the materials. Such values are limited for commonly encountered silicates at visual and near-infrared wavelengths (lambda, 0.4-5 micron). Availability of optical constants for candidate materials allows more thorough modeling of the observations obtained by Earth-based telescopes and spacecraft. Two approaches for determining the absorption coefficient (alpha=2 pi k/lambda) from reflectance measurements of particulates have been described; one relies upon Kubelka-Munk theory and the other Hapke theory. Both have been applied to estimate alpha and k for various materials. Neither enables determination of the wavelength dependence of n, n=f(lambda). Thus, a mechanism providing this ability is desirable. Using Hapke-theory to estimate k from reflectance measurements requires two additional quantities be known or assumed: 1) n=f(lambda) and 2) d, the sample particle diameter. Typically n is assumed constant (c) or modestly varying with lambda; referred to here as n(sub 0). Assuming n(sub 0), at each lambda an estimate of k is used to calculate the reflectance and is iteratively adjusted until the difference between the model and measured reflectance is minimized. The estimated k's (k(sub l)) are the final results, and this concludes the typical analysis. Additional information is included in the original extended abstract.

Roush, T. L.↗
NASA NTRS2018

Predicting Crew Time Allocations for Lunar Orbital Missions Based on Historical ISS Operational Activities

As the National Aeronautics and Space Administration continues to define candidate architectures for the planned lunar “Gateway”, it will be necessary to have a detailed understanding of how the crew will inhabit, operate, and maintain the spacecraft. The nature of the Gateway vehicle systems configuration and operations will have a direct impact on the scope of work activities required of the crew. Crew work schedules are sensitive to variations in spacecraft architecture, visiting vehicle activities, and logistics operations – particularly within short duration missions as initially planned for the lunar Gateway. These system and operational configurations must be taken into account when planning for crew time availability to conduct science activities on Gateway missions. This paper presents a methodology that is used to predict crew time distributions for lunar Gateway missions, as applied in NASA’s Exploration Crew Time Model (ECTM). The process utilized for evaluating crew time distributions is based on the categorization of all crew activities into a standardized ontology. Historical ISS daily crew timeline data from July 20, 2011 (post STS retirement) to present day was captured via the Operational Planning Timeline Integration System (OPTimIS) database and characterized according to the standardized ontology. This process enabled correlation and statistical analysis of the ISS data according to common mission parameters such as crew size, ECLSS system design, vehicle traffic operations, and logistics delivery operations. The results of the statistical analysis are a set of crew time distributions for each activity category. These distributions are then utilized within the ECTM to examine crew time allocations based on mission parameter inputs, which serve to characterize the Gateway mission configurations. Results for predicted crew time allocations for representative short duration Gateway missions are presented. These results can be used to evaluate crew schedule availability for science and utilization activities. Variations in expected mission architectures and mission operations are accounted for to correct crew time predictions. The analysis is being leveraged to plan utilization capability objectives that are achievable on the Gateway missions, as well as inform the viability of various mission architecture options.

Stromgren, Chel↗
NASA NTRSDate not supplied

Modeling Deformable Linear Objects for Autonomous Robotic Outfitting of Lunar Surface Systems

This paper presents structural models of deformable linear objects (DLOs). DLOs are a subclass of deformable objects that encompasses common outfitting elements such as cables and ropes. Models are validated through hardware experiments, and integration in a robotic autonomy architecture for space environments is discussed. A persistent human presence on the lunar surface is one of the next major milestones in space exploration. This requires the development of robust extraplanetary construction technologies including structures and materials modeling and robotic systems. Previous robotic construction technology development has primarily focused on structural assembly, with significantly less focus on robotically performed outfitting tasks to instantiate subsystems providing power, data, life support, etc. These tasks involve manipulation of highly flexible elements, which are difficult to model, such as cable harnesses, ropes, and hoses. Robotic manipulation of DLOs, especially cable harnesses, is an active area of research as cable harnesses are essential for providing power and data to space assets. DLO models that can be used for robot manipulator trajectory generation are necessary for autonomous operation of lunar infrastructure. There are many proposed methods for modeling DLOs, and they primarily fall into three types: 1) discrete model-based, 2) continuum model-based, and 3) Neural Network-based. These types each have pros and cons, and the tradeoff between model accuracy and computational speed informs which type should be used. An understanding of this trade-off is imperative for real-time control of autonomous systems. High computational requirements reduce the speed of the model, making real-time control difficult, while accuracy is critical to preventing collisions. Discrete models, such as a mass-spring multibody representation, require relatively few calculations, and accuracy is directly tied to the step size of the discretization. Continuum models, such as a B-spline representation or a Cosserat rod model (a mix of continuous and discrete), are more informed of the structural properties of the cable and are much more accurate than a rigid body mass-spring model, but at significant computational cost. A Neural Network approach can provide an online solution with very few computational steps, but properly generating training data can be difficult and validation for an in-space application is not trivial. This paper explores the trade-off between different modeling approaches and compares accuracy and computational speed/complexity of the three types mentioned above. Model accuracy is evaluated using a cable in a static configuration. True cable shape is obtained using a depth camera for RGB images and point-cloud segmentation. The purpose of this experiment is to evaluate the trade-offs of different approaches to the DLO modeling problem. Understanding the tradeoffs between different cable modeling techniques paves the way for developing robotic control and planning architectures necessary for real-time manipulation of DLOs for lunar infrastructure outfitting. Real-time control is required for robotic systems to be able to actively manipulate a cable in a harsh environment where model and sensor errors compound, and environmental conditions can cause significant disturbances. Cable routing must be performed in areas with high density of objects/obstacles: through truss structures, near solar panels or mirror arrays, next to bundles of electrical equipment. Understanding the best way to plan and manipulate a cable without disrupting the environment or damaging the cable is imperative to robotic outfitting operations on the lunar surface.

Amy M Quartaro↗
NASA NTRSDate not supplied

Identifying Human Errors and Error Mechanisms From Accident Reports Using Large Language Models

Emerging operational concepts for aviation hinge on novel paradigms for human machine interaction. Critical to their safe operation is early consideration of human error into the design process. Existing methods for consideration of human error require significant expert input, which is challenging both in early design and in novel systems for which there is little existing safety expertise. In this research, we propose a methodology for identifying human error, error producing factors, and mechanisms in early design from historical incident reports. Additionally, we hypothesize that cross-domain sharing of lessons learned can aid with early design human considerations in circumstances where data is not relevant or incomplete. This is addressed by identifying causes of human error in aviation and railway domains through applying state-of-the art natural language processing techniques to historical incident reports. Using this method, it is possible to extract extensive reports on human error from past incidents. Using the proposed approach, we identify nine human errors from railway reports and fourteen from aviation reports, with three errors common to both domains. There is at least one error producing conditions for each human error while a majority of the errors have more than one error mechanism. We also found that a majority of the human errors, error producing factors, and error mechanisms (even if they are not common between the domains) can be used to inform safe operations across domains as long as the errors are not domain specific and are interpreted and contextualized using engineering judgement.

Human Errors↗
NASA NTRS2008

Evidence in Magnetic Clouds for Systematic Open Flux Transport on the Sun

Most magnetic clouds encountered by spacecraft at 1 AU display a mix of unidirectional suprathermal electrons signaling open field lines and counterstreaming electrons signaling loops connected to the Sun at both ends. Assuming the open fields were originally loops that underwent interchange reconnection with open fields at the Sun, we determine the sense of connectedness of the open fields found in 72 of 97 magnetic clouds identified by the Wind spacecraft in order to obtain information on the location and sense of the reconnection and resulting flux transport at the Sun. The true polarity of the open fields in each magnetic cloud was determined from the direction of the suprathermal electron flow relative to the magnetic field direction. Results indicate that the polarity of all open fields within a given magnetic cloud is the same 89% of the time, implying that interchange reconnection at the Sun most often occurs in only one leg of a flux rope loop, thus transporting open flux in a single direction, from a coronal hole near that leg to the foot point of the opposite leg. This pattern is consistent with the view that interchange reconnection in coronal mass ejections systematically transports an amount of open flux sufficient to reverse the polarity of the heliospheric field through the course of the solar cycle. Using the same electron data, we also find that the fields encountered in magnetic clouds are only a third as likely to be locally inverted as not. While one might expect inversions to be equally as common as not in flux rope coils, consideration of the geometry of spacecraft trajectories relative to the modeled magnetic cloud axes leads us to conclude that the result is reasonable.

Crooker, N. U.↗
NASA NTRS1999

Measuring Greenland Ice Mass Variation With Gravity Recovery and the Climate Experiment Gravity and GPS

The response of the Greenland ice sheet to climate change could significantly alter sea level. The ice sheet was much thicker at the last glacial maximum. To gain insight into the global change process and the future trend, it is important to evaluate the ice mass variation as a function of time and space. The Gravity Recovery and Climate Experiment (GRACE) mission to fly in 2001 for 5 years will measure gravity changes associated with the current ice variation and the solid earth's response to past variations. Our objective is to assess the separability of different change sources, accuracy and resolution in the mass variation determination by the new gravity data and possible Global Positioning System (GPS) bedrock uplift measurements. We use a reference parameter state that follows a dynamic ice model for current mass variation and a variant of the Tushingham and Peltier ICE-3G deglaciation model for historical deglaciation. The current linear trend is also assumed to have started 5 kyr ago. The Earth model is fixed as preliminary reference Earth model (PREM) with four viscoelastic layers. A discrete Bayesian inverse algorithm is developed employing an isotropic Gaussian a priori covariance function over the ice sheet and time. We use data noise predicted by the University of Texas and JPL for major GRACE error sources. A 2 mm/yr uplift uncertainty is assumed for GPS occupation time of 5 years. We then carry out covariance analysis and inverse simulation using GRACE geoid coefficients up to degree 180 in conjunction with a number of GPS uplift rates. Present-day ice mass variation and historical deglaciation are solved simultaneously over 146 grids of roughly 110 km x 110 km and with 6 time increments of 3 kyr each, along with a common starting epoch of the current trend. For present-day ice thickness change, the covariance analysis using GRACE geoid data alone results in a root mean square (RMS) posterior root variance of 2.6 cm/yr, with fairly large a priori uncertainties in the parameters and a Gaussian correlation length of 350 km. Simulated inverse can successfully recover most features in the reference present-day change. The RMS difference between them over the grids is 2.8 cm/yr. The RMS difference becomes 1.1 cm/yr when both are averaged with a half Gaussian wavelength of 150 km. With a fixed Earth model, GRACE alone can separate the geoid signals due to past and current load fairly well. Shown are the reference geoid signatures of direct and elastic effects of the current trend, the viscoelastic effect of the same trend starting from 5 kyr ago, the Post Glacial Rebound (PGR), and the predicted GRACE geoid error. The difference between the reference and inverse modeled total viscoelastic signatures is also shown. Although past and current ice mass variations are allowed the same spatial scale, their geoid signals have different spatial patterns. GPS data can contribute to the ice mass determination as well. Additional information is contained in the original.

Wu, Xiao-Ping↗
NASA NTRSDate not supplied

I’ve Looked at Clouds from Both Sides, Now: Viewpoints from Surface and Spaceborne Lidar Systems

Clouds play a critical role in the Earth’s climate system because they are inextricably linked to the hydrological cycle and radiation budget. Information about cloud height, thickness, occurrence, and amount are critical inputs for a host of numerical applications involving climate research. Therefore, it is important to have highly accurate and quantitative data records of cloud properties that span several years and geographic regions. Verification of even the most basic modeling processes demands long term and continuous observations of global cloud occurrence, if there is to be any confidence in their fidelity. For a number of reasons, however, it is impossible to gauge the complexities of clouds from a single source. Fundamentally, an array of remote sensing methods is needed in order to provide a complete picture. Within the Micropulse Lidar Network (MPLNET), we have developed a new algorithm to improve the quality of our cloud products. The largest impact of the changes to the cloud detection algorithm is evident with high clouds (those with cloud base > 5 km). Furthermore, polarized measurements enable us to determine the cloud thermodynamic phase. Naturally, the next step is to investigate how these improvements compare with observations from spaceborne lidars (e.g. CALIOP) which have a better (unobstructed) view of high clouds. Recent advances in remote sensing have revealed that cirrus clouds are the most common cloud genus observed in the atmosphere. Furthermore, cirrus skew highly towards relatively low cloud optical depths, as observed from both surface and spaceborne viewpoints. The radiative impacts of these findings are quite significant, considering the cumulative effect cirrus exhibit when compared to low clouds.

Jasper R Lewis↗
NASA NTRSDate not supplied

SWIPE: Spectral Water Inversion Processor and Emulator

Degradation of Earth’s inland water resources due to anthropogenic perturbations and climate anomalies at both local and global scales continues to place human health at substantial risk. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will be discussing the progress made developing SWIPE: Spectral Water Inversion Processor and Emulator. SWIPE is a platform for advanced modeling of coastal and inland aquatic habitats. The goal is create a comprehensive and cohesive system to leverage recent advancements in computation and machine learning to develop a synthetic training ground for sensitivity studies and algorithm development. The four principal facets of SWIPE include: 1. Advanced two-layer coated sphere bio-optical modeling and GPU radiative transfer modeling, 2. Big Data involving massive synthetic spectral libraries of optical properties of various global aquatic particles, surface reflectance, and top-of-atmosphere reflectance, all at hyperspectral resolution leveraging high-end computing systems at NASA Ames Research Center, 3. Deep Learning for algorithm development for water quality inversion of concentrations of common biogeophysical variables as well as optics, full uncertainty characterization by water type, and forward emulation, and lastly, 4. Image Processing for application of developed retrieval algorithms for both hyperspectral and multispectral sensors with experimental corrections for global adjacency, noise, sunglint, and benthic reflectance. This presentation will demonstrate the Equivalent Algal Populations (EAP) two-layer coated sphere scattering model which has been used develop spectral libraries of hyperspectral inherent optical properties of roughly 80 species of phytoplankton, covering 15 different classes and nine taxonomic functional types. The EAP model was also used to derive spectral properties of 10 different non-algal particle functional types. Examples of how the SMART-G (Speed-up Monte-carlo Advanced Radiative Transfer using GPU) radiative transfer code is used to model optically complex aquatic signals will be presented and discussed in the context of creating a massive synthetic database which can leverage the full power of next generation machine learning techniques and high end computing for water quality inversion. We will discuss our active investigation in things like appropriate model architectures, dimensionality reduction techniques such as PCA and autoencoders, uncertainty quantification and abstaining, and which variables actually benefit most from hyperspectral information versus multispectral resolution. We are also curious about questions relating to cost/benefit analysis in terms of computation resources, neural network complexity, and data volumes. Answers to these questions will hopefully elaborate on cost efficiency for potential future sensor design considerations.

SWIPE↗
NASA NTRS2015

Uncertainty Quantification of Turbulence Model Closure Coefficients for Transonic Wall-Bounded Flows

The goal of this work was to quantify the uncertainty and sensitivity of commonly used turbulence models in Reynolds-Averaged Navier-Stokes codes due to uncertainty in the values of closure coefficients for transonic, wall-bounded flows and to rank the contribution of each coefficient to uncertainty in various output flow quantities of interest. Specifically, uncertainty quantification of turbulence model closure coefficients was performed for transonic flow over an axisymmetric bump at zero degrees angle of attack and the RAE 2822 transonic airfoil at a lift coefficient of 0.744. Three turbulence models were considered: the Spalart-Allmaras Model, Wilcox (2006) k-w Model, and the Menter Shear-Stress Trans- port Model. The FUN3D code developed by NASA Langley Research Center was used as the flow solver. The uncertainty quantification analysis employed stochastic expansions based on non-intrusive polynomial chaos as an efficient means of uncertainty propagation. Several integrated and point-quantities are considered as uncertain outputs for both CFD problems. All closure coefficients were treated as epistemic uncertain variables represented with intervals. Sobol indices were used to rank the relative contributions of each closure coefficient to the total uncertainty in the output quantities of interest. This study identified a number of closure coefficients for each turbulence model for which more information will reduce the amount of uncertainty in the output significantly for transonic, wall-bounded flows.

Schaefer, John↗
NASA NTRS2010

Aeroelasticity Benchmark Assessment: Subsonic Fixed Wing Program

The fundamental technical challenge in computational aeroelasticity is the accurate prediction of unsteady aerodynamic phenomena and the effect on the aeroelastic response of a vehicle. Currently, a benchmarking standard for use in validating the accuracy of computational aeroelasticity codes does not exist. Many aeroelastic data sets have been obtained in wind-tunnel and flight testing throughout the world; however, none have been globally presented or accepted as an ideal data set. There are numerous reasons for this. One reason is that often, such aeroelastic data sets focus on the aeroelastic phenomena alone (flutter, for example) and do not contain associated information such as unsteady pressures and time-correlated structural dynamic deflections. Other available data sets focus solely on the unsteady pressures and do not address the aeroelastic phenomena. Other discrepancies can include omission of relevant data, such as flutter frequency and / or the acquisition of only qualitative deflection data. In addition to these content deficiencies, all of the available data sets present both experimental and computational technical challenges. Experimental issues include facility influences, nonlinearities beyond those being modeled, and data processing. From the computational perspective, technical challenges include modeling geometric complexities, coupling between the flow and the structure, grid issues, and boundary conditions. The Aeroelasticity Benchmark Assessment task seeks to examine the existing potential experimental data sets and ultimately choose the one that is viewed as the most suitable for computational benchmarking. An initial computational evaluation of that configuration will then be performed using the Langley-developed computational fluid dynamics (CFD) software FUN3D1 as part of its code validation process. In addition to the benchmarking activity, this task also includes an examination of future research directions. Researchers within the Aeroelasticity Branch will examine other experimental efforts within the Subsonic Fixed Wing (SFW) program (such as testing of the NASA Common Research Model (CRM)) and other NASA programs and assess aeroelasticity issues and research topics.

Florance, Jennifer P.↗
NASA NTRS2016

A Possible Explanation for the Z-R Parameter Inconsistency when Comparing Stratiform and Convective Rainfall

The well-known Z-R power law Z A R(sup b) uses two parameters, A and b, in order to relate rainfall rate R to measured weather radar reflectivity Z. A common method used by researchers is to compute Z and R from disdrometer data and then extract the A-b parameter pair from a log-linear line fit to a scatter plot of Z-R pairs. Even though it may seem far more truthful to extract the parameter pair from a fit of radar Z(sub R) versus gauge rainfall rate R(sub G), the extreme difference in spatial and temporal sampling volumes between radar and rain gauge creates a slew of problems that can generally only be solved by using rain gauge arrays and long sampling averages. Disdrometer derived A-b parameters are easily obtained and can provide information for the study of stratiform versus convective rainfall. However, an inconsistency appears when comparing averaged A-b pairs from various researchers. Values of b range from 1.26 to 1.51 for both stratiform and convective events. Paradoxically the values of A fall into three groups: 150 to 200 for convective; 200 to 400 for stratiform; and 400 to 500 again for convective. This apparent inconsistency can be explained by computing the A-b pair using the gamma DSD coupled with a modified drop terminal velocity model, v(D) alpha D(sup beta) - w, where w is a somewhat artificial constant vertical velocity of the air above the disdrometer. This model predicts three regions of A, corresponding to w less than 0, w = 0, and w greater than 0, which approximately matches observed data.

gamma DSD↗
NASA NTRS2006

Promoting Lifelong Ocean Education: Shaping Tomorrow's Earth Stewards and the Science and Technology Workforce

The coming ocean observing systems provide an unprecedented opportunity to change both the public perception of our oceans, and to inspire, captivate and motivate our children, our young adults and even our fellow adults to pursue careers allied with the oceans and to become stewards of our Planet's last unexplored environment. Education plans for the operational component, the Integrated Ocean Observing System (IOOS), and for the research component, Ocean Research Interactive Observatory Networks (ORION), are designed to take advantage of this opportunity. In both cases, community recommendations were developed within the context of the following assumptions: 1. Utilize research on how people learn, especially the four-pronged model of simultaneous learner-centered, knowledge-center, assessment-centered and community-centered learning 2. Strive for maximum impact on national needs in science and technology learning 3. Build on the best of what is already in place 4. Pay special attention to quality, sustainability, and scalability of efforts 5. Use partnerships across federal, state and local government, academia, and industry. Community recommendations for 100s and ORION education have much in common and offer the opportunity to create a coherent education effort allied with ocean observing systems. Both efforts focus on developing the science and technology workforce of the future, and the science and technology literacy of the public within the context of the Earth system and the role of the oceans and Great Lakes in that system. Both also recognize that an organized education infrastructure that supports sustainability and scalability of education efforts is required if ocean observing education efforts are to achieve a small but measurable improvement in either of these areas. Efforts have begun to develop the education infrastructure by beginning to form a community of educators from existing ocean and aquatic education networks and by exploring needs and issues associated with using ocean observing information assets in education. Likewise efforts are underway to address workforce issues by a systematic analysis of current and future workforce and educational needs. These activities will be described as will upcoming opportunities for the community to participate in these efforts.

Meeson, Blanche↗
NASA NTRS2011

Land and Atmosphere Near-Real-Time Capability for Earth Observing System

The past decade has seen a rapid increase in availability and usage of near-real-time data from satellite sensors. The EOSDIS (Earth Observing System Data and Information System) was not originally designed to provide data with sufficiently low latency to satisfy the requirements for near-real-time users. The EOS (Earth Observing System) instruments aboard the Terra, Aqua and Aura satellites make global measurements daily, which are processed into higher-level 'standard' products within 8-40 hours of observation and then made available to users, primarily earth science researchers. However, applications users, operational agencies, and even researchers desire EOS products in near-real-time to support research and applications, including numerical weather and climate prediction and forecasting, monitoring of natural hazards, ecological/invasive species, agriculture, air quality, disaster relief and homeland security. These users often need data much sooner than routine science processing allows, usually within 3 hours, and are willing to trade science product quality for timely access. While Direct Broadcast provides more timely access to data, it does not provide global coverage. In 2002, a joint initiative between NASA (National Aeronautics and Space Administration), NOAA (National Oceanic and Atmospheric Administration), and the DOD (Department of Defense) was undertaken to provide data from EOS instruments in near-real-time. The NRTPE (Near Real Time Processing Effort) provided products within 3 hours of observation on a best-effort basis. As the popularity of these near-real-time products and applications grew, multiple near-real-time systems began to spring up such as the Rapid Response System. In recognizing the dependence of customers on this data and the need for highly reliable and timely data access, NASA's Earth Science Division sponsored the Earth Science Data and Information System Project (ESDIS)-led development of a new near-real-time system called LANCE (Land, Atmosphere Near-Real-Time Capability for EOS) in 2009. LANCE consists of special processing elements, co-located with selected EOSDIS data centers and processing facilities. A primary goal of LANCE is to bring multiple near-real-time systems under one umbrella, offering commonality in data access, quality control, and latency. LANCE now processes and distributes data from the Moderate Resolution Imaging Spectroradiometer (MODIS), Atmospheric Infrared Sounder (AIRS), Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E), Microwave Limb Sounder (MLS) and Ozone Monitoring Instrument (OMI) instruments within 3 hours of satellite observation. The Rapid Response System and the Fire Information for Resource Management System (FIRMS) capabilities will be incorporated into LANCE in 2011. LANCE maintains a central website to facilitate easy access to data and user services. LANCE products are extensively tested and compared with science products before being made available to users. Each element also plans to implement redundant network, power and server infrastructure to ensure high availability of data and services. Through the user registration system, users are informed of any data outages and when new products or services will be available for access. Building on a significant investment by NASA in developing science algorithms and products, LANCE creates products that have a demonstrated utility for applications requiring near-real-time data. From lower level data products such as calibrated geolocated radiances to higher-level products such as sea ice extent, snow cover, and cloud cover, users have integrated LANCE data into forecast models and decision support systems. The table above shows the current near-real-time product categories by instrument. The ESDIS Project continues to improve the LANCE system and use the experience gained through practice to seek adjustments to improve the quality and performance of the system. For example, anGC-compliant Web Map Service (WMS) will be added shortly that will allow users to download geo-referenced MODIS images for arbitrary bounding boxes. Further, an OGC-compliant Web Coverage Service (WCS) will be added later this year that will expedite user access to arbitrary data subsets or re-formatted products. AIRS images are now served through WMS and available in multiple formats (PNG, GeoTIFF, KMZ). NASA has established a LANCE User Working Group to steer the development of the system and create a forum for sharing ideas and experiences that are expected to further improve the LANCE capabilities. The LANCE system has proved a success by satisfying the growing needs of the applications and operational communities for land and atmosphere data in near-real-time. NASA's Earth Sciences Division was able to leverage existing science research capabilities to provide the near-real-time community with products and imagery that support monitoring of disasters in a timely manner.

Murphy, Kevin J.↗
NASA NTRS2018

Concept of Operations for Management by Trajectory

This document describes Management by Trajectory (MBT), a concept for future air traffic management (ATM) in which every flight operates in accordance with a four-dimensional trajectory (4DT) that is negotiated between the airspace user and the Federal Aviation Administration (FAA) to respect the airspace user's goals while complying with National Airspace System (NAS) constraints. In the present-day NAS, the ATM system attempts to predict the trajectory for each flight based on the approved flight plan and scheduled or controlled departure time. However, once the aircraft starts to move, controllers tactically manage the aircraft to implement traffic management restrictions, separate otherwise conflicting aircraft, and address arising NAS constraints. Tactical controller actions are not directly communicated to the automation systems or other stakeholders. Furthermore, the initial trajectory prediction does not anticipate these disruptions or how they will impact the flight. Consequently, and compounded by gaps in required data and models, trajectory predictions are less accurate than possible, which affects Traffic Flow Management (TFM) performance. A cornerstone of the MBT concept is that all air vehicles have, at all times, an assigned 4DT from their current state to their destination. These assigned trajectories consist of trajectory constraints and descriptions. Pilots and air traffic controllers, with the aid of automation, operate the aircraft to comply with the assigned trajectory, unless first negotiating a revision. Equipped aircraft have substantial responsibility for complying with the assigned trajectory without controller intervention. To maximize the operational flexibility available to the airspace user, the assigned trajectory only imposes trajectory constraints as required to achieve the ATM goals of NAS constraint compliance and aircraft separation. Trajectory descriptions are added to the assigned trajectory to ensure sufficient predictability. To further improve trajectory prediction accuracy, airspace users supplement the assigned trajectory by broadcasting intent information and updating it as necessary. Air vehicle intent is a more detailed description of the airspace user's plan for how the flight will fly the assigned trajectory. Air vehicle intent can change freely, without negotiation, as long as it remains in compliance with the assigned trajectory. Aircraft assigned trajectories, air vehicle intent, and predicted trajectories are shared, creating a common view among stakeholders. A NAS Constraint Service gathers and publishes information about all known NAS constraints, enabling airspace users to be informed participants in trajectory negotiation. Trajectory constraints in the assigned trajectory are mapped to NAS constraints to facilitate identifying which aircraft are affected when NAS constraints change. To support efficient trajectory negotiation, all aircraft provide current information about air vehicle capabilities. Assigned trajectories are constructed to satisfy all known NAS constraints, improving trajectory stability and predictability. Uncertainty and disruptions are handled by modifying the assigned trajectory as far in advance as possible. By proactively negotiating changes to the assigned trajectory, rather than relying on controller-selected tactical actions such as vectors to resolve traffic conflicts or implement miles-in-trail restrictions, MBT keeps aircraft on closed trajectories that are fully known to all stakeholders. Since reactive air traffic control actions cannot be predicted in advance, the downstream trajectory cannot be accurately predicted until they happen. Reliable trajectory predictions allow the system to identify needed modifications to trajectories further in advance, where they can be negotiated and communicated as amendments (i.e., additional or altered trajectory constraints) to the assigned trajectory. Decision Support Tools (DSTs) aid controllers in rapidly defining and communicating closed trajectories to the aircraft and support all stakeholders in trajectory negotiation. Anticipated MBT benefit mechanisms include more accurate trajectory predictions, improved ATM performance and robustness to off-nominal conditions, increased flexibility and operational efficiency, reduced impediments to emerging classes of airspace users accessing NAS resources, reduced environmental impacts, and enhanced safety.

ConOps↗
NASA NTRS2017

GES DISC Datalist Improves Earth Science Data Discoverability

At American Geophysical Union(AGU) 2016 Fall Meeting, Goddard Earth Sciences Data Information Services Center (GES DISC) unveiled a novel way to access data: Datalist. Currently, datalist is a collection of predefined data variables from one or more archived datasets, curated by our subject matter expert (SME). Our science support team has curated a predefined Hurricane Datalist and received very positive feedback from the user community. Datalist uses the same architecture our new website uses and have the same look and feel as other datasets on our web site. and also provides a one-stop shopping for data, metadata, citation, documentation, visualization and other available services. Since the last AGU Meeting, we have further developed a few new datalists corresponding to the Big Earth Data Initiative (BEDI) Societal Benefit Areas and A-Train data. We now have four datalists: Hurricane, Wind Energy, Greenhouse Gas and A-Train. We have also started working with our User Working Group members to create their favorite datalists and working with other DAAC to explore the possibility to include their products in our datalists that may also lead to a future of potential federated (cross-DAAC) datalists. Since our datalist prototype effort was a success, we are planning to make datalist operational. It's extremely important to have a common metadata model to support datalist, this will also be the foundation of federated datalist. We mapped our datalist metadata model to the unpublished UMM(Universal Metadata Model)-Var (Variable) (June version) and found that the UMM-var together with UMM-C (Collection) and possible UMM-S (Service) will meet our basic requirements. For example: Dataset shortname, and version are already specified in UMM-C, variable name, long name, units, dimensions are all specified in UMM-Var. UMM-Var also facilitates Science Keywords to allow tagging at variable level and Characteristics for optional variable characteristics. Measurements is useful for grouping of the variables and Set is promising to define datalist. And finally, the UMM-Service model to specify the available services for the variable will be very beneficial. In summary, UMM-Var, UMM-C and UMM-S are the basis of federated datalist and the development and deployment of datalist will contribute to the evolution of the UMM.

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