SEARCH · Search NASA

Results for “Operations Research in environmental modeling”

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

SourceResearch connectionFrom yearThrough year

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

At least 217 records · Page 12

All sources NASA NTRS DOE OSTI

NASA NTRS2016

Adaptive Management Methods to Protect the California Sacramento-San Joaquin Delta Water Resource

The California Sacramento-San Joaquin River Delta is the hub for California's water supply, conveying water from Northern to Southern California agriculture and communities while supporting important ecosystem services, agriculture, and communities in the Delta. Changes in climate, long-term drought, water quality changes, and expansion of invasive aquatic plants threatens ecosystems, impedes ecosystem restoration, and is economically, environmentally, and sociologically detrimental to the San Francisco Bay/California Delta complex. NASA Ames Research Center and the USDA-ARS partnered with the State of California and local governments to develop science-based, adaptive-management strategies for the Sacramento-San Joaquin Delta. The project combines science, operations, and economics related to integrated management scenarios for aquatic weeds to help land and waterway managers make science-informed decisions regarding management and outcomes. The team provides a comprehensive understanding of agricultural and urban land use in the Delta and the major water sheds (San Joaquin/Sacramento) supplying the Delta and interaction with drought and climate impacts on the environment, water quality, and weed growth. The team recommends conservation and modified land-use practices and aids local Delta stakeholders in developing management strategies. New remote sensing tools have been developed to enhance ability to assess conditions, inform decision support tools, and monitor management practices. Science gaps in understanding how native and invasive plants respond to altered environmental conditions are being filled and provide critical biological response parameters for Delta-SWAT simulation modeling. Operational agencies such as the California Department of Boating and Waterways provide testing and act as initial adopter of decision support tools. Methods developed by the project can become routine land and water management tools in complex river delta systems.

Agriculture↗

NASA NTRSDate not supplied

Assessing Sediment Inputs into the Shoshone River in Wyoming to Determine Areas for Protection and Restoration Practices

In 2016, a routine repair operation at the Willwood Dam released tons of built-up sediment into the Shoshone River, polluting the river and killing thousands of fish. This release greatly concerned the communities that rely on the river for farming, recreation, and tourism. In partnership with the Wyoming Department of Environmental Quality (WDEQ), Shoshone River Partners, and the United States Geological Survey (USGS) Wyoming-Montana Water Science Center, this multipart project illustrated the application of high-resolution satellite imagery and hydrological modeling techniques to identify major tributaries contributing to sediment influxes into the Shoshone River between the Buffalo Bill Dam and the Willwood Dam. The team used PlanetScope and Sentinel-2 Multi-Spectral Instrument (MSI) satellite images to assess changes in the surface reflectance of the river in response to precipitation events. To determine these storm events, the team selected dates of interest using Global Precipitation Measurement (GPM) Integrated Multi-Spectral Retrieval (IMERG) and gauge precipitation data from the Global Historical Climatology Network Daily (GHCNd). The continuation of this research further quantified sediment influx in the Shoshone River following snowfall and melt events using the Soil & Water Assessment Tool (SWAT). The precipitation analysis and sediment contribution maps helped the partner organizations prioritize their current decision making and best management practices to specific sites along the river. The results demonstrated the feasibility of using PlanetScope data for categorizing sediment in the Shoshone River.

Caroline Williams↗

NASA NTRSDate not supplied

GeoAI advances in specific landform mapping

Landform mapping (also referred to as geomorphology or geomorphometry) can be divided into two domains: general and specific (Evans 2012). Whereas general landform mapping categorizes all elements of the study area into landform classes, such as ridges, valleys, peaks, and depressions, the mapping of specific landforms requires the delineation (even if fuzzy) of individual landforms. The former is mainly driven by physical properties such as elevation, slope, and curvature. The latter, however, must consider the cognitive (human) reasoning that discriminates individual landforms in addition to these physical properties (Arundel and Sinha 2018). Both mapping forms are important. General geomorphometry is needed to understand geological and ecological processes and as boundary layer input to climate and environmental models. Specific geomorphometry supports such activities as disaster management and recovery, emergency response, transportation, and navigation. In the United States, individual landforms of interest are named in the U.S. Geological Survey (USGS) Geographic Names Information System, a point dataset captured specifically to digitize geographic names from the USGS Historical Topographic Map Collection (HTMC). Named landform extent is represented only by the name placement in the HTMC. Recent work has investigated CNN-based deep learning methods to capture these extents in machine-readable form. These studies first relied on physical properties (Arundel et al. 2020) and then included the HTMC as a band in RGB images in limited testing (Arundel et al. 2023). Results from the HTMC dataset surpassed those using just physical properties and using the HTMC alone performed best due to the hillshading and elevation (contour) data incorporated into the topographic maps. However, results fell short of an operational capacity to map all named landforms in the United States. Thus, our current work expands upon past research by focusing on the HTMC and physical information as inputs and the named landform label extents. Specifically, we propose to leverage pre-trained foundation models for segmentation and optical character recognition (OCR) models to jointly map landforms in the United States. Our approach aims to bridge the disparities among the independent information sources to facilitate informed decision-making. The modeling pipeline performs (1) segmentation using the physical information and (2) information extraction using OCR, in parallel. Then a computer vision approach merges the two branches into a labeled segmentation. References: Arundel, Samantha T., Wenwen Li, and Sizhe Wang. 2020. “GeoNat v1.0: A Dataset for Natural Feature Mapping with Artificial Intelligence and Supervised Learning.” Transactions in GIS 24 (3): 556–72. https://doi.org/10.1111/tgis.12633. Arundel, Samantha T, and Gaurav Sinha. 2018. “Validating GEOBIA Based Terrain Segmentation and Classification for Automated Delineation of Cognitively Salient Landforms BT - Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017).” In Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017), Lecture Notes in Geoinformation and Cartography, edited by Paolo Fogliaroni, Andrea Ballatore, and Eliseo Clementini, 9–14. Cham: Springer International Publishing. Arundel, Samantha T., Gaurav Sinha, Wenwen Li, David P. Martin, Kevin G. McKeehan, and Philip T. Thiem. 2023. “Historical Maps Inform Landform Cognition in Machine Learning.” Abstracts of the ICA 6 (August): 1–2. https://doi.org/10.5194/ica-abs-6-10-2023. Evans, Ian S. 2012. “Geomorphometry and Landform Mapping: What Is a Landform?” Geomorphology 137 (1): 94–106. https://doi.org/10.1016/j.geomorph.2010.09.029.

machine learning↗

NASA NTRSDate not supplied

GeoAI Advances in Specific Landform Mapping

machine learning↗

NASA NTRS2012

Towards Reliable Evaluation of Anomaly-Based Intrusion Detection Performance

This report describes the results of research into the effects of environment-induced noise on the evaluation process for anomaly detectors in the cyber security domain. This research was conducted during a 10-week summer internship program from the 19th of August, 2012 to the 23rd of August, 2012 at the Jet Propulsion Laboratory in Pasadena, California. The research performed lies within the larger context of the Los Angeles Department of Water and Power (LADWP) Smart Grid cyber security project, a Department of Energy (DoE) funded effort involving the Jet Propulsion Laboratory, California Institute of Technology and the University of Southern California/ Information Sciences Institute. The results of the present effort constitute an important contribution towards building more rigorous evaluation paradigms for anomaly-based intrusion detectors in complex cyber physical systems such as the Smart Grid. Anomaly detection is a key strategy for cyber intrusion detection and operates by identifying deviations from profiles of nominal behavior and are thus conceptually appealing for detecting "novel" attacks. Evaluating the performance of such a detector requires assessing: (a) how well it captures the model of nominal behavior, and (b) how well it detects attacks (deviations from normality). Current evaluation methods produce results that give insufficient insight into the operation of a detector, inevitably resulting in a significantly poor characterization of a detectors performance. In this work, we first describe a preliminary taxonomy of key evaluation constructs that are necessary for establishing rigor in the evaluation regime of an anomaly detector. We then focus on clarifying the impact of the operational environment on the manifestation of attacks in monitored data. We show how dynamic and evolving environments can introduce high variability into the data stream perturbing detector performance. Prior research has focused on understanding the impact of this variability in training data for anomaly detectors, but has ignored variability in the attack signal that will necessarily affect the evaluation results for such detectors. We posit that current evaluation strategies implicitly assume that attacks always manifest in a stable manner; we show that this assumption is wrong. We describe a simple experiment to demonstrate the effects of environmental noise on the manifestation of attacks in data and introduce the notion of attack manifestation stability. Finally, we argue that conclusions about detector performance will be unreliable and incomplete if the stability of attack manifestation is not accounted for in the evaluation strategy.

cyber defense↗

NASA NTRS2020

InSight Entry, Descent and Landing Pre-Flight Performance Predictions

On November 26, 2018, the Interior Exploration using Seismic Investigations, Geodesy and Heat Transport (InSight) lander successfully touched down on the surface of Mars. Over its seven-plus year development, NASA Langley Research Center’s (LaRC) Program to Optimize Simulated Trajectories II (POST2) was used to assess the mission’s Entry, Descent and Landing (EDL) vehicle system performance against related requirements across the full range of possible environmental and spacecraft conditions. Much of the simulation code was derived from the Phoenix mission, for which this vehicle is very similar. The InSight six degree-of-freedom simulation included models for Mars atmosphere, gravity and digital elevation maps of the landing location. Additionally, vehicle specific aerodynamic, parachute, engine, navigation sensor, flight software and landing radar models were also included. A set of dispersions for each model, as well as for additional simulation input parameters, were also included in order to provide a statistical, Monte Carlo prediction of the EDL system performance. An overview of the pre-flight performance assessments completed, including the various simulation campaigns used, will be provided. Ultimately, this work was critical in the assessment of readiness for InSight launch. A brief description of the use of this simulation in support of flight operations is also discussed.

Robert W Maddock↗

NASA NTRSDate not supplied

Self-Aligned Focusing Schlieren at the 0.3-M Transonic Cryogenic Tunnel and the National Transonic Facility

The implementation of a self-aligned focusing schlieren (SAFS) system at two cryogenic wind tunnels at NASA Langley Research Center is discussed. Risk-reduction testing of the SAFS system was first performed at the 0.3-M Transonic Cryogenic Tunnel to evaluate the system’s operation in a small-scale characteristic cryogenic facility. Testing was conducted with three models: a three-quarter span 25.4-mm-diameter cylinder, a semi-span 65A006 tapered unswept airfoil, and a full-span SC(3)-0712 airfoil. Testing with the cylinder and semi-span airfoil revealed a highly dynamic shock environment, whereas the shock on the full-span airfoil was stationary, solidifying the usage of this model for a pre-/post-shock particle tracking velocimetry measurement. Temperature-induced polarization-altering window stresses were encountered during low-temperature testing and were mitigated using a “non-ideal” quartz/quartz Rochon prism that had largely been neglected since the SAFS system’s first introduction in favor of the more favorable “ideal” glass/quartz Rochon prism. The size of the SAFS system was then decreased in order to fit inside an environmentally controlled camera can enclosure at the National Transonic Facility (NTF) for testing of a sting-mounted aircraft model. The SAFS system was demonstrated to be effective at filtering out the large density gradient flow in the0.3-M plenum, and the thick, high density turbulent boundary layers on the wind tunnel walls at the NTF. Results of the testing campaigns and improvements to future systems are discussed.

Joshua M Weisberger↗

NASA NTRSDate not supplied

Self-Aligned Focusing Schlieren at the 0.3-M Transonic Cryogenic Tunnel and the National Transonic Facility

The implementation of a self-aligned focusing schlieren (SAFS) system at two cryogenic wind tunnels at NASA Langley Research Center is discussed. Risk-reduction testing of the SAFS system was first performed at the 0.3-M Transonic Cryogenic Tunnel to evaluate the system’s operation in a small-scale characteristic cryogenic facility. Testing was conducted with three models: a three-quarter span 25.4-mm-diameter cylinder, a semi-span 65A006 tapered unswept airfoil, and a full-span SC(3)-0712 airfoil. Testing with the cylinder and semi-span airfoil revealed a highly dynamic shock environment, whereas the shock on the full-span airfoil was stationary, solidifying the usage of this model for a pre-/post-shock particle tracking velocimetry measurement. Temperature-induced polarization-altering window stresses were encountered during low-temperature testing, and were mitigated using a “non-ideal” quartz/quartz Rochon prism that had largely been neglected since the SAFS system’s first introduction in favor of the more favorable “ideal” glass/quartz Rochon prism. The size of the SAFS system was then decreased in order to fit inside an environmentally-controlled camera can enclosure at the National Transonic Facility (NTF) for testing of a sting-mounted aircraft model. The SAFS system was demonstrated to be effective at filtering out the large density gradient flow in the 0.3-M plenum, and the thick, high density turbulent boundary layers on the wind tunnel walls at the NTF. Results of the testing campaigns and improvements to future systems are discussed.

Joshua M. Weisberger↗

NASA NTRS2020

InSight Entry, Descent and Landing Post-Flight Performance Assessment

On November 26, 2018, the Interior Exploration using Seismic Investigations, Geodesy and Heat Transport (InSight) lander successfully touched down on the surface of Mars. NASA Langley Research Center’s (LaRC) Program to Optimize Simulated Trajectories II (POST2) was used during both project development and flight operations to assess the Entry, Descent and Landing (EDL) vehicle performance against related requirements across the expected range of possible environmental and spacecraft conditions. During flight operations, these analyses were used to evaluate the need for updating flight software EDL parameters and the effects of executing a trajectory correction maneuver (TCM). Therefore, the NASA LaRC POST2 simulation had a critical role during the cruise, approach and ultimately EDL phases of the mission. This paper presents results of the final pre- and post- EDL flight performance assessments. A summary of the reconstructed “as-flown” trajectory with a comparison of key EDL metrics to the nominal and three-sigma bound pre-EDL predictions is also provided. Emphasis on the trajectory between entry interface and parachute deploy is provided as this is the period during which deviations from the pre-EDL prediction occurred. The post-flight assessment provides important verification of the POST2 models and analysis techniques used for InSight, providing critical feed-forward information to future lander missions.

Robert W Maddock↗

NASA NTRSDate not supplied

Reduction of Aircraft Noise Uncertainty for a Notional Supersonic Business Jet

NASA supported a study by the International Civil Aviation Organization on the environmental impact of adding supersonic aircraft to the existing global fleet by designing a notional 55-tonne supersonic business jet. The aircraft is referred to as the Supersonic Technology Concept Aeroplane (STCA) and has been used in a multitude of studies over the past few years. One of the many studies on this aircraft was the noise produced by the STCA during landing and take-off (LTO) operations. The LTO noise for the STCA was assessed using contemporary noise prediction tools that have been primarily developed for and utilized by subsonic aircraft, resulting in a high level of uncertainty for the STCA noise predictions. NASA has recently been conducting research and developing tools to reduce the uncertainty for airport noise predictions of supersonic aircraft. The initial focus of the uncertainty reduction was on the jet and inlet-radiated fan noise produced by supersonic engine systems since these sources are dominant during take-off and landing operations for supersonic jets. The results of these efforts are discussed and applied to the STCA model to update the airport noise predictions and associated uncertainty metrics. It is shown that by using new noise source prediction models, the overall system-level cumulative noise uncertainty of the study vehicle is reduced from a standard deviation of 7.8 EPNdB to 2.0 EPNdB.

uncertainty↗

NASA NTRS2005

The Calibration and Characterization of Earth Remote Sensing and Environmental Monitoring Instruments

The use of remote sensing instruments on orbiting satellite platforms in the study of Earth Science and environmental monitoring was officially inaugurated with the April 1, 1960 launch of the Television Infrared Observation Satellite (TIROS) [1]. The first TIROS accommodated two television cameras and operated for only 78 days. However, the TIROS program, in providing in excess of 22,000 pictures of the Earth, achieved its primary goal of providing Earth images from a satellite platform to aid in identifying and monitoring meteorological processes. This marked the beginning of what is now over four decades of Earth observations from satellite platforms. reflected and emitted radiation from the Earth using instruments on satellite platforms. These measurements are input to climate models, and the model results are analyzed in an effort to detect short and long-term changes and trends in the Earth's climate and environment, to identify the cause of those changes, and to predict or influence future changes. Examples of short-term climate change events include the periodic appearance of the El Nino-Southern Oscillation (ENSO) in the tropical Pacific Ocean [2] and the spectacular eruption of Mount Pinatubo on the Philippine island of Luzon in 1991. Examples of long term climate change events, which are more subtle to detect, include the destruction of coral reefs, the disappearance of glaciers, and global warming. Climatic variability can be both large and small scale and can be caused by natural or anthropogenic processes. The periodic El Nino event is an example of a natural process which induces significant climatic variability over a wide range of the Earth. A classic example of a large scale anthropogenic influence on climate is the well-documented rapid increase of atmospheric carbon dioxide occurring since the beginning of the Industrial Revolution [3]. An example of the study of a small-scale anthropogenic influence in climate variability is the Atlanta Land-use Analysis Temperature and Air-quality (ATLANTA) project [4]. This project has found that the replacement of trees and vegetation with concrete and asphalt in Atlanta, Georgia, and its environs has created a microclimate capable of producing wind and thunderstorms. A key objective of climate research is to be able to distinguish the natural versus human roles in climate change and to clearly communicate those findings to those who shape and direct environmental policy.

Butler, James J.↗

NASA NTRS2003

Systems Analysis Initiated for All-Electric Aircraft Propulsion

A multidisciplinary effort is underway at the NASA Glenn Research Center to develop concepts for revolutionary, nontraditional fuel cell power and propulsion systems for aircraft applications. There is a growing interest in the use of fuel cells as a power source for electric propulsion as well as an auxiliary power unit to substantially reduce or eliminate environmentally harmful emissions. A systems analysis effort was initiated to assess potential concepts in an effort to identify those configurations with the highest payoff potential. Among the technologies under consideration are advanced proton exchange membrane (PEM) and solid oxide fuel cells, alternative fuels and fuel processing, and fuel storage. Prior to this effort, the majority of fuel cell analysis done at Glenn was done for space applications. Because of this, a new suite of models was developed. These models include the hydrogen-air PEM fuel cell; internal reforming solid oxide fuel cell; balance-of-plant components (compressor, humidifier, separator, and heat exchangers); compressed gas, cryogenic, and liquid fuel storage tanks; and gas turbine/generator models for hybrid system applications. Initial mass, volume, and performance estimates of a variety of PEM systems operating on hydrogen and reformate have been completed for a baseline general aviation aircraft. Solid oxide/turbine hybrid systems are being analyzed. In conjunction with the analysis efforts, a joint effort has been initiated with Glenn s Computer Services Division to integrate fuel cell stack and component models with the visualization environment that supports the GRUVE lab, Glenn s virtual reality facility. The objective of this work is to provide an environment to assist engineers in the integration of fuel cell propulsion systems into aircraft and provide a better understanding of the interaction between system components and the resulting effect on the overall design and performance of the aircraft. Initially, three-dimensional computer-aided design (CAD) models of representative PEM fuel cell stack and components were developed and integrated into the virtual reality environment along with an Excel-based model used to calculate fuel cell electrical performance on the basis of cell dimensions (see the figure). CAD models of a representative general aviation aircraft were also developed and added to the environment. With the use of special headgear, users will be able to virtually manipulate the fuel cell s physical characteristics and its placement within the aircraft while receiving information on the resultant fuel cell output power and performance. As the systems analysis effort progresses, we will add more component models to the GRUVE environment to help us more fully understand the effect of various system configurations on the aircraft.

Kohout, Lisa L.↗

NASA NTRS2007

Comparison of Cloud Properties from CALIPSO-CloudSat and Geostationary Satellite Data

Cloud properties are being derived in near-real time from geostationary satellite imager data for a variety of weather and climate applications and research. Assessment of the uncertainties in each of the derived cloud parameters is essential for confident use of the products. Determination of cloud amount, cloud top height, and cloud layering is especially important for using these real -time products for applications such as aircraft icing condition diagnosis and numerical weather prediction model assimilation. Furthermore, the distribution of clouds as a function of altitude has become a central component of efforts to evaluate climate model cloud simulations. Validation of those parameters has been difficult except over limited areas where ground-based active sensors, such as cloud radars or lidars, have been available on a regular basis. Retrievals of cloud properties are sensitive to the surface background, time of day, and the clouds themselves. Thus, it is essential to assess the geostationary satellite retrievals over a variety of locations. The availability of cloud radar data from CloudSat and lidar data from CALIPSO make it possible to perform those assessments over each geostationary domain at 0130 and 1330 LT. In this paper, CloudSat and CALIPSO data are matched with contemporaneous Geostationary Operational Environmental Satellite (GOES), Multi-functional Transport Satellite (MTSAT), and Meteosat-8 data. Unlike comparisons with cloud products derived from A-Train imagers, this study considers comparisons of nadir active sensor data with off-nadir retrievals. These matched data are used to determine the uncertainties in cloud-top heights and cloud amounts derived from the geostationary satellite data using the Clouds and the Earth s Radiant Energy System (CERES) cloud retrieval algorithms. The CERES multi-layer cloud detection method is also evaluated to determine its accuracy and limitations in the off-nadir mode. The results will be useful for constraining the use of the passive retrieval data in models and for improving the accuracy of the retrievals.

Nguyen, L.↗

NASA NTRS1986

Simulation model for plant growth in controlled environment systems

The role of the mathematical model is to relate the individual processes to environmental conditions and the behavior of the whole plant. Using the controlled-environment facilities of the phytotron at North Carolina State University for experimentation at the whole-plant level and methods for handling complex models, researchers developed a plant growth model to describe the relationships between hierarchial levels of the crop production system. The fundamental processes that are considered are: (1) interception of photosynthetically active radiation by leaves, (2) absorption of photosynthetically active radiation, (3) photosynthetic transformation of absorbed radiation into chemical energy of carbon bonding in solube carbohydrates in the leaves, (4) translocation between carbohydrate pools in leaves, stems, and roots, (5) flow of energy from carbohydrate pools for respiration, (6) flow from carbohydrate pools for growth, and (7) aging of tissues. These processes are described at the level of organ structure and of elementary function processes. The driving variables of incident photosynthetically active radiation and ambient temperature as inputs pertain to characterization at the whole-plant level. The output of the model is accumulated dry matter partitioned among leaves, stems, and roots; thus, the elementary processes clearly operate under the constraints of the plant structure which is itself the output of the model.

Raper, C. D., Jr.↗

NASA NTRS2007

High-Performance, Radiation-Hardened Electronics for Space Environments

The Radiation Hardened Electronics for Space Environments (RHESE) project endeavors to advance the current state-of-the-art in high-performance, radiation-hardened electronics and processors, ensuring successful performance of space systems required to operate within extreme radiation and temperature environments. Because RHESE is a project within the Exploration Technology Development Program (ETDP), RHESE's primary customers will be the human and robotic missions being developed by NASA's Exploration Systems Mission Directorate (ESMD) in partial fulfillment of the Vision for Space Exploration. Benefits are also anticipated for NASA's science missions to planetary and deep-space destinations. As a technology development effort, RHESE provides a broad-scoped, full spectrum of approaches to environmentally harden space electronics, including new materials, advanced design processes, reconfigurable hardware techniques, and software modeling of the radiation environment. The RHESE sub-project tasks are: SelfReconfigurable Electronics for Extreme Environments, Radiation Effects Predictive Modeling, Radiation Hardened Memory, Single Event Effects (SEE) Immune Reconfigurable Field Programmable Gate Array (FPGA) (SIRF), Radiation Hardening by Software, Radiation Hardened High Performance Processors (HPP), Reconfigurable Computing, Low Temperature Tolerant MEMS by Design, and Silicon-Germanium (SiGe) Integrated Electronics for Extreme Environments. These nine sub-project tasks are managed by technical leads as located across five different NASA field centers, including Ames Research Center, Goddard Space Flight Center, the Jet Propulsion Laboratory, Langley Research Center, and Marshall Space Flight Center. The overall RHESE integrated project management responsibility resides with NASA's Marshall Space Flight Center (MSFC). Initial technology development emphasis within RHESE focuses on the hardening of Field Programmable Gate Arrays (FPGA)s and Field Programmable Analog Arrays (FPAA)s for use in reconfigurable architectures. As these component/chip level technologies mature, the RHESE project emphasis shifts to focus on efforts encompassing total processor hardening techniques and board-level electronic reconfiguration techniques featuring spare and interface modularity. This phased approach to distributing emphasis between technology developments provides hardened FPGA/FPAAs for early mission infusion, then migrates to hardened, board-level, high speed processors with associated memory elements and high density storage for the longer duration missions encountered for Lunar Outpost and Mars Exploration occurring later in the Constellation schedule.

Keys, Andrew S.↗

NASA NTRS2012

Performance Considerations for the SIMPL Single Photon, Polarimetric, Two-Color Laser Altimeter as Applied to Measurements of Forest Canopy Structure and Composition

The Slope Imaging Multi-polarization Photon-counting Lidar (SIMPL) is a multi-beam, micropulse airborne laser altimeter that acquires active and passive polarimetric optical remote sensing measurements at visible and near-infrared wavelengths. SIMPL was developed to demonstrate advanced measurement approaches of potential benefit for improved, more efficient spaceflight laser altimeter missions. SIMPL data have been acquired for wide diversity of forest types in the summers of 2010 and 2011 in order to assess the potential of its novel capabilities for characterization of vegetation structure and composition. On each of its four beams SIMPL provides highly-resolved measurements of forest canopy structure by detecting single-photons with 15 cm ranging precision using a narrow-beam system operating at a laser repetition rate of 11 kHz. Associated with that ranging data SIMPL provides eight amplitude parameters per beam unlike the single amplitude provided by typical laser altimeters. Those eight parameters are received energy that is parallel and perpendicular to that of the plane-polarized transmit pulse at 532 nm (green) and 1064 nm (near IR), for both the active laser backscatter retro-reflectance and the passive solar bi-directional reflectance. This poster presentation will cover the instrument architecture and highlight the performance of the SIMPL instrument with examples taken from measurements for several sites with distinct canopy structures and compositions. Specific performance areas such as probability of detection, after pulsing, and dead time, will be highlighted and addressed, along with examples of their impact on the measurements and how they limit the ability to accurately model and recover the canopy properties. To assess the sensitivity of SIMPL's measurements to canopy properties an instrument model has been implemented in the FLIGHT radiative transfer code, based on Monte Carlo simulation of photon transport. SIMPL data collected in 2010 over the Smithsonian Environmental Research Center, MD are currently being modelled and compared to other remote sensing and in situ data sets. Results on the adaptation of FLIGHT to model micropulse, single'photon ranging measurements are presented elsewhere at this conference. NASA's ICESat-2 spaceflight mission, scheduled for launch in 2016, will utilize a multi-beam, micropulse, single-photon ranging measurement approach (although non-polarimetric and only at 532 nm). Insights gained from the analysis and modelling of SIMPL data will help guide preparations for that mission, including development of calibration/validation plans and algorithms for the estimation of forest biophysical parameters.

Dabney, Philip W.↗

NASA NTRS2014

Meningitis and Climate: From Science to Practice

Meningococcal meningitis is a climate sensitive infectious disease. The regional extent of the Meningitis Belt in Africa, where the majority of epidemics occur, was originally defined by Lapeysonnie in the 1960s. A combination of climatic and environmental conditions and biological and social factors have been associated to the spatial and temporal patterns of epidemics observed since the disease first emerged in West Africa over a century ago. However, there is still a lack of knowledge and data that would allow disentangling the relative effects of the diverse risk factors upon epidemics. The Meningitis Environmental Risk Information Technologies Initiative (MERIT), a collaborative research-to-practice consortium, seeks to inform national and regional prevention and control strategies across the African Meningitis Belt through the provision of new data and tools that better determine risk factors. In particular MERIT seeks to consolidate a body of knowledge that provides evidence of the contribution of climatic and environmental factors to seasonal and year-to-year variations in meningococcal meningitis incidence at both district and national scales. Here we review recent research and practice seeking to provide useful information for the epidemic response strategy of National Ministries of Health in the Meningitis Belt of Africa. In particular the research and derived tools described in this paper have focused at "getting science into policy and practice" by engaging with practitioner communities under the umbrella of MERIT to ensure the relevance of their work to operational decision-making. We limit our focus to that of reactive vaccination for meningococcal meningitis. Important but external to our discussion is the development and implementation of the new conjugate vaccine, which specifically targets meningococcus A

Mineral dust↗

NASA NTRS2000

Hurricane Wind Field Measurements with Scanning Airborne Doppler Lidar During CAMEX-3

During the 1998 Convection and Moisture Experiment (CAMEX-3), the first hurricane wind field measurements with Doppler lidar were achieved. Wind fields were mapped within the eye, along the eyewall, in the central dense overcast, and in the marine boundary layer encompassing the inflow region. Spatial coverage was determined primarily by cloud distribution and opacity. Within optically-thin cirrus slant range of 20- 25 km was achieved, whereas no propagation was obtained during penetration of dense cloud. Measurements were obtained with the Multi-center Airborne Coherent Atmospheric Wind Sensor (MACAWS) on the NASA DC-8 research aircraft. MACAWS was developed and operated cooperatively by the atmospheric lidar remote sensing groups of NOAA Environmental Technology Laboratory, NASA Marshall Space Flight Center, and Jet Propulsion Laboratory. A pseudo-dual Doppler technique ("co-planar scanning") is used to map the horizontal component of the wind at several vertical levels. Pulses from the laser are directed out the left side of the aircraft in the desired directions using computer-controlled rotating prisms. Upon exiting the aircraft, the beam is completely eyesafe. Aircraft attitude and speed are taken into account during real-time signal processing, resulting in determination of the ground-relative wind to an accuracy of about 1 m/s magnitude and about 10 deg direction. Beam pointing angle errors are about 0.1 deg, equivalent to about 17 m at 10 km. Horizontal resolution is about 1 km (along-track) for typical signal processor and scanner settings; vertical resolution varies with range. Results from CAMEX-3 suggest that scanning Doppler wind lidar can complement airborne Doppler radar by providing wind field measurements in regions that are devoid of hydrometeors. At present MACAWS observations are being assimilated into experimental forecast models and satellite Doppler wind lidar simulations to evaluate the relative impact.

Rothermel, Jeffry↗