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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.

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At least 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Operation of the Australian Store.Synchrotron for macromolecular crystallography

The Store.Synchrotron service, a fully functional, cloud computing-based solution to raw X-ray data archiving and dissemination at the Australian Synchrotron, is described. The Store.Synchrotron service, a fully functional, cloud computing-based solution to raw X-ray data archiving and dissemination at the Australian Synchrotron, is described. The service automatically receives and archives raw diffraction data, related metadata and preliminary results of automated data-processing workflows. Data are able to be shared with collaborators and opened to the public. In the nine months since its deployment in August 2013, the service has handled over 22.4 TB of raw data (∼1.7 million diffraction images). Several real examples from the Australian crystallographic community are described that illustrate the advantages of the approach, which include real-time online data access and fully redundant, secure storage. Discoveries in biological sciences increasingly require multidisciplinary approaches. With this in mind, Store.Synchrotron has been developed as a component within a greater service that can combine data from other instruments at the Australian Synchrotron, as well as instruments at the Australian neutron source ANSTO. It is therefore envisaged that this will serve as a model implementation of raw data archiving and dissemination within the structural biology research community.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRSDate not supplied

Towards Autonomous Lunar Resource Excavation via Reinforcement Learning

To continue on a sustainable and flexible path, NASA needs to address the challenge of collecting and moving large amounts of regolith at the destination. NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU) processing. RASSOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar surface, RASSOR software and sensory systems need to be robust and maximize the information extracted from a reduced sensor payload. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We created reduced-order simulation environments to develop autonomous trenching controllers via reinforcement learning and prototype state estimation architectures. The goal of reinforcement learning is for an agent to learn a policy (task strategy) through interactions with an environment. When the agent performs an action, a change occurs in environment state and a numerical reward is received which informs the agent whether the action performed was good or not. Since reinforcement learning algorithms learn through trial-and-error, a simulation is a desirable first environment for development and learning. We developed two simulations, the first is a 2D excavation simulation developed to facilitate parameter selection, and a 3D simulation developed using a game physics engine, to simulate simplified soil interactions and increase the fidelity of the dynamic models of the robotic agents. The development of this 3D simulation has enabled the training of additional sensing capabilities and research both at the granular mechanics and operations levels. We experimented with various virtual sensor payloads to identify a combination that enabled efficient excavation operation and learning. Our reward function is based on how much material is excavated per step. A penalty is also received for leaving the dig site and to smooth the acceleration of the drum arms. We implemented pseudo time-of-flight sensors to report distance from each drum to ground and the height above ground which was found to be more efficient than existing solutions. Our findings suggest that reinforcement learning for autonomous operations has learned viable trenching strategies within 3000 training episodes in our simplified 2D environment and helped identify desirable sensing capabilities, arrangements, and considerations such as the positioning of time-of-flight sensors. Future work includes expanding our simulation to more complex environments and scenarios, and transfer learning from simulation to RASSOR 2.0 hardware for deployment in the Regolith Test Bin at NASA's Kennedy Space Center.

rassor↗
DOE OSTI2019

iMicrobe: Tools and data-driven discovery platform for the microbiome sciences

Background: Scientists have amassed a wealth of microbiome datasets, making it possible to study microbes in biotic and abiotic systems on a population or planetary scale; however, this potential has not been fully realized given that the tools, datasets, and computation are available in diverse repositories and locations. To address this challenge, we developed iMicrobe.us, a community-driven microbiome data marketplace and tool exchange for users to integrate their own data and tools with those from the broader community. Findings: The iMicrobe platform brings together analysis tools and microbiome datasets by leveraging National Science Foundation–supported cyberinfrastructure and computing resources from CyVerse, Agave, and XSEDE. The primary purpose of iMicrobe is to provide users with a freely available, web-based platform to (1) maintain and share project data, metadata, and analysis products, (2) search for related public datasets, and (3) use and publish bioinformatics tools that run on highly scalable computing resources. Analysis tools are implemented in containers that encapsulate complex software dependencies and run on freely available XSEDE resources via the Agave API, which can retrieve datasets from the CyVerse Data Store or any web-accessible location (e.g., FTP, HTTP). Conclusions: iMicrobe promotes data integration, sharing, and community-driven tool development by making open source data and tools accessible to the research community in a web-based platform.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2022

An Investigation Into Some Important Aspects of Droplet Breakup/Vaporization Behavior Caused By a Gas Flow (Both Nonreacting & Detonative Combustion) in a Shock Tube

As a part of the rotating detonation engine (RDE) technology enablement project at NASA Glenn Research Center (GRC), an effort was undertaken to extend our current computational capabilities of OpenNCC in some important ways with the implementation of a modeling approach to account for the droplet breakup caused by a shock-induced gas motion, & a vaporization model valid over a wide range of pressure conditions encountered in multiphase detonation. With the modified code, a study was undertaken to investigate the individual droplet behavior followed by the passage of a shock front. The study is carried out by tracking a sparse group of droplets to gain some understanding of shock induced droplet behavior under various shock strengths & fuel injector conditions. The study also looks into the effect of randomization involved in determining the droplet breakup outcomes. Over a wide range of sub-critical conditions examined, larger droplets are observed to undergo significant changes in droplet behavior following their breakup. However, smaller droplets (10 µm or less ) remain unaffected by any shock induced breakup. In a follow-on work, we investigated the impact of shock and droplet interaction in a detonation study involving both gaseous as well as gas/liquid (droplet clouds) fuel/air stoichiometric mixtures in a simple 3D shock-tube configuration. The droplet clouds are made up of different initial droplet sizes of either 6, 10, or 30 µm. We also investigated the individual droplet behavior followed by the passage of a detonation front. The results represent conditions that lead to both overdriven and C-J (ChapmanJouguet) detonations. Under both test conditions, most of the droplet vaporization is completed within a short distance (duration) behind the detonation front & well within the region of complete combustion observed in a corresponding equivalent gas-phase fuel/air mixture. The impact of the shock-induced droplet breakup is found to be significant in the calculations involving the 30-µm droplets. Subsequent to the breakup, the drop sizes vary from 1 to 10 µm. Another factor that contributed to the observed rapid vaporization is the result of vaporization taking place under supercritical conditions. The overall detonation properties of various droplet clouds (made up of different initial sizes) are similar to those observed in a corresponding gaseous fuel/air mixture. In the calculation involving a gaseous fuel, the calculated C-J detonation velocity is 1822 m/s involving Jet-A/air and φ = 1. In the overdriven detonation, it is 2044 m/s. In the calculations involving droplet clouds, the corresponding detonation velocities are lower. The impact of increased droplet size is primarily seen in a higher reduction in the detonation velocity.

shock/droplet interaction↗
DOE OSTI2017

Scaling Deep Learning on GPU and Knights Landing clusters

The speed of deep neural networks training has become a big bottleneck of deep learning research and development. For example, training GoogleNet by ImageNet dataset on one Nvidia K20 GPU needs 21 days. To speed up the training process, the current deep learning systems heavily rely on the hardware accelerators. However, these accelerators have limited on-chip memory compared with CPUs. To handle large datasets, they need to fetch data from either CPU memory or remote processors. We use both self-hosted Intel Knights Landing (KNL) clusters and multi-GPU clusters as our target platforms. From an algorithm aspect, current distributed machine learning systems are mainly designed for cloud systems. These methods are asynchronous because of the slow network and high fault-tolerance requirement on cloud systems. We focus on Elastic Averaging SGD (EASGD) to design algorithms for HPC clusters. Original EASGD used round-robin method for communication and updating. The communication is ordered by the machine rank ID, which is inefficient on HPC clusters. First, we redesign four efficient algorithms for HPC systems to improve EASGD's poor scaling on clusters. Async EASGD, Async MEASGD, and Hogwild EASGD are faster \textcolor{black}{than} their existing counterparts (Async SGD, Async MSGD, and Hogwild SGD, resp.) in all the comparisons. Finally, we design Sync EASGD, which ties for the best performance among all the methods while being deterministic. In addition to the algorithmic improvements, we use some system-algorithm codesign techniques to scale up the algorithms. By reducing the percentage of communication from 87% to 14%, our Sync EASGD achieves 5.3x speedup over original EASGD on the same platform. We get 91.5% weak scaling efficiency on 4253 KNL cores, which is higher than the state-of-the-art implementation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Stratospheric sulfate geoengineering could enhance the terrestrial photosynthesis rate

Stratospheric sulfate geoengineering could impact the terrestrial carbon cycle by enhancing the carbon sink. With an 8 Tg yr -1 injection of SO 2 to produce a stratospheric aerosol cloud to balance anthropogenic radiative forcing from the Representative Concentration Pathway 6.0 (RCP6.0) scenario, we conducted climate model simulations with the Community Earth System Model – the Community Atmospheric Model 4 fully coupled to tropospheric and stratospheric chemistry (CAM4–chem). During the geoengineering period, as compared to RCP6.0, land-averaged downward visible (300–700 nm) diffuse radiation increased 3.2 W m -2 (11%). The enhanced diffuse radiation combined with the cooling increased plant photosynthesis by 0.07±0.02 µmol C m -2 s -1 , which could contribute to an additional 3.8±1.1 Gt C yr -1 global gross primary productivity without explicit nutrient limitation. This increase could potentially increase the land carbon sink. Suppressed plant and soil respiration due to the cooling would reduce natural land carbon emission and therefore further enhance the terrestrial carbon sink during the geoengineering period. In conclusion, this potentially beneficial impact of stratospheric sulfate geoengineering would need to be balanced by a large number of potential risks in any future decisions about the implementation of geoengineering.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Impacts of Aerosol Dry Deposition on Black Carbon Spatial Distributions and Radiative Effects in the Community Atmosphere Model CAM5

Dry deposition is a vital process affecting the lifetime and spatial distributions of atmospheric aerosols. Black carbon (BC) plays an important role in the Earth’s climate. To explore the sensitivity of modeled BC spatial distributions to particle dry deposition treatments, the scheme of Petroff & Zhang (2010) (PZ10) is implemented into the Community Atmospheric Model version 5 (CAM5), and model simulations using PZ10 are compared with the one using the default scheme of Zhang et al. (2001) (Z01). The PZ10 scheme suggests much lower dry deposition velocities than Z01 for fine particles in Aitken, primary carbon, and accumulation modes, resulting in 73.0% lower of global mean BC dry deposition fluxes and 23.2% higher of global mean BC column burdens. CAM5 with PZ10 increases modeled BC concentrations at all altitudes and latitudes compared to Z01, which improves the agreement with observations of BC profiles in the lower troposphere in the Arctic. It also helps the simulation of surface BC concentrations in high-latitudes remote regions and its seasonality in the Arctic. Yet, PZ10 slightly exacerbates the overestimation of modeled BC concentrations in the middle and upper troposphere, which might be due to model uncertainties in other aerosol physical and chemical processes. The global annual mean radiative effects due to aerosol-radiation interactions (REari) and aerosol-cloud interactions (REaci) of BC from the CAM5 experiment using Z01 are 0.61±0.007 and -0.11±0.02 W m -2 , respectively, compared to slightly larger REari (0.75±0.01 W m -2 ) and REaci (-0.14±0.02 W m -2 ) from CAM5 using PZ10.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Energy flow networks: deep sets for particle jets

A key question for machine learning approaches in particle physics is how to best represent and learn from collider events. As an event is intrinsically a variable-length unordered set of particles, we build upon recent machine learning efforts to learn directly from sets of features or “point clouds”. Adapting and specializing the “Deep Sets” framework to particle physics, we introduce Energy Flow Networks, which respect infrared and collinear safety by construction. We also develop Particle Flow Networks, which allow for general energy dependence and the inclusion of additional particle-level information such as charge and flavor. These networks feature a per-particle internal (latent) representation, and summing over all particles yields an overall event-level latent representation. We show how this latent space decomposition unifies existing event representations based on detector images and radiation moments. To demonstrate the power and simplicity of this set-based approach, we apply these networks to the collider task of discriminating quark jets from gluon jets, finding similar or improved performance compared to existing methods. We also show how the learned event representation can be directly visualized, providing insight into the inner workings of the model. These architectures lend themselves to efficiently processing and analyzing events for a wide variety of tasks at the Large Hadron Collider. Implementations and examples of our architectures are available online in our EnergyFlow package.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

GoAmazon 2014/15. SRI-PTR-ToFMS Field Campaign Report

Our science team, including Dr. Alex Guenther (previously at Pacific Northwest National Laboratory (PNNL) and now at the University of California, Irvine) Dr. Saewung Kim and Dr. Roger Seco, and Dr. Jim Smith (previously at NCAR and now at UC Irvine), deployed a selected reagent ion – proton transfer reaction – time-of-flight mass spectrometer (SRI-PTR-TOFMS) to the T3 site during the GoAmazon study. One of the major uncertainties in climate model simulations is the effects of aerosols on radiative forcing, and a better understanding of the factors controlling aerosol distributions and life cycle is urgently needed. Aerosols contribute directly to the Earth’s radiation balance by scattering or absorbing light as a function of their physical properties and indirectly through particle-cloud interactions that lead to cloud formation and the modification of cloud properties. On a global scale, the dominant source of organic aerosol is biogenic volatile organic compounds (BVOC) emitted from terrestrial ecosystems. These organic aerosols are a major part of the total mass of all airborne particles and are currently not sufficiently represented in climate models. To incorporate quantitatively the effects of BVOCs and their oxidation products on biogenic organic aerosol (BOA) requires parameterization of their production in terrestrial ecosystems and their atmospheric transformations. This project was designed to reduce the gaps in our understanding of how these processes control BVOCs and BOAs, and their impact on climate. This was accomplished by wet and dry season measurements at the U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) Climate Research Facility T3 site along with a comprehensive suite of complementary measurements. The specific goals were to 1) measure and mechanistically understand the factors affecting aerosol distributions over a tropical rain forest, especially the effects of anthropogenic pollution as a perturbation to the natural state, and 2) develop and implement an upscaling analysis from this new data set and knowledge of aerosol distributions to prognosticate possible climatic impacts of present-day urban pollution and possibly greater pollution in the future.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2003

Doppler Radar and Lightning Network Observations of a Severe Outbreak of Tropical Cyclone Tornadoes

Data from a single WSR-88D Doppler radar and the National Lightning Detection Network are used to examine in detail the characteristics of the convective storms that produced a severe tornado outbreak within Tropical Storm Beryl's remnants on 16 August 1994. Comparison of the radar data with reports of tornadoes suggests that only 13 cells produced the 29 tornadoes that were documented in Georgia and the Carolinas on that date. Six of these cells spawned multiple tornadoes, and the radar data confirm the presence of miniature supercells. One of the cells was identifiable on radar for 11 hours, spawning tornadoes over a time period spanning approximately 6.5 hours. Several other tornadic cells also exhibited great longevity, with cell lifetimes greater than ever previously documented in a landfalling tropical cyclone tornado event, and comparable to those found in major midlatitude tornadic supercell outbreaks. Time-height analyses of the three strongest tornadic supercells are presented in order to document storm kinematic structure and to show how these storms appear at different ranges from a WSR-88D radar. In addition, cloud-to-ground (CG) lightning data are examined for the outbreak, the most intense tropical cyclone tornado event studied thus far. Although the tornadic cells were responsible for most of Beryl's CG lightning, flash rates were only weak to moderate, even in the most intense supercells, and in all the tornadic storms the lightning flashes were almost entirely negative in polarity. A few of the single-tornado storms produced no detectable CG lightning at all. In the stronger cells, there is some evidence that CG lightning rates decreased during tornadogenesis, as has been documented before in some midlatitude tornadic storms. A number of the storms spawned tornadoes just after producing their final CG lightning flashes. Surprisingly, both peak currents and positive flash percentages were larger in Beryl s nontornadic storms than in the tornadic ones. Despite some intriguing patterns, the CG lightning behavior in this outbreak remains mostly inconsistent and ambiguous, and offers only secondary value for warning guidance. The present findings argue in favor of the implementation of observing systems capable of continuous monitoring of total lightning activity in storms.

McCaul, Eugene W., Jr.↗
NASA NTRS2016

Cryogenic Fiber Optic Assemblies for Spaceflight Environments: Design, Manufacturing, Testing, and Integration

Fiber optic assemblies have been used on spaceflight missions for many years as an enabling technology for routing, transmitting, and detecting optical signals. Due to the overwhelming success of NASA in implementing fiber optic assemblies on spaceflight science-based instruments, system scientists increasingly request fibers that perform in extreme environments while still maintaining very high optical transmission, stability, and reliability. Many new applications require fiber optic assemblies that will operate down to cryogenic temperatures as low as 20 Kelvin. In order for the fiber assemblies to operate with little loss in optical throughput at these extreme temperatures requires a system level approach all the way from how the fiber assembly is manufactured to how it is held, routed, and integrated. The NASA Goddard Code 562 Photonics Group has been designing, manufacturing, testing, and integrating fiber optics for spaceflight and other high reliability applications for nearly 20 years. Design techniques and lessons learned over the years are consistently applied to developing new fiber optic assemblies that meet these demanding environments. System level trades, fiber assembly design methods, manufacturing, testing, and integration will be discussed. Specific recent examples of ground support equipment for the James Webb Space Telescope (JWST); the Ice, Cloud and Land Elevation Satellite-2 (ICESat-2); and others will be included.

Fiber Optic↗
DOE OSTI2022

Effect of the Grell-Freitas Deep Convection Scheme in Quasi-Uniform and Variable-Resolution Aquaplanet CAM Simulations

The Grell-Freitas deep convection scheme (GF) has been implemented in the Community Atmosphere Model (CAM) with the nonhydrostatic Model for Prediction Across Scales (MPAS) dynamical core. Aquaplanet simulations have been performed with a global quasi-uniform resolution mesh with ~120 km grid spacing and a variable resolution mesh employing a circular refined region with ~30 km grid spacing centering at the equator and ~120 km elsewhere. GF produces overall patterns of the general circulations comparable to those of other aquaplanet experiments, including those produced using the default Zhang-McFarlane convection scheme (ZM) in CAM Version 5.4. GF alleviates the overestimated occurrence of weak intensity precipitation characteristic of ZM due to GF’s different closure methods and deep-convection trigger conditions. Compared to ZM, the GF characteristics of the equatorial Kelvin waves are closer to observations. GF also better simulates precipitation at variable resolution, with no unrealistic wave distortion by the refinement. Overall, GF shows less sensitivity of convective precipitation and heating/moistening tendencies to changes in horizontal resolution compared to ZM. However, vertically-overlapped total cloud fraction simulated with GF in the tropics is more sensitive to the resolution than ZM due to the larger resolution-sensitivity of high-level cloud fraction in the tropics.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2017

Optimal A-Train Data Utilization: A Use Case of Aura OMI L2G and MERRA-2 Aerosol Products

Ozone Monitoring Instrument (OMI) aboard NASA's Aura mission measures ozone column and profile, aerosols, clouds, surface UV irradiance, and the trace gases including NO2, SO2, HCHO, BrO, and OClO using UltraViolet electromagnetic spectrum (280 - 400 nm) with a daily global coverage and a pixel spatial resolution of 13 km × 24 km at nadir, and it's been one of the key instruments to study the Earth's atmospheric composition and chemistry. The second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) is NASA's atmospheric reanalysis using an upgraded version of Goddard Earth Observing System Model, version 5 (GEOS-5) data assimilation system. Compared to its predecessor MERRA, MERRA-2 is enhanced with more aspects of the Earth system among which is aerosol assimilation. When comparing between satellite pixel measurements and modeled grid data, how to properly handle counterpart pairing is critical considering their spatial and temporal variations. The comparison between satellite and model data by simply using Level 3 (L3) products may result biases due to lack of detailed temporal information. It has been preferred to inter-compare or implement satellite derived physical quantity (i.e., Level 2 (L2) Swath type) directly with/to model measurements with higher temporal and spatial resolution as possible. However, this has posed a challenge in the community to handle. Rather than directly handling the L2 or L3 data, there is a Level 2G (L2G) product conserving L2 pixel scientific data quality but in Grid type with the global coverage. In this presentation, we would like to demonstrate the optimal utilization of OMI L2G daily aerosol products by comparing with MERRA-2 hourly aerosol simulations matched well in both space and time.

MERRA-2 reanalysis↗
DOE OSTI2023

Software Bill of Materials (SBOM) Sharing Lifecycle Report

As Software Bill of Materials (SBOM) adoption efforts mature, SBOM sharing continues to occur, but no single solution or set of solutions have become ubiquitous. The purpose of this report is to enumerate and describe the different parties and phases of the SBOM sharing lifecycle and assist readers in choosing suitable SBOM sharing solutions based on the amount of time, resources, subject-matter expertise, effort, and access to tooling that is available to the reader to implement a phase of the SBOM sharing lifecycle. The SBOM sharing lifecycle consists of the Discovery, Access, and Transport of an SBOM and this report details these individual phases and how an SBOM goes from author to the consumer. This report also details how potential enrichment activities may be performed on an SBOM to create a new product before or after it has been shared. The concept of a sophistication classification for SBOM sharing solutions is concurrently introduced with a focus on the inclusion or lack of certain features and effort associated with their implementation. Examples of low, medium, and high-sophistication solutions are provided; however, these examples and associated categorizations should not be seen as a qualitative judgment meant to push the reader towards a particular adoption strategy since sharing solutions are chosen based on the unique needs of the user. This report does recommend the SBOM community consider how to make current and future sharing solutions interoperable with each other as well as more automated methods to facilitate sharing and broader SBOM adoption. This report also highlights an SBOM sharing survey results obtained from interviews with stakeholders to understand the current SBOM sharing landscape. The categorized results of the survey suggest that SBOMs are currently transported directly to the receiver through email or similar informal communication mechanisms or alternatively the SBOM resides on a repository available to consumers. In addition to these transport methods, this report captures industry efforts to create private sharing solutions and services that can store and transport enrichment data and may use higher sophistication features that are cloud-based or using distributed ledger technologies.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2017

Generating Land Surface Reflectance for the New Generation of Geostationary Satellite Sensors with the MAIAC Algorithm

The latest generation of geostationary satellite sensors, including the GOES-16/ABI and the Himawari 8/AHI, provide exciting capability to monitor land surface at very high temporal resolutions (5-15 minute intervals) and with spatial and spectral characteristics that mimic the Earth Observing System flagship MODIS. However, geostationary data feature changing sun angles at constant view geometry, which is almost reciprocal to sun-synchronous observations. Such a challenge needs to be carefully addressed before one can exploit the full potential of the new sources of data. Here we take on this challenge with Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, recently developed for accurate and globally robust applications like the MODIS Collection 6 re-processing. MAIAC first grids the top-of- atmosphere measurements to a fixed grid so that the spectral and physical signatures of each grid cell are stacked (“remembered”) over time and used to dramatically improve cloud/shadow/snow detection, which is by far the dominant error source in the remote sensing. It also exploits the changing sun-view geometry of the geostationary sensor to characterize surface BRDF with augmented angular resolution for accurate aerosol retrievals and atmospheric correction. The high temporal resolutions of the geostationary data indeed make the BRDF retrieval much simpler and more robust as compared with sun-synchronous sensors such as MODIS. As a prototype test for the geostationary-data processing pipeline on NASA Earth Exchange (GEONEX), we apply MAIAC to process 18 months of data from Himawari 8/AHI over Australia. We generate a suite of test results, including the input TOA reflectance and the output cloud mask, aerosol optical depth (AOD), and the atmospherically-corrected surface reflectance for a variety of geographic locations, terrain, and land cover types. Comparison with MODIS data indicates a general agreement between the retrieved surface reflectance products. Furthermore, the geostationary results satisfactorily capture the movement of clouds and variations in atmospheric dust/aerosol concentrations, suggesting that high quality land surface and vegetation datasets from the advanced geostationary sensors can help complement and improve the corresponding EOS products.

geostationary satellite sensors↗
NASA NTRSDate not supplied

The Transition of RGB Imagery Applied to Fog and Low Clouds from NASA Capabilities within the GOES Proving Ground to the Present Era

A wide range of RGB Imagery products are now available from the GOES ABI instrument, but less than a decade earlier, these false-color imagery products were only seen over Europe and Africa within the suite of the EUMETSAT SEVIRI geostationary instrument. The issue of fog and low clouds as applied to aviation and ground transportation hazards has been a long-standing challenge where satellite imagery provides notable value to visually inspect large spatial areas vs relying on in situ point observations. A legacy channel difference product was the standard satellite imagery tool, but the Nighttime Microphysics RGB would soon be introduced with GOES-R and -S where users had no interpretation experience. As part of the GOES Proving Ground efforts, the SPoRT program utilized existing NASA LEO satellites to demonstrate the future ABI capabilities. Operational users were able to test and adopt the RGBs from NASA LEO instruments prior to the GOES-R launch in order to be ready for the wealth of new channels available from ABI. The transition experience and methods from these proxy RGBs to the start of the GOES-R ABI era to the present day provides insights to the value of these new qualitative products in operations and to how the transition method of such products can be successfully implemented as well as future needs in the RGB Imagery area.

Kevin Fuell↗
NASA NTRS2014

Infrared Spectral Radiance Intercomparisons With Satellite and Aircraft Sensors

Measurement system validation is critical for advanced satellite sounders to reach their full potential of improving observations of the Earth's atmosphere, clouds, and surface for enabling enhancements in weather prediction, climate monitoring capability, and environmental change detection. Experimental field campaigns, focusing on satellite under-flights with well-calibrated FTS sensors aboard high-altitude aircraft, are an essential part of the validation task. Airborne FTS systems can enable an independent, SI-traceable measurement system validation by directly measuring the same level-1 parameters spatially and temporally coincident with the satellite sensor of interest. Continuation of aircraft under-flights for multiple satellites during multiple field campaigns enables long-term monitoring of system performance and inter-satellite cross-validation. The NASA / NPOESS Airborne Sounder Testbed - Interferometer (NAST-I) has been a significant contributor in this area by providing coincident high spectral/spatial resolution observations of infrared spectral radiances along with independently-retrieved geophysical products for comparison with like products from satellite sensors being validated. This presentation gives an overview of benefits achieved using airborne sensors such as NAST-I utilizing examples from recent field campaigns. The methodology implemented is not only beneficial to new sensors such as the Cross-track Infrared Sounder (CrIS) flying aboard the Suomi NPP and future JPSS satellites but also of significant benefit to sensors of longer flight heritage such as the Atmospheric InfraRed Sounder (AIRS) and the Infrared Atmospheric Sounding Interferometer (IASI) on the AQUA and METOP-A platforms, respectively, to ensure data quality continuity important for climate and other applications. Infrared spectral radiance inter-comparisons are discussed with a particular focus on usage of NAST-I data for enabling inter-platform cross-validation.

Larar, Allen M.↗
NASA NTRS2001

A Second Generation Multi-Angle Imaging SpectroRadiometer (MISR-2)

The Multi-angle Imaging SpectroRadiometer (MISR) has been in Earth orbit since December 1999 on NASA's Terra spacecraft. This instrument provides new ways of looking at the Earth's atmosphere, clouds, and surface for the purpose of understanding the Earth's ecology, environment, and climate. To facilitate the potential future continuation of MISR's multi-angle observations, a study was undertaken in 1999 and 2000 under the Instrument Incubator Program (IIP) of NASA Code Y's Earth Science Technology Office (ESTO) to investigate and demonstrate the feasibility of a successor to MISR that will have greatly reduced size and mass. The kernel of the program was the design, construction, and testing of a highly miniaturized camera, one of the nine that would probably be used on a future space borne MISR-like instrument. This demonstrated that the size and mass reduction of the optical system and camera electronics are possible and that filters can be assembled to meet the miniaturized packaging requirements. An innovative, reflective optics design was used, enabling the wavelength range to be extended into the shortwave infrared. This was the smallest all-reflective camera ever produced by the contractor. A study was undertaken to determine the feasibility of implementing nine (multi-angle) cameras within a single structure. This resulted in several possible configurations. It would also be possible to incorporate one of the cameras into an airborne instrument.

spectroradiometers↗