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At least 181 records · Page 10

An invertible seven-dimensional Dirichlet cell characterization of lattices

Characterization of crystallographic lattices is an important tool in structure solution, crystallographic database searches and clustering of diffraction images in serial crystallography. Characterization of lattices by Niggli-reduced cells (based on the three shortest non-coplanar lattice vectors) or by Delaunay-reduced cells (based on four non-coplanar vectors summing to zero and all meeting at obtuse or right angles) is commonly performed. The Niggli cell derives from Minkowski reduction. The Delaunay cell derives from Selling reduction. All are related to the Wigner–Seitz (or Dirichlet, or Voronoi) cell of the lattice, which consists of the points at least as close to a chosen lattice point as they are to any other lattice point. The three non-coplanar lattice vectors chosen are here called the Niggli-reduced cell edges. Starting from a Niggli-reduced cell, the Dirichlet cell is characterized by the planes determined by 13 lattice half-edges: the midpoints of the three Niggli cell edges, the six Niggli cell face-diagonals and the four body-diagonals, but seven of the lengths are sufficient: three edge lengths, the three shorter of each pair of face-diagonal lengths, and the shortest body-diagonal length. These seven are sufficient to recover the Niggli-reduced cell.

Chemistry↗

The Crew Earth Observations Experiment: Earth System Science from the ISS

This viewgraph presentation reviews the use of Astronaut Photography (AP) as taken from the International Space Station (ISS) in Earth System Science (ESS). Included are slides showing basic remote sensing theory, data characteristics of astronaut photography, astronaut training and operations, crew Earth observations group, targeting sites and acquisition, cataloging and database, analysis and applications for ESS, image analysis of particular interest urban areas, megafans, deltas, coral reefs. There are examples of the photographs and the analysis.

Stefanov, William L.↗

Aerosol Optical Depth Distribution in Extratropical Cyclones over the Northern Hemisphere Oceans

Using Moderate Resolution Imaging Spectroradiometer and an extratropical cyclone database,the climatological distribution of aerosol optical depth (AOD) in extratropical cyclones is explored based solely on observations. Cyclone-centered composites of aerosol optical depth are constructed for the Northern Hemisphere mid-latitude ocean regions, and their seasonal variations are examined. These composites are found to be qualitatively stable when the impact of clouds and surface insolation or brightness is tested. The larger AODs occur in spring and summer and are preferentially found in the warm frontal and in the post-cold frontal regions in all seasons. The fine mode aerosols dominate the cold sector AODs, but the coarse mode aerosols display large AODs in the warm sector. These differences between the aerosol modes are related to the varying source regions of the aerosols and could potentially have different impacts on cloud and precipitation within the cyclones.

MODIS↗

The Arctic-Boreal vulnerability experiment model benchmarking system

NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) integrates field and airborne data into modeling and synthesis activities for understanding Arctic and Boreal ecosystem dynamics. The ABoVE Benchmarking System (ABS) is an operational software package to evaluate terrestrial biosphere models against key indicators of Arctic and Boreal ecosystem dynamics, i.e.: carbon biogeochemistry, vegetation, permafrost, hydrology, and disturbance. The ABS utilizes satellite remote sensing data, airborne data, and field data from ABoVE as well as collaborating research networks in the region, e.g.: the Permafrost Carbon Network, the International Soil Carbon Network, the Northern Circumpolar Soil Carbon Database, AmeriFlux sites, the Moderate Resolution Imaging Spectroradiometer, the Orbiting Carbon Observatory 2, and the Soil Moisture Active Passive mission. The ABS is designed to be interactive for researchers interested in having their models accurately represent observations of key Arctic indicators: a user submits model results to the system, the system evaluates the model results against a set of Arctic-Boreal benchmarks outlined in the ABoVE Concise Experiment Plan, and the user then receives a quantitative scoring of model strengths and deficiencies through a web interface. This interactivity allows model developers to iteratively improve their model for the Arctic-Boreal Region by evaluating results from successive model versions. We show here, for illustration, the improvement of the Lund–Potsdam–Jena-Wald Schnee und Landschaft (LPJwsl) version model through the ABoVE ABS as a new permafrost module is coupled to the existing model framework. The ABS will continue to incorporate new benchmarks that address indicators of Arctic-Boreal ecosystem dynamics as they become available.

ABoVE↗

An imaging spectrometric observatory for Spacelab

The large payload capacity of the Space Shuttle Orbiter offers the first opportunity to launch into space an instrument able to simultaneously measure the entire spectrum of atmospheric emission, from the extreme UV to the near-IR, with high spectral resolution and high sensitivity while obtaining global and altitude coverage. This capability would provide a database for atmospheric process studies. The Spacelab Imaging Spectrometric Observatory (ISO) comprises an array of five spectrometers designed to make full use of the Space Shuttle as an observing platform for atmosphere remote sensing, and covers the 300-12,000-A wavelength range with a 2-7 A resolution. Area array detectors can scan the entire wavelength range in less than 20 sec and, being two-dimensional, permit spectral imaging in one direction and spatial imaging on the other.

Torr, M. R.↗

Principal Components Analysis of Reflectance Spectra Returned by the Mars Exploration Rover Opportunity

The Mars Exploration Rover Opportunity has spent over six years exploring the Martian surface near its landing site at Meridiani Planum. Meridiani bedrock observed by the rover is largely characterized by sulfate-rich sandstones and hematite spherules, recording evidence of ancient aqueous environments [1]. The region is a deflationary surface, allowing hematite spherules, fragments of bedrock, and "cobbles" of foreign origin to collect loosely on the surface. These cobbles may be meteorites (e.g., Barberton, Heat Shield Rock, Santa Catarina) [2], or rock fragments of exotic composition derived from adjacent terranes or from the subsurface and delivered to Meridiani Planum as impact ejecta [3]. The cobbles provide a way to better understand Martian meteorites and the lithologic diversity of Meridiani Planum by examining the various rock types located there. In the summer of 2007, a global dust storm on Mars effectively disabled Opportunity's Miniature Thermal Emission Spectrometer (Mini-TES), which served as the Athena Science Team s primary tool for remotely identifying rocks of interest on a tactical timescale for efficient rover planning. While efforts are ongoing to recover use of the Mini-TES, the team is currently limited to identifying rocks of interest by visual inspection of images returned from Opportunity's Panoramic Camera (Pancam). This study builds off of previous efforts to characterize cobbles at Meridiani Planum using a database of reflectance spectra extracted from Pancam 13-Filter (13F) images [3]. We analyzed the variability of rock spectra in this database and identified physical characteristics of Martian rocks that could potentially account for the observed variance. By understanding such trends, we may be able to distinguish between rock types at Meridiani Planum and regain the capability to remotely identify locally unique rocks.

Mercer, C. M.↗

Leveraging the Multiplatform Precipitation Feature Database of Combined Ground Radar and Satellite Lightning Observations for Convective Studies

The Multiplatform Precipitation Feature (MPF) database combines coincident observations of lightning from space with satellite- and ground-based radar data in a storm-based, feature-defined framework. The concept for the MPF database stems from the motivation to extend the scale and scope of investigations into the complex connections between ice precipitation, updrafts, and lightning in thunderstorms. The earliest iteration of the MPF database incorporates NASA International Space Station Lightning Imaging Sensor (ISS LIS) observations alongside the NASA Global Precipitation Measurement (GPM) Mission Dual-frequency Precipitation Radar (DPR) and Validation Network (VN). Because of the specific role of the GPM VN in forming the first MPFs, this subset is referred to as the VNMPF database. The synthesis of these multi-scale and multi-resolution observations leveraged each platform’s unique insights into convective properties and processes, offering a more complete view of deep convection over the large viewing area afforded by satellite coverage. The VNMPF database established the feasibility and infrastructure to combine microphysical, kinematic, and lightning observations from multiple satellite- and ground-based platforms. Following this successful proof-of-concept, recent progress has expanded the MPF database to make use of coincident ISS LIS lightning observations and data from the operational Weather Surveillance Radar - 1988 Doppler (WSR-88D) network, where individual WSR-88D proximity facilitates vertical wind retrievals via dual-Doppler analysis. These database changes have expanded the scope of the dataset by removing the requirement for coincident GPM and ISS LIS overpasses. This increases the sample size dramatically and facilitates analysis of higher-resolution radar-derived properties. These advancements allow both storm-scale and unprecedented broad statistical analyses of deep convection from electrical, kinematic, and microphysical perspectives over the contiguous United States. This presentation outlines the construction of the new MPF database and introduces some preliminary analyses of the convection captured within it. Early results summarize relationships based on updraft characteristics ascertained from WSR-88D dual-Doppler three-dimensional wind retrievals, properties of ice microphysics gleaned from dual-polarization analyses, and electrical characteristics observed by the ISS LIS. The expanded scale and scope offered by more than five years of ISS LIS observations enable new insights into regional and seasonal variations in the microphysical, kinematic, and electrical relationships of convection. Analysis of the database highlights the potential for advancements in lightning meteorology made possible by combining large-scale spaceborne lightning detection and proven storm interrogation tools such as operational polarimetric Doppler radar.

Lightning↗

Potential of deep learning methods to enhance satellite-based monitoring of nuclear power plants focusing on remote operation evaluations

The anticipated expansion of the nuclear industry and the deployment of new nuclear reactors (200 + GW of new nuclear capacity by 2050) require the development of monitoring systems that align with safety and security concerns, providing enhanced evaluation capabilities. A remote monitoring system using satellites and deep learning techniques was evaluated for its ability to detect anomalies and capture various features of nuclear reactors independently of the conditions on the ground. Satellite images of current operational and under-construction nuclear power plants were collected from Google Earth Pro as a surrogate database. Subsequently, five datasets were created from the collected images. Transfer learning technique was used for several classification tasks utilizing VGG16, ResNet50V2, Xception, DenseNet121, and MobileNetV2 pre-trained models. In the first task, the capability of the monitoring system to detect abnormal conditions or processes in a nuclear power plant was investigated. In the second task, the ability to capture operational features remotely was examined. As an example, for the purposes of this study, these features included classifying reactors based on type, power range, or onsite condition. Several evaluation metrics were used to compare the performance of the pre-trained models and the overall monitoring system. Here, the evaluation results demonstrated that deep learning techniques and pre-trained models applied to satellite images have the potential to facilitate further and expand capabilities in monitoring systems to assess plant operation details.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

International Polar Year Observations From the International Space Station

Astronauts aboard the International Space Station (ISS) have several opportunities each day to observe and document high-latitude phenomena. Although lighting conditions, ground track and other viewing parameters change with orbital precessions and season, the 51.6 degree orbital inclination and 400 km altitude of the ISS provide the crew an excellent vantage point for collecting image-based data for IPY investigators. To date, the database of imagery acquired by the Crew Earth Observations (CEO) experiment aboard the ISS (http://eol.jsc.nasa.gov) contains more than 12,000 images of high latitude (above 50 degrees) events such as aurora, mesospheric clouds, sea-ice, high-latitude plankton blooms, volcanic eruptions, and snow cover. The ISS Program will formally participate in IPY through an activity coordinated through CEO entitled Synchronized Observations of Polar Mesospheric Clouds, Aurora and Other Large-scale Polar Phenomena from the ISS and Ground Sites. The activity will augment the existing collection of Earth images taken from the ISS by focusing astronaut observations on polar phenomena. NASA s CEO experiment will solicit requests by IPY investigators for ISS observations that are coordinated with or complement ground-based polar studies. The CEO imagery website (http://eol.jsc.nasa.gov) will provide an on-line form for IPY investigators to interact with CEO scientists and define their imagery requests. This information will be integrated into daily communications with the ISS crews about their Earth Observations targets. All data collected will be cataloged and posted on the website for downloading and assimilation into IPY projects.

Pettit, Donald R.↗

Image Labeler: A Web Interface to Catalog Earth Science Events

Advances in machine learning (ML) have made it possible to automatically detect Earth science phenomena from satellite imagery. While useful, ML algorithms typically require an extensive dataset containing labeled images for training. Systematic labeling and management of such datasets is quite cumbersome. With this in mind, we present the Image Labeler. Image Labeler is a fast and scalable cloud-based tool that facilitates the rapid development of Earth science event databases, in order to aid automated ML-based image classification.

Case Study↗

Numerical Model Sensitivity to Heterogeneous Satellite Derived Vegetation Roughness

The sensitivity of a mesoscale weather prediction model to a 1 km satellite-based vegetation roughness initialization is investigated for a domain within the south central United States. Three different roughness databases are employed: i) a control or standard lookup table roughness that is a function only of land cover type, ii) a spatially heterogeneous roughness database, specific to the domain, that was previously derived using a physically based procedure and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery, and iii) a MODIS climatologic roughness database that like (i) is a function only of land cover type, but possesses domain specific mean values from (ii). The model used is the Weather Research and Forecast Model (WRF) coupled to the Community Land Model within the Land Information System (LIS). For each simulation, a statistical comparison is made between modeled results and ground observations within a domain including Oklahoma, Eastern Arkansas, and Northwest Louisiana during a 4-day period within IHOP 2002. Sensitivity analysis compares the impact the three roughness initializations on time-series temperature, precipitation probability of detection (POD), average wind speed, boundary layer height, and turbulent kinetic energy (TKE). Overall, the results indicate that, for the current investigation, replacement of the standard look-up table values with the satellite-derived values statistically improves model performance for most observed variables. Such natural roughness heterogeneity enhances the surface wind speed, PBL height and TKE production up to 10 percent, with a lesser effect over grassland, and greater effect over mixed land cover domains.

Jasinski, Michael↗

Three-dimensional reconstruction of laser-direct-drive inertial confinement fusion hot-spot plasma from x-ray diagnostics on the OMEGA laser facility (invited)

A deep-learning convolutional neural network (CNN) is used to infer, from x-ray images along multiple lines of sight, the low-mode shape of the hot-spot emission of deuterium–tritium (DT) laser-direct-drive cryogenic implosions on OMEGA. The motivation of this approach is to develop a physics-informed 3-D reconstruction technique that can be performed within minutes to facilitate the use of the results to inform changes to the initial target and laser conditions for the subsequent implosion. The CNN is trained on a 3D radiation-hydrodynamic simulation database to relate 2D x-ray images to 3D emissivity at stagnation. The CNN accounts for the lack of an absolute spatial reference and the different bands of photon energies in the x-ray images. While previous works studied the effect of mode-1 asymmetries on implosion performance using nuclear diagnostics, this work focuses on the effect of mode 2 inferred from x-ray diagnostics on implosion performance. A current analysis of 19 DT cryogenic implosions indicates there is an upper limit of ~20% reduction in the neutron yield caused by an ℓ = 2 amplitude for ℓ 2 /ℓ 0 ≤ 0.32. Here, these conclusions are supported by 2D simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Passive IFF: Autonomous Nonintrusive Rapid Identification of Friendly Assets

A proposed optoelectronic instrument would identify targets rapidly, without need to radiate an interrogating signal, apply identifying marks to the targets, or equip the targets with transponders. The instrument was conceived as an identification, friend or foe (IFF) system in a battlefield setting, where it would be part of a targeting system for weapons, by providing rapid identification for aimed weapons to help in deciding whether and when to trigger them. The instrument could also be adapted to law-enforcement and industrial applications in which it is necessary to rapidly identify objects in view. The instrument would comprise mainly an optical correlator and a neural processor (see figure). The inherent parallel-processing speed and capability of the optical correlator would be exploited to obtain rapid identification of a set of probable targets within a scene of interest and to define regions within the scene for the neural processor to analyze. The neural processor would then concentrate on each region selected by the optical correlator in an effort to identify the target. Depending on whether or not a target was recognized by comparison of its image data with data in an internal database on which the neural processor was trained, the processor would generate an identifying signal (typically, friend or foe ). The time taken for this identification process would be less than the time needed by a human or robotic gunner to acquire a view of, and aim at, a target. An optical correlator that has been under development for several years and that has been demonstrated to be capable of tracking a cruise missile might be considered a prototype of the optical correlator in the proposed IFF instrument. This optical correlator features a 512-by-512-pixel input image frame and operates at an input frame rate of 60 Hz. It includes a spatial light modulator (SLM) for video-to-optical image conversion, a pair of precise lenses to effect Fourier transforms, a filter SLM for digital-to-optical correlation-filter data conversion, and a charge-coupled device (CCD) for detection of correlation peaks. In operation, the input scene grabbed by a video sensor is streamed into the input SLM. Precomputed correlation-filter data files representative of known targets are then downloaded and sequenced into the filter SLM at a rate of 1,000 Hz. When there occurs a match between the input target data and one of the known-target data files, the CCD detects a correlation peak at the location of the target. Distortion- invariant correlation filters from a bank of such filters are then sequenced through the optical correlator for each input frame. The net result is the rapid preliminary recognition of one or a few targets.

Moynihan, Philip↗

Verification and Validation of NASA-Supported Enhancements to Decision Support Tools of PECAD

This section of the evaluation report summarizes the verification and validation (V&V) of recently implemented, NASA-supported enhancements to the decision support tools of the Production Estimates and Crop Assessment Division (PECAD). The implemented enhancements include operationally tailored Moderate Resolution Imaging Spectroradiometer (MODIS) products and products of the Global Reservoir and Lake Monitor (GRLM). The MODIS products are currently made available through two separate decision support tools: the MODIS Image Gallery and the U.S. Department of Agriculture (USDA) Foreign Agricultural Service (FAS) MODIS Normalized Difference Vegetation Index (NDVI) Database. Both the Global Reservoir and Lake Monitor and MODIS Image Gallery provide near-real-time products through PECAD's CropExplorer. This discussion addresses two areas: 1. Assessments of the standard NASA products on which these enhancements are based. 2. Characterizations of the performance of the new operational products.

Ross, Kenton W.↗

ISSARS Aerosol Database : an Incorporation of Atmospheric Particles into a Universal Tool to Simulate Remote Sensing Instruments

The Instrument Simulator Suite for Atmospheric Remote Sensing (ISSARS) entered its third and final year of development with an overall goal of providing a unified tool to simulate active and passive space borne atmospheric remote sensing instruments. These simulations focus on the atmosphere ranging from UV to microwaves. ISSARS handles all assumptions and uses various models on scattering and microphysics to fill the gaps left unspecified by the atmospheric models to create each instrument's measurements. This will help benefit mission design and reduce mission cost, create efficient implementation of multi-instrument/platform Observing System Simulation Experiments (OSSE), and improve existing models as well as new advanced models in development. In this effort, various aerosol particles are incorporated into the system, and a simulation of input wavelength and spectral refractive indices related to each spherical test particle(s) generate its scattering properties and phase functions. These atmospheric particles being integrated into the system comprise the ones observed by the Multi-angle Imaging SpectroRadiometer(MISR) and by the Multiangle SpectroPolarimetric Imager(MSPI). In addition, a complex scattering database generated by Prof. Ping Yang (Texas A&M) is also incorporated into this aerosol database. Future development with a radiative transfer code will generate a series of results that can be validated with results obtained by the MISR and MSPI instruments; nevertheless, test cases are simulated to determine the validity of various plugin libraries used to determine or gather the scattering properties of particles studied by MISR and MSPI, or within the Single-scattering properties of tri-axial ellipsoidal mineral dust particles database created by Prof. Ping Yang.

Instrument Simulator Suite for Atmospheric Remote ↗

LDEF meteoroid and debris database

The Long Duration Exposure Facility (LDEF) Meteoroid and Debris Special Investigation Group (M&D SIG) database is maintained at the Johnson Space Center (JSC), Houston, Texas, and consists of five data tables containing information about individual features, digitized images of selected features, and LDEF hardware (i.e., approximately 950 samples) archived at JSC. About 4000 penetrations (greater than 300 micron in diameter) and craters (greater than 500 micron in diameter) were identified and photodocumented during the disassembly of LDEF at the Kennedy Space Center (KSC), while an additional 4500 or so have subsequently been characterized at JSC. The database also contains some data that have been submitted by various PI's, yet the amount of such data is extremely limited in its extent, and investigators are encouraged to submit any and all M&D-type data to JSC for inclusion within the M&D database. Digitized stereo-image pairs are available for approximately 4500 features through the database.

Dardano, C. B.↗

SkICAT: A cataloging and analysis tool for wide field imaging surveys

We describe an integrated system, SkICAT (Sky Image Cataloging and Analysis Tool), for the automated reduction and analysis of the Palomar Observatory-ST ScI Digitized Sky Survey. The Survey will consist of the complete digitization of the photographic Second Palomar Observatory Sky Survey (POSS-II) in three bands, comprising nearly three Terabytes of pixel data. SkICAT applies a combination of existing packages, including FOCAS for basic image detection and measurement and SAS for database management, as well as custom software, to the task of managing this wealth of data. One of the most novel aspects of the system is its method of object classification. Using state-of-theart machine learning classification techniques (GID3* and O-BTree), we have developed a powerful method for automatically distinguishing point sources from non-point sources and artifacts, achieving comparably accurate discrimination a full magnitude fainter than in previous Schmidt plate surveys. The learning algorithms produce decision trees for classification by examining instances of objects classified by eye on both plate and higher quality CCD data. The same techniques will be applied to perform higher-level object classification (e.g., of galaxy morphology) in the near future. Another key feature of the system is the facility to integrate the catalogs from multiple plates (and portions thereof) to construct a single catalog of uniform calibration and quality down to the faintest limits of the survey. SkICAT also provides a variety of data analysis and exploration tools for the scientific utilization of the resulting catalogs. We include initial results of applying this system to measure the counts and distribution of galaxies in two bands down to Bj is approximately 21 mag over an approximate 70 square degree multi-plate field from POSS-II. SkICAT is constructed in a modular and general fashion and should be readily adaptable to other large-scale imaging surveys.

Weir, N.↗

Intercomparison of MODIS AQUA and VIIRS I-Band Fires and Emissions in an Agricultural Landscape - Implications for Air Pollution Research

Quantifying emissions from crop residue burning is crucial as it is a significant source of air pollution. In this study, we first compared the fire products from two different sensors, the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG) and Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km fire product (MCD14ML) in an agricultural landscape, Punjab, India. We then performed an intercomparison of three different approaches for estimating total particulate matter (TPM) emissions which includes the fire radiative power (FRP) based approach using VIIRS and MODIS data, the Global Fire Emissions Database (GFED) burnt area emissions and a bottom-up emissions approach involving agricultural census data. Results revealed that VIIRS detected fires were higher by a factor of 4.8 compared to MODIS Aqua and Terra sensors. Further, VIIRS detected fires were higher by a factor of 6.5 than Aqua. The mean monthly MODIS Aqua FRP was found to be higher than the VIIRS FRP; however, the sum of FRP from VIIRS was higher than MODIS data due to the large number of fires detected by the VIIRS. Besides, the VIIRS sum of FRP was 2.5 times more than the MODIS sum of FRP. MODIS and VIIRS monthly FRP data were found to be strongly correlated (r2 = 0.98). The bottom-up approach suggested TPM emissions in the range of 88.19-91.19 Gg compared to 42.0-61.71 Gg, 42.59-58.75 Gg and 93.98-111.72 Gg using the GFED, MODIS FRP, and VIIRS FRP based approaches, respectively. Of the different approaches, VIIRS FRP TPM emissions were highest. Since VIIRS data are only available since 2012 compared to MODIS Aqua data which have been available since May 2002, a prediction model combining MODIS and VIIRS FRP was derived to obtain potential TPM emissions from 2003-2016. The results suggested a range of 2.56-63.66 (Gg) TPM emissions per month, with the highest crop residue emissions during November of each year. Our results on TPM emissions for seasonality matched the ground-based data from the literature. As a mitigation option, stringent policy measures are recommended to curtail agricultural residue burning in the study area.

Global Fire Emissions Database (GFED)↗