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Report presents data derived from satellite images predicting pollution loads after rainfall. It explains method for converting Landsat images of Eastern United States into cover maps for Baltimore/five county region.
The effects of long-duration space exposure and launch environment on the performance of pyroelectric detectors which is important for the prediction of performance degradation, setting exposure limits, or determining shielding requirements was investigated. Air pollution monitoring and thermal mapping of the Earth, which includes the remote sensing of aerosols and limb scanning infrared radiometer projects, requires photodetection in the 6- to 20 micro m region of the spectrum. Pyroelectric detectors can detect radiation in the 1- to 100 micro m region while operating at room temperature. This makes tahe pyroelectric detector a prime candidate to fill the thermal infrared detector requirements.
The first centimeter-wave transition of the recently identified SiC2 radical has been observed in the envelope of the evolved carbon star IRC + 10216. The excellent agreement between the measured astronomical rest frequency and the predicted frequency, and the measured line intensity support the SiC2 identification. The high-resolution line profile and mapping data are used to estimate the size of the IRC + 10216 SiC2 envelope and the abundance of SiC2 relative to H2.
The Constellation-X Spectroscopy X-ray telescope (SXT) is designed to be built from X-ray optic segments. The X-ray segments will be fabricated from the segmented mandrels using a replication process. The purpose of the incoming metrology is to map the surface of the mandrels, so the performance of the X-ray optics produced can be predicted. Three Constellation-X segmented mandrels have been delivered to MSFC for incoming metrology. The segmented mandrels are 30-degree sections of a cylindrical surface and have diameters of 1.0 m, 1.2 m and 1.6 m. The maximum dimensions of the optical surface are 1.0 m axial length and 0.5 m azimuthal segment length. The metrology of the mandrels consists of the measurement of their slope differences, roundness, absolute radius, axial profile and microroughness. Accuracy goals for each type of measurement and the accuracy of the instruments used for the measurements will be discussed. The results of the mandrel metrology together with the performance predictions will be presented.
The HYDROS mission is under development by NASA as part of its Earth System Science Pathfinder program. HYDROS is designed to provide global maps of the Earth's soil moisture and freeze/thaw state every 2-3 days, for weather and climate prediction, water and carbon cycle studies, natural hazards monitoring, and national security applications.
The HYDROS mission is under development by NASA as part of its Earth System Science Pathfinder (ESSP) program. HYDROS is designed to provide global maps of the Earth's soil moisture and freezel/thaw state every 2-3 days, for weather and climate prediction, water and carbon cycle studies, natural hazards monitoring, and national security applications. HYDROS uses a unique active and passive L-band microwave system that optimizes measurement accuracy, spatial resolution, and coverage. It provides measurements in nearly all weather conditions, regardless of solar illumination. The designs of the radar and radiometer electronics, antenna feedhorn and reflector, and science data system, are driven by specific mission and science objectives. These objectives impose requirements on the frequencies, polarizations, sampling, spatial resolution, and accuracy of the system. In this paper we describe the HYDROS mission requirements, baseline design, and measurement capabilities.
The North Alabama Lightning Mapping Array NALMA has been collecting total lightning data on storms in the Tennessee Valley region since 2001. Forecasters from nearby National Weather Service (NWS) offices have been ingesting this data for display with other AWIPS products. The current lightning product used by the offices is the lightning source density plot. The new product provides a probabalistic, short-term, graphical forecast of the probability of lightning activity occurring at 5 min intervals over the next 30 minutes . One of the uses of the current lightning source density product by the Huntsville National Weather Service Office is to identify areas of potential for cloud-to-ground flashes based on where LMA total lightning is occurring. This product quantifies that observation. The Lightning Warning Product is derived from total lightning observations from the Washington, D.C. (DCLMA) and North Alabama Lightning Mapping Arrays and cloud-to-ground lightning flashes detected by the National Lightning Detection Network (NLDN). Probability predictions are provided for both intracloud and cloud-to-ground flashes. The gridded product can be displayed on AWIPS workstations in a manner similar to that of the lightning source density product.
NASA Earth science research utilizes both spaceborne and airborne real time observations in the planning and operations of its field campaigns. The coordination of air and space components is critical to achieve the goals and objectives and ensure the success of an experiment. Spaceborne imagery provides regular and continual coverage of the Earth and it is a significant component in all NASA field experiments. Real time visible and infrared geostationary images from GOES satellites and multi-spectral data from the many elements of the NASA suite of instruments aboard the TRMM, Terra, Aqua, Aura, and other NASA satellites have become norm. Similarly, the NASA Airborne Science Program draws upon a rich pool of instrumented aircraft. The NASA McDonnell Douglas DC-8, Lockheed P3 Orion, DeHavilland Twin Otter, King Air B200, Gulfstream-III are all staples of a NASA's well-stocked, versatile hangar. A key component in many field campaigns is coordinating the aircraft with satellite overpasses, other airplanes and the constantly evolving, dynamic weather conditions. Given the variables involved, developing a good flight plan that meets the objectives of the field experiment can be a challenging and time consuming task. Planning a research aircraft mission within the context of meeting the science objectives is complex task because it is much more than flying from point A to B. Flight plans typically consist of flying a series of transects or involve dynamic path changes when "chasing" a hurricane or forest fire. These aircraft flight plans are typically designed by the mission scientists then verified and implemented by the navigator or pilot. Flight planning can be an arduous task requiring frequent sanity checks by the flight crew. This requires real time situational awareness of the weather conditions that affect the aircraft track. Scientists at the University of Alabama-Huntsville and the NASA Marshall Space Flight Center developed the Waypoint Planning Tool, an interactive software tool, that enables scientists to develop their own flight plans (also known as waypoints) with point-and-click mouse capabilities on a digital map draped with real time satellite imagery. The Waypoint Planning Tool has further advanced to include satellite orbit predictions and seamlessly interfaces with the Real Time Mission Monitor which tracks the aircraft s position when the planes are flying. This presentation will describe the capabilities and features of the Waypoint Planning Tool highlighting the real time aspect, interactive nature and the resultant benefits to the airborne science community.
The Japanese Hayabusa II mission is planned to rendezvous with and return a sample from the near-Earth asteroid (162173) 1999 JU3. Previous mid-infrared studies have constrained the albedo and thermal properties of this object (Muller et al. 2011; Campins et al. 2011; Hasegawa et al. 2008). Visible wavelength spectra from 1999 and 2007 reveal a C-type asteroid that displays pronounced spectroscopic variability around 0.7 microns. Variability in the strength of a 0.7 micron band could be due to heterogeneous concentrations of iron-bearing phyllosilicates across its surface (Vilas 2008). We will present new observations from the favorable 2012 apparition to further characterize this object. In June of 2012 spectroscopic observations were conducted with the LDSS3 and FIRE instruments on the Magellan telescopes at Las Campanas Observatory in Chile. Between April and July of 2012 broadband visible-wavelength photometry was obtained with the Tenagra II telescope in Arizona and with the IMACS, Megacam and LDSS3 instruments on the Magellan telescopes. Our visible and near-infrared spectra confirm a C-type taxonomic classification, but do not show evidence for the presence of a 0.7 micron phyllosilicate band. Our time series of visible spectra cover approximately 60% of the rotational phases of 1999 JU3, but do not display any pronounced variability. We use our new optical light curves, combined with photometry from 2007, to refine a shape model of the asteroid. This shape model provides a means for mapping surface regions accessed by the spectroscopic observations from both 2007 and 2012, and thus to directly address the possibility of surface heterogeneity. This surface map, in conjunction with the newly measured photometric phase curve of the asteroid, will be used to make predictions regarding composition and surface properties that will ultimately be tested upon arrival of the Hayabusa II spacecraft.
A study outlining a fracture mechanics based model that is being developed to investigate crack growth and spallation of environmental barrier coating (EBC) under thermal cycling conditions is presented. A description of the current plan and a model to estimate thermal residual stresses in the coating and preliminary fracture mechanics concepts for studying crack growth in the coating are also discussed. A road map for modeling life and durability of the EBC and the results of FEA model(s) developed for predicting thermal residual stresses and the cracking behavior of the coating are generated and described. Further initial assessment and preliminary results showed that developing a comprehensive EBC life prediction model incorporating EBC cracking, degradation and spalling mechanism under stress and temperature gradients typically seen in turbine components is difficult. This is basically due to mismatch in thermal expansion difference between sub-layers of EBC as well as between EBC and substrate, diffusion of moisture and oxygen though the coating, and densification of the coating during operating conditions as well as due to foreign object damage, the EBC can also crack and spall from the substrate causing oxidation and recession and reducing the design life of the EBC coated substrate.
NASA's Short‐term Prediction Research and Transition Center (SPoRT) has been transitioning real‐time total lightning observations from ground‐based lightning mapping arrays since 2003. This initial effort was with the local Weather Forecast Offices (WFO) that could use the North Alabama Lightning Mapping Array (NALMA). These early collaborations established a strong interest in the use of total lightning for WFO operations. In particular the focus started with warning decision support, but has since expanded to include impact‐based decision support and lightning safety. SPoRT has used its experience to establish connections with new lightning mapping arrays as they become available. The GOES‐R / JPSS Visiting Scientist Program has enabled SPoRT to conduct visits to new partners and expand the number of operational users with access to total lightning observations. In early 2014, SPoRT conducted the most recent visiting scientist trips to meet with forecast offices that will used the Colorado, Houston, and Langmuir Lab (New Mexico) lightning mapping arrays. In addition, SPoRT met with the corresponding Center Weather Service Units (CWSUs) to expand collaborations with the aviation community. These visits were an opportunity to learn about the forecast needs of each office visited as well as to provide on‐site training for the use of total lightning, setting the stage for a real‐time assessment during May‐July 2014. With five lightning mapping arrays covering multiple geographic locations, the 2014 assessment has demonstrated numerous uses of total lightning in varying situations. Several highlights include a much broader use of total lightning for impact‐based decision support ranging from airport weather warnings, supporting fire crews, and protecting large outdoor events. The inclusion of the CWSUs has broadened the operational scope of total lightning, demonstrating how these data can support air traffic management, particularly in the Terminal Radar Approach Control Facilities (TRACON) region around an airport. These collaborations continue to demonstrate, from the operational perspective, the utility of total lightning and the importance of continued training and preparation in advance of the Geostationary Lightning Mapper.
In this study, we demonstrate skillful spring forecasts of detrended September Arctic sea ice extent using passive microwave observations of sea ice concentration (SIC) and melt onset (MO). We compare these to forecasts produced using data from a sophisticated melt pond model, and find similar to higher skill values, where the forecast skill is calculated relative to linear trend persistence. The MO forecasts shows the highest skill in March-May, while the SIC forecasts produce the highest skill in June-August, especially when the forecasts are evaluated over recent years (since 2008). The high MO forecast skill in early spring appears to be driven primarily by the presence and timing of open water anomalies, while the high SIC forecast skill appears to be driven by both open water and surface melt processes. Spatial maps of detrended anomalies highlight the drivers of the different forecasts, and enable us to understand regions of predictive importance. Correctly capturing sea ice state anomalies, along with changes in open water coverage appear to be key processes in skillfully forecasting summer Arctic sea ice.
The need to identify the presence and quantify the concentrations of gases and vapors is ubiquitous in NASA missions and societal applications. Sensors for air quality monitoring in crew cabins and ISS have been actively under development (Ref. 1). In particular, measuring the concentration of CO2 and NH3 is important because high concentrations of these gases pose a risk to ISS crew health. Detection of fuel and oxidant leaks in crew vehicles is critical for ensuring mission safety. Accurate gas and vapor concentrations can be measured, but this typically requires bulky and expensive instrumentation. Recently, inexpensive sensors with low power demands have been fabricated for use on the International Space Station (ISS). Carbon Nanotube (CNT) based chemical sensors are one type of these sensors. CNT sensors meet the requirements for low cost and ease of fabrication for deployment on the ISS. However, converting the measured signal from the sensors to human readable indicators of atmospheric air quality and safety is challenging. This is because it is difficult to develop an analytical model that maps the CNT sensor output signal to gas concentration. Training a neural network on CNT sensor data to predict gas concentration is more effective than developing an analytic approach to calculate the concentration from the same data set. With this in mind a neural network was created to tackle this challenge of converting the measured signal into CO2 and NH3 concentration values.
The SARS-CoV-2 nucleocapsid (N) protein is highly immunogenic, and anti-N antibodies are commonly used as markers for prior infection. While several studies have examined or predicted the antigenic regions of N, these have lacked consensus and structural context. Using COVID-19 patient sera to probe an overlapping peptide array, we identified six public and four private epitope regions across N, some of which are unique to this study. We further report the first deposited X-ray structure of the stable dimerization domain at 2.05 Å as similar to all other reported structures. Structural mapping revealed that most epitopes are derived from surface-exposed loops on the stable domains or from the unstructured linker regions. An antibody response to an epitope in the stable RNA binding domain was found more frequently in sera from patients requiring intensive care. Since emerging amino acid variations in N map to immunogenic peptides, N protein variation could impact detection of seroconversion for variants of concern.
ct: PV service lifetime prediction (SLP) enables accurate calculation of levelized cost of energy (LCOE), which is crucial to rationalizing PV investment and installation. However, SLP is challeging since PV reliability in the field is affected by many combined factors, including various environmental stresses and module quality. In order to map out the active degradation mechanisms and pathways that best resemble real world conditions, we introduce the framework of a study protocol and use network models fitted to data, to enable analysis and SLP of complex PV systems with multiple active degradation mechanisms. The study protocol is the experimental design, including module variants and different exposure conditions, selection of evaluation methods, time-series data acquisition and training of network models to these data. We present SLP of minimodules in the lab and PV systems in the field. For lab SLP, minimodules with 8 variants based on manufacturer, architecture, and encapsulation were prepared and aged in modified damp heat with or without full spectrum light exposure. Stepwise I-V and Suns-Voc data acquisition tracks changes in electrical properties including Rs,IV, Isc,IV, Vmp,PIV providing insights into power loss of minimodules. Network structural equation modeling (netSEM) was utilized to construct degradation pathway models that identify active degradation mechanisms and predict power loss over time. For field SLP, datastreams of Pmp values and I-V curve datastreams of two types of modules installed in three distinctly different Köppen-Geiger climate zones for 9 years were acquired. With power loss modes corresponding to uniform current loss (ΔPIsc), recombination (ΔPVoc), series resistance (ΔPRs), and current mismatch (ΔPImis) determined, the performance loss rates (PLR) were determined using PVplr. We show how to establish a study protocol framework to ensure appropriate parametric variations and valid data collection from the variants of your complex systems. Then the data-driven netSEM model fitting provides a comprehensive mapping of multiple active degradation mechanisms, and accurate service life prediction.
One hundred twenty seven hours of high-resolution H alpha movies of young active regions have been compared with simultaneous 1.5-15 keV X-ray measurements from the Mapping X-Ray Heliometer experiment on OSO-8, with particular attention to preflare periods and to the possibility of X-ray emission associated with filament activity during that time. The period studied included eight confirmed flares or subflares, 16 unreported events of comparable magnitude, and numerous examples of filament activity. No evidence for X-ray emission from areas of enhanced filament activity was found unless simultaneous brightenings were present in H alpha. In addition, no peculiar behavior of either filaments or X-rays was detected during the period of approximately 20 min preceding these small flares, which, even in retrospect, would have allowed them to be 'predicted'.
Our article introduces a universal predictive framework for point defect formation energies and charge transition levels in a wide chemical space of zinc blende semiconductors and possible impurity atoms selected from across the periodic table. This framework was developed by leveraging high-throughput quantum mechanical simulations benchmarked using some experimental data from the literature, as well as machine learning (ML)-based regressions techniques that map unique materials descriptors to computed defect properties and yield optimized and generalizable models. Furthermore, the power and utility of these models is revealed through quick predictions for thousands of new defects and screening of low-energy impurities, which may tune the equilibrium conductivity in the semiconductor. This work presents, to our knowledge, the largest density functional theory (DFT) dataset of defect properties in semiconductors and the largest DFT+ML-based screening of point defects in semiconductors to date.
The San Francisco Bay Delta is a large hydrodynamic complex that incorporates the Sacramento and San Joaquin Estuaries, the Burman Marsh, and the San Francisco Bay proper. Competition exists for the use of this extensive water system both from the fisheries industry, the agricultural industry, and from the marine and estuarine animal species within the Delta. As tidal fluctuations occur, more saline water pushes upstream allowing fish to migrate beyond the Burman Marsh for breeding and habitat occupation. However, the agriculture industry does not want extensive salinity intrusion to impact water quality for human and plant consumption. The balance is regulated by pumping stations located alone the estuaries and reservoirs whereby flushing of fresh water keeps the saline intrusion at bay. The pumping schedule is driven by data collected at various locations within the Bay Delta and by numerical models that predict the salinity intrusion as part of a larger model of the system. The Interagency Ecological Program (IEP) for the San Francisco Bay/Sacramento-San Joaquin Estuary collects, monitors, and archives the data, and the Department of Water Resources provides a numerical model simulation (DSM2) from which predictions are made that drive the pumping schedule. A problem with this procedure is that the numerical simulation takes roughly 16 hours to complete a C:~ prediction. We have created a neural net, optimized with a genetic algorithm, that takes as input the archived data from multiple stations and predicts stage, salinity, and flow at the Carquinez Straits (at the downstream end of the Burman Marsh). This model seems to be robust in its predictions and operates much faster than the current numerical DSM2 model. Because the system is strongly tidal driven, we used both Principal Component Analysis and Fast Fourier Transforms to discover dominant features within the IEP data. We then filtered out the dominant tidal forcing to discover non-primary tidal effects, and used this to enhance the neural network by mapping input-output relationships in a more efficient manner. Furthermore, the neural network implicitly incorporates both the hydrodynamic and water quality models into a single predictive system. Although our model has not yet been enhanced to demonstrate improve pumping schedules, it has the possibility to support better decision-making procedures that may then be implemented by State agencies if desired. Our intention is now to use this model in the smaller Elkhorn Slough complex near Monterey Bay where no such hydrodynamic model currently exists. At the Elkhorn Slough, we are fusing the neural net model of tidally-driven flow with in situ flow data and airborne and satellite remote sensation data. These further constrain the behavior of the model in predicting the longer-term health and future of this vital estuary.