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NASA NTRS2016

Thermodynamic Spectrum of Solar Flares Based on SDO/EVE Observations: Techniques and First Results

The Solar Dynamics Observatory (SDO)/EUV Variability Experiment (EVE) provides rich information on the thermodynamic processes of solar activities, particularly on solar flares. Here, we develop a method to construct thermodynamic spectrum (TDS) charts based on the EVE spectral lines. This tool could potentially be useful for extreme ultraviolet (EUV) astronomy to learn about the eruptive activities on distant astronomical objects. Through several cases, we illustrate what we can learn from the TDS charts. Furthermore, we apply the TDS method to 74 flares equal to or greater than the M5.0 class, and reach the following statistical results. First, EUV peaks are always behind the soft X-ray (SXR) peaks and stronger flares tend to have faster cooling rates. There is a power-law correlation between the peak delay times and the cooling rates, suggesting a coherent cooling process of flares from SXR to EUV emissions. Second, there are two distinct temperature drift patterns, called Type I and Type II. For Type I flares, the enhanced emission drifts from high to low temperature like a quadrilateral, whereas for Type II flares the drift pattern looks like a triangle. Statistical analysis suggests that Type II flares are more impulsive than Type I flares. Third, for late-phase flares, the peak intensity ratio of the late phase to the main phase is roughly correlated with the flare class, and the flares with a strong late phase are all confined. We believe that the re-deposition of the energy carried by a flux rope, which unsuccessfully erupts out, into thermal emissions is responsible for the strong late phase found in a confined flare. Furthermore, we show the signatures of the flare thermodynamic process in the chromosphere and transition region in the TDS charts. These results provide new clues to advance our understanding of the thermodynamic processes of solar flares and associated solar eruptions, e.g., coronal mass ejections.

Wang, Yuming↗

NASA NTRS1979

Inflight fuel tank temperature survey data

Statistical summaries of the fuel and air temperature data for twelve different routes and for different aircraft models (B747, B707, DC-10 and DC-8), are given. The minimum fuel, total air and static air temperature expected for a 0.3% probability were summarized in table form. Minimum fuel temperature extremes agreed with calculated predictions and the minimum fuel temperature did not necessarily equal the minimum total air temperature even for extreme weather, long range flights.

Pasion, A. J.↗

NASA NTRS2010

Non-Geiger-Mode Single-Photon Avalanche Detector with Low Excess Noise

This design constitutes a self-resetting (gain quenching), room-temperature operational semiconductor single-photon-sensitive detector that is sensitive to telecommunications optical wavelengths and is scalable to large areas (millimeter diameter) with high bandwidth and efficiencies. The device can detect single photons at a 1,550-nm wavelength at a gain of 1 x 10(exp 6). Unlike conventional single photon avalanche detectors (SPADs), where gain is an extremely sensitive function to the bias voltage, the multiplication gain of this device is stable at 1 x 10(exp 6) over a wide range of bias from 30.2 to 30.9 V. Here, the multiplication gain is defined as the total number of charge carriers contained in one output pulse that is triggered by the absorption of a single photon. The statistics of magnitude of output signals also shows that the device has a very narrow pulse height distribution, which demonstrates a greatly suppressed gain fluctuation. From the histograms of both pulse height and pulse charge, the equivalent gain variance (excess noise) is between 1.001 and 1.007 at a gain of 1 x 10(exp 6). With these advantages, the device holds promise to function as a PMT-like photon counter at a 1,550- nm wavelength. The epitaxial layer structure of the device allows photons to be absorbed in the InGaAs layer, generating electron/hole (e-h) pairs. Driven by an electrical field in InGaAs, electrons are collected at the anode while holes reach the multiplication region (InAlAs p-i-n structure) and trigger the avalanche process. As a result, a large number of e-h pairs are created, and the holes move toward the cathode. Holes created by the avalanche process gain large kinetic energy through the electric field, and are considered hot. These hot holes are cooled as they travel across a p -InAlAs low field region, and are eventually blocked by energy barriers formed by the InGaAsP/ InAlAs heterojunctions. The composition of the InGaAsP alloy was chosen to have an 80 meV valance band offset with InAlAs, which is high enough to hinder the transport of the already cooled holes. Being stopped by the energy barrier, holes are accumulated at the junctions to shield the electric field, resulting in a decrease of the electric field in the multiplication region. Because the impact ionization rate is extremely sensitive to the magnitude of the electric field, the field-screening effect drastically reduces the impact ionization rate and quenches the output signals. After the avalanche pulse signal is self-quenched, the accumulated holes at the InGaAsP/ InAlAs interface escape the energy barrier through thermal excitation and tunneling and finally leave the device. The device is thus reset and ready for subsequent photon detection. This recovery time is controlled by the height of the energy barrier and the hole-cooling rate.

Zhao, Kai↗

NASA NTRS2018

Flood Hazard Assessment from Storm Tides, Rain and Sea Level Rise for a Tidal River Estuary

Cities and towns along the tidal Hudson River are highly vulnerable to flooding through the combination of storm tides and high streamflows, compounded by sea level rise. Here a three-dimensional hydrodynamic model, validated by comparing peak water levels for 76 historical storms, is applied in a probabilistic flood hazard assessment. In simulations, the model merges streamflows and storm tides from tropical cyclones (TCs), offshore extratropical cyclones (ETCs) and inland "wet extratropical" cyclones (WETCs). The climatology of possible ETC and WETC storm events is represented by historical events (1931-2013), and simulations include gauged streamflows and inferred ungauged streamflows (based on watershed area) for the Hudson River and its tributaries. The TC climatology is created using a stochastic statistical model to represent a wider range of storms than is contained in the historical record. TC streamflow hydrographs are simulated for tributaries spaced along the Hudson, modeled as a function of TC attributes (storm track, sea surface temperature, maximum wind speed) using a statistical Bayesian approach. Results show WETCs are important to flood risk in the upper tidal river (e.g., Albany, New York), ETCs are important in the estuary (e.g., New York City) and lower tidal river, and TCs are important at all locations due to their potential for both high surge and extreme rainfall. The raising of floods by sea level rise is shown to be reduced by approximately 30-60 percent at Albany due to the dominance of streamflow for flood risk. This can be explained with simple channel flow dynamics, in which increased depth throughout the river reduces frictional resistance, thereby reducing the water level slope and the upriver water level.

Tidal river↗

NASA NTRS1974

STANSORT - Stanford Remote Sensing Laboratory pattern recognition and classification system

The principal barrier to routine use of the ERTS multispectral scanner computer compatible tapes, rather than photointerpretation examination of the images, has been the high computing costs involved due to the large quantity of information (4 Mbytes) contained in a scene. STANSORT, the interactive program package developed at Stanford Remote Sensing Laboratories alleviates this problem, providing an extremely rapid, flexible and low cost tool for data reduction, scene classification, species searches and edge detection. The primary classification procedure, utilizing a search with variable gate widths, for similarities in the normalized, digitized spectra is described along with associated procedures for data refinement and extraction of information. The more rigorous statistical classification procedures are also explained.

Honey, F. R.↗

NASA NTRS2008

Generation of Boundary Manikin Anthropometry

The purpose of this study was to develop 3D digital boundary manikins that are representative of the anthropometry of a unique population. These digital manikins can be used by designers to verify and validate that the components of the spacesuit design satisfy the requirements specified in the Human Systems Integration Requirements (HSIR) document. Currently, the HSIR requires the suit to accommodate the 1st percentile American female to the 99th percentile American male. The manikin anthropometry was derived using two methods: Principal Component Analysis (PCA) and Whole Body Posture Based Analysis (WBPBA). PCA is a statistical method for reducing a multidimensional data set by using eigenvectors and eigenvalues. The goal is to create a reduced data set that encapsulates the majority of the variation in the population. WBPBA is a multivariate analytical approach that was developed by the Anthropometry and Biomechanics Facility (ABF) to identify the extremes of the population for a given body posture. WBPBA is a simulation-based method that finds extremes in a population based on anthropometry and posture whereas PCA is based solely on anthropometry. Both methods yield a list of subjects and their anthropometry from the target population; PCA resulted in 20 female and 22 male subjects anthropometry and WBPBA resulted in 7 subjects' anthropometry representing the extreme subjects in the target population. The subjects anthropometry is then used to 'morph' a baseline digital scan of a person with the same body type to create a 3D digital model that can be used as a tool for designers, the details of which will be discussed in subsequent papers.

Young, Karen S.↗

NASA NTRS1972

Jimsphere wind and turbulence exceedance statistic

Exceedance statistics of winds and gusts observed over Cape Kennedy with Jimsphere balloon sensors are described. Gust profiles containing positive and negative departures, from smoothed profiles, in the wavelength ranges 100-2500, 100-1900, 100-860, and 100-460 meters were computed from 1578 profiles with four 41 weight digital high pass filters. Extreme values of the square root of gust speed are normally distributed. Monthly and annual exceedance probability distributions of normalized rms gust speeds in three altitude bands (2-7, 6-11, and 9-14 km) are log-normal. The rms gust speeds are largest in the 100-2500 wavelength band between 9 and 14 km in late winter and early spring. A study of monthly and annual exceedance probabilities and the number of occurrences per kilometer of level crossings with positive slope indicates significant variability with season, altitude, and filter configuration. A decile sampling scheme is tested and an optimum approach is suggested for drawing a relatively small random sample that represents the characteristic extreme wind speeds and shears of a large parent population of Jimsphere wind profiles.

Adelfang, S. I.↗

NASA NTRS2014

Injury Surveillance Among NASA Astronauts Using the Barell Injury Diagnosis Matrix

Astronauts perform physically demanding tasks and risk incurring musculoskeletal injuries during both groundbased training and missions. Increased injury rates throughout the history of the U.S. space program have been attributed to numerous factors, including an aging astronaut corps, increased Weightless Environment Training Facility (WETF) and Neutral Buoyancy Laboratory (NBL) training to construct the International Space Station, and improved clinical operations that promote injury prevention and reporting. With NASA program changes through the years (including retirement of the Shuttle program) and an improved training environment (including a new astronaut gym), there is no surveillance program to systematically track injury rates. A limited number of research projects have been conducted over the past 20 years to evaluate musculoskeletal injuries: (1) to evaluate orthopedic injuries from 1987 to 1995, (2) to describe upper extremity injuries, (3) to evaluate EVA spacesuit training related injuries, and (4) to evaluate in-flight musculoskeletal injuries. Nevertheless, there has been no consistently performed comprehensive assessment of musculoskeletal injuries among astronauts. The Barell Injury Diagnosis Matrix was introduced at the 2001 meeting of the International Collaborative Effort (ICE) on Injury Statistics. The Matrix proposes a standardized method of classifying body region by nature of injury. Diagnoses are coded using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) coding system. The purpose of this study is to assess the usefulness and complexity of the Barell Injury Diagnosis Matrix to classify and track musculoskeletal injuries among NASA astronauts.

Murray, J. D.↗

NASA NTRSDate not supplied

Spacesuit Reach Capability and Work Volume Assessment

Work volumes and reach envelopes define the locations where work objects or interfaces can be placed around a person to reduce ergonomic risk. Spacesuits, however, show unique reach patterns, due to the stiffness and mechanical constraints from pressurization. This study measured the reach motion patterns and capabilities from subjects wearing a pressurized spacesuit using a 3D motion capture system. The subjects performed arm and hand motions, focusing on extreme lateral and vertical positions. These motions defined the maximum ranges. They also performed on-suit reaches to measure where they could or couldn’t reach on the suit surface to determine where critical controls and devices could be placed on the suit. A parametric model of the reach volume was statistically built using the collected hand position data. The observed shape and size of the work volume substantially vary by persons, depending on anthropometry and strength capability. It was also indicated that the traditional metrics (e.g., simple spherical or cylindrical representations) estimated from “unsuited” persons or computer simulations may not accurately capture such variations. The results of this study can help to design and optimize space hardware and task activities, to protect crewmembers from overexertion or injuries.

Han Kim↗

NASA NTRS1989

Cloud field classification based on textural features

An essential component in global climate research is accurate cloud cover and type determination. Of the two approaches to texture-based classification (statistical and textural), only the former is effective in the classification of natural scenes such as land, ocean, and atmosphere. In the statistical approach that was adopted, parameters characterizing the stochastic properties of the spatial distribution of grey levels in an image are estimated and then used as features for cloud classification. Two types of textural measures were used. One is based on the distribution of the grey level difference vector (GLDV), and the other on a set of textural features derived from the MaxMin cooccurrence matrix (MMCM). The GLDV method looks at the difference D of grey levels at pixels separated by a horizontal distance d and computes several statistics based on this distribution. These are then used as features in subsequent classification. The MaxMin tectural features on the other hand are based on the MMCM, a matrix whose (I,J)th entry give the relative frequency of occurrences of the grey level pair (I,J) that are consecutive and thresholded local extremes separated by a given pixel distance d. Textural measures are then computed based on this matrix in much the same manner as is done in texture computation using the grey level cooccurrence matrix. The database consists of 37 cloud field scenes from LANDSAT imagery using a near IR visible channel. The classification algorithm used is the well known Stepwise Discriminant Analysis. The overall accuracy was estimated by the percentage or correct classifications in each case. It turns out that both types of classifiers, at their best combination of features, and at any given spatial resolution give approximately the same classification accuracy. A neural network based classifier with a feed forward architecture and a back propagation training algorithm is used to increase the classification accuracy, using these two classes of features. Preliminary results based on the GLDV textural features alone look promising.

Sengupta, Sailes Kumar↗

NASA NTRS2005

An Ensemble Approach to Building Mercer Kernels with Prior Information

This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly dimensional feature space. we describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using pre-defined kernels. These data adaptive kernels can encode prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. Specifically, we demonstrate the use of the algorithm in situations with extremely small samples of data. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS) and demonstrate the method's superior performance against standard methods. The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains templates for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic-algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code.

Srivastava, Ashok N.↗

NASA NTRS2011

Autonomous, In-Flight Crew Health Risk Management for Exploration-Class Missions: Leveraging the Integrated Medical Model for the Exploration Medical System Demonstration Project

The Integrated Medical Model (IMM) captures organizational knowledge across the space medicine, training, operations, engineering, and research domains. IMM uses this knowledge in the context of a mission and crew profile to forecast risks to crew health and mission success. The IMM establishes a quantified, statistical relationship among medical conditions, risk factors, available medical resources, and crew health and mission outcomes. These relationships may provide an appropriate foundation for developing an in-flight medical decision support tool that helps optimize the use of medical resources and assists in overall crew health management by an autonomous crew with extremely limited interactions with ground support personnel and no chance of resupply.

Butler, D. J.↗

NASA NTRS2017

Measuring Surface Bulk Elemental Composition on Venus

The extreme surface environment (462 C, 93 bars pressure) of Venus makes subsurface measurements of its bulk elemental composition extremely challenging. Instruments landed on the surface of Venus must be enclosed in a pressure vessel. The high surface temperatures also require a thermal control system to keep the instrumentation temperatures within their operational range for as long as possible. Since Venus surface probes can currently operate for only a few hours, it is crucial that the lander instrumentation be able to make statistically significant measurements in a short time. An instrument is described that can achieve such a measurement over a volume of thousands of cubic centimeters of material by using high energy penetrating neutron and gamma radiation. The instrument consists of a Pulsed Neutron Generator (PNG) and a Gamma-Ray Spectrometer (GRS). The PNG emits isotropic pulses of 14.1 MeV neutrons that penetrate the pressure vessel walls, the dense atmosphere and the surface rock. The neutrons induce nuclear reactions in the rock to produce gamma rays with energies specific to the element and nuclear process involved. Thus the energies of the detected gamma rays identify the elements present and their intensities provide the abundance of each element. The GRS spectra are analyzed to determine the Venus elemental composition from the spectral signature of individual major, minor, and trace radioactive elements. As a test of such an instrument, a Schlumberger Litho Scanner oil well logging tool was used in a series of experiments at NASA's Goddard Space Flight Center. The Litho Scanner tool was mounted above large (1.8 m x 1.8 m x.9 m) granite and basalt monuments and made a series of one-hour elemental composition measurements in a planar geometry more similar to a planetary lander measurement. Initial analysis of the results shows good agreement with target elemental assays

Venus↗

NASA NTRS2009

Antarctica Meta-Analysis: Psychosocial Factors Related to Long Duration Isolation and Confinement

This meta-analysis is examining the psychological effects of wintering-over in Antarctica. As an isolated, confined, and extreme (ICE) environment, Antarctica provides invaluable opportunities to experience stressors more common to spaceflight than to the average person s everyday life. Increased prevalence of psychological symptoms, syndromes, and psychiatric disorders, as well as positive effects, are expected to be associated with various demographic and environmental factors. Implications for spaceflight are discussed. Findings from statistical review of the Antarctic articles will be shared.

Leveton, Lauren↗

NASA NTRS2020

Assessment of Extremes in Global Precipitation Products: How Reliable Are They?

Global gridded precipitation products have proven essential for many applications ranging from hydrological modeling and climate model validation to natural hazard risk assessment. They provide a global picture of how precipitation varies across time and space, specifically in regions where ground-based observations are scarce. While the application of global precipitation products has become widespread, there is limited knowledge on how well these products represent the magnitude and frequency of extreme precipitation—the key features in triggering flood hazards. Here, five global precipitation datasets (MSWEP, CFSR, CPC, PERSIANN-CDR, and WFDEI) are compared to each other and to surface observations. The spatial variability of relatively high precipitation events (tail heaviness) and the resulting discrepancy among datasets in the predicted precipitation return levels were evaluated for the time period 1979–2017. The analysis shows that 1) these products do not provide a consistent representation of the behavior of extremes as quantified by the tail heaviness, 2) there is strong spatial variability in the tail index, 3) the spatial patterns of the tail heaviness generally match the Köppen–Geiger climate classification, and 4) the predicted return levels for 100 and 1000 years differ significantly among the gridded products. More generally, our findings reveal shortcomings of global precipitation products in representing extremes and highlight that there is no single global product that performs best for all regions and climates.

Risk assessment↗

NASA NTRS2015

Relationships Between Excessive Heat and Daily Mortality over the Coterminous U.S

In the United States, extreme heat is the most deadly weather-related hazard. In the face of a warming climate and urbanization, it is very likely that extreme heat events (EHEs) will become more common and more severe in the U.S. Using National Land Data Assimilation System (NLDAS) meteorological reanalysis data, we have developed several measures of extreme heat to enable assessments of the impacts of heat on public health over the coterminous U.S. These measures include daily maximum and minimum air temperatures, daily maximum heat indices and a new heat stress variable called Net Daily Heat Stress (NDHS) that gives an integrated measure of heat stress (and relief) over the course of a day. All output has been created on the NLDAS 1/8 degree (approximately 12 km) grid and aggregated to the county level, which is the preferred geographic scale of analysis for public health researchers. County-level statistics have been made available through the Centers for Disease Control and Prevention (CDC) via the Wide-ranging Online Data for Epidemiologic Research (WONDER) system. We have examined the relationship between excessive heat events, as defined in eight different ways from the various daily heat metrics, and heat-related and all-cause mortality defined in CDC's National Center for Health Statistics 'Multiple Causes of Death 1999-2010' dataset. To do this, we linked daily, county-level heat mortality counts with EHE occurrence based on each of the eight EHE definitions by region and nationally for the period 1999-2010. The objectives of this analysis are to determine (1) whether heat-related deaths can be clearly tied to excessive heat events, (2) what time lags are critical for predicting heat-related deaths, and (3) which of the heat metrics correlates best with mortality in each US region. Results show large regional differences in the correlations between heat and mortality. Also, the heat metric that provides the best indicator of mortality varied by region. Results from this research will potentially lead to improvements in our ability to anticipate and mitigate any significant impacts of extreme heat events on health.

heat↗

NASA NTRS2012

Sensitivity of Marine Warm Cloud Retrieval Statistics to Algorithm Choices: Examples from MODIS Collection 6

The optical and microphysical structure of warm boundary layer marine clouds is of fundamental importance for understanding a variety of cloud radiation and precipitation processes. With the advent of MODIS (Moderate Resolution Imaging Spectroradiometer) on the NASA EOS Terra and Aqua platforms, simultaneous global/daily 1km retrievals of cloud optical thickness and effective particle size are provided, as well as the derived water path. In addition, the cloud product (MOD06/MYD06 for MODIS Terra and Aqua, respectively) provides separate effective radii results using the l.6, 2.1, and 3.7 ~m spectral channels. Cloud retrieval statistics are highly sensitive to how a pixel identified as being "notclear" by a cloud mask (e.g., the MOD35/MYD35 product) is determined to be useful for an optical retrieval based on a 1-D cloud model. The Collection 5 MODIS retrieval algorithm removed pixels associated with cloud'edges as well as ocean pixels with partly cloudy elements in the 250m MODIS cloud mask - part of the so-called Clear Sky Restoral (CSR) algorithm. Collection 6 attempts retrievals for those two pixel populations, but allows a user to isolate or filter out the populations via CSR pixel-level Quality Assessment (QA) assignments. In this paper, using the preliminary Collection 6 MOD06 product, we present global and regional statistical results of marine warm cloud retrieval sensitivities to the cloud edge and 250m partly cloudy pixel populations. As expected, retrievals for these pixels are generally consistent with a breakdown of the ID cloud model. While optical thickness for these suspect pixel populations may have some utility for radiative studies, the retrievals should be used with extreme caution for process and microphysical studies.

Platnick, Steven↗

NASA NTRS2017

Classifying Multi-Model Wheat Yield Impact Response Surfaces Showing Sensitivity to Temperature and Precipitation Change

Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (minus 2 to plus 9 degrees Centigrade) and precipitation (minus 50 to plus 50 percent). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern. The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index. Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities.

Classification; Climate change; Crop model; Ensemb↗