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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Kepler

The NASA Kepler mission recently announced over 1200 exoplanet candidates. While some are common Hot Jupiters, a large number are Neptune size and smaller, transit depths suggest sizes down to the radius of Earth. The Kepler project has a fairly high confidence that most of these candidates are real exoplanets. Many analysis steps and lessons learned from Kepler light curves are used during the vetting process. This talk will cover some new results in the areas of stellar variability, solar systems with multiple planets, and how transit-like signatures are vetted for false positives, especially those indicative of small planets.

Howell, Steve B.↗
NASA NTRS2014

Abiotic Ozone and Oxygen in Atmospheres Similar to Prebiotic Earth

The search for life on planets outside our solar system will use spectroscopic identification of atmospheric biosignatures. The most robust remotely detectable potential biosignature is considered to be the detection of oxygen (O 2 ) or ozone (O 3 ) simultaneous to methane (CH 4 ) at levels indicating fluxes from the planetary surface in excess of those that could be produced abiotically. Here we use an altitude-dependent photochemical model with the enhanced lower boundary conditions necessary to carefully explore abiotic O 2 and O 3 production on lifeless planets with a wide variety of volcanic gas fluxes and stellar energy distributions. On some of these worlds, we predict limited O 2 and O 3 buildup, caused by fast chemical production of these gases. This results in detectable abiotic O 3 and CH 4 features in the UV-visible, but no detectable abiotic O 2 features. Thus, simultaneous detection of O 3 and CH 4 by a UV-visible mission is not a strong biosignature without proper contextual information. Discrimination between biological and abiotic sources of O 2 and O 3 is possible through analysis of the stellar and atmospheric context-particularly redox state and O atom inventory-of the planet in question. Specifically, understanding the spectral characteristics of the star and obtaining a broad wavelength range for planetary spectra should allow more robust identification of false positives for life. This highlights the importance of wide spectral coverage for future exoplanet characterization missions. Specifically, discrimination between true and false positives may require spectral observations that extend into infrared wavelengths and provide contextual information on the planet's atmospheric chemistry.

planets and satellites↗
NASA NTRS2021

Cloud Detection over Sunglint Regions with Observations from the Earth Polychromatic Imaging Camera (EPIC)

With the ability to observe the entire sunlit side of the Earth, EPIC data have become an important resource for studying cloud daily variability. Inaccurate cloud masking is a great source of uncertainty. One main region that is prone to error in cloud masking is the sunglint area over ocean surfaces. Cloud detection over these regions is challenging for the EPIC instrument because of its limited spectral channels. Clear sky ocean surface reflectance from visible channels over sunglint is much larger than that over the non-glint areas and can exceed reflectance from thin clouds. This paper presents an improved EPIC ocean cloud masking algorithm (Version 3). Over sunglint regions (glint angle ≤ 25°), the algorithm utilizes EPIC’s oxygen (O2) A-band ratio (764nm to 780 nm) in addition to the 780 nm reflectance observations in masking tests. Outside the sunglint regions, a dynamic reflectance threshold for the Rayleigh corrected 780 nm reflectance is applied. The thresholds are derived as a function of glint angle. When compared with co-located data from the geosynchronous Earth orbit (GEO) and the low Earth orbit (LEO) observations, the consistency of the new ocean cloud mask algorithm has increased by 4~10% and 4~6% in the glint center and granule edges respectively. The false positive rate is reduced by 10~17%. Overall global ocean cloud detection consistency increases by 2%. This algorithm, along with other improvements to the EPIC cloud masks, has been implemented in the EPIC cloud products Version 3. This algorithm will improve the cloud daily variability analysis by removing the artificial peak at local noon time in the glint center latitudes and reducing biases in the early morning and late afternoon cloud fraction over ocean surfaces.

EPIC↗
NASA NTRS2004

Life Detection on the Early Earth

Finding evidence for first the existence, and then the nature of life on the early Earth or early Mars requires both the recognition of subtle biosignatures and the elimination of false positives. The history of the search for fossils in increasingly older Precambrian strata illustrates these difficulties very clearly, and new observational and theoretical approaches are both needed and being developed. At the microscopic level of investigation, three-dimensional morphological characterization coupled with in situ chemical (isotopic, elemental, structural) analysis is the desirable first step. Geological context is paramount, as has been demonstrated by the controversies over AH84001, the Greenland graphites, and the Apex chert microfossils . At larger scales, the nature of sedimentary bedforms and the structures they display becomes crucial, and here the methods of condensed matter physics prove most useful in discriminating between biological and non-biological constructions. Ultimately, a combination of geochemical, morphological, and contextural evidence may be required for certain life detection on the early Earth or elsewhere.

Runnegar, B.↗
NASA NTRS2018

Optimization of Second Fault Detection Thresholds to Maximize Mission POS

In order to support manned spaceflight safety requirements, the Space Launch System (SLS) has defined program-level requirements for key systems to ensure successful operation under single fault conditions. To accommodate this with regards to Navigation, the SLS utilizes an internally redundant Inertial Navigation System (INS) with built-in capability to detect, isolate, and recover from first failure conditions and still maintain adherence to performance requirements. The unit utilizes multiple hardware- and software-level techniques to enable detection, isolation, and recovery from these events in terms of its built-in Fault Detection, Isolation, and Recovery (FDIR) algorithms. Successful operation is defined in terms of sufficient navigation accuracy at insertion while operating under worst case single sensor outages (gyroscope and accelerometer faults at launch). In addition to first fault detection and recovery, the SLS program has also levied requirements relating to the capability of the INS to detect a second fault, tracking any unacceptable uncertainty in knowledge of the vehicle's state. This detection functionality is required in order to feed abort analysis and ensure crew safety. Increases in navigation state error and sensor faults can drive the vehicle outside of its operational as-designed environments and outside of its performance envelope causing loss of mission, or worse, loss of crew. The criteria for operation under second faults allows for a larger set of achievable missions in terms of potential fault conditions, due to the INS operating at the edge of its capability. As this performance is defined and controlled at the vehicle level, it allows for the use of system level margins to increase probability of mission success on the operational edges of the design space. Due to the implications of the vehicle response to abort conditions (such as a potentially failed INS), it is important to consider a wide range of failure scenarios in terms of both magnitude and time. As such, the Navigation team is taking advantage of the INS's capability to schedule and change fault detection thresholds in flight. These values are optimized along a nominal trajectory in order to maximize probability of mission success, and reducing the probability of false positives (defined as when the INS would report a second fault condition resulting in loss of mission, but the vehicle would still meet insertion requirements within system-level margins). This paper will describe an optimization approach using Genetic Algorithms to tune the threshold parameters to maximize vehicle resilience to second fault events as a function of potential fault magnitude and time of fault over an ascent mission profile. The analysis approach, and performance assessment of the results will be presented to demonstrate the applicability of this process to second fault detection to maximize mission probability of success.

Anzalone, Evan↗
NASA NTRS2012

Multi-Sensor Observations of Earthquake Related Atmospheric Signals over Major Geohazard Validation Sites

We are conducting a scientific validation study involving multi-sensor observations in our investigation of phenomena preceding major earthquakes. Our approach is based on a systematic analysis of several atmospheric and environmental parameters, which we found, are associated with the earthquakes, namely: thermal infrared radiation, outgoing long-wavelength radiation, ionospheric electron density, and atmospheric temperature and humidity. For first time we applied this approach to selected GEOSS sites prone to earthquakes or volcanoes. This provides a new opportunity to cross validate our results with the dense networks of in-situ and space measurements. We investigated two different seismic aspects, first the sites with recent large earthquakes, viz.- Tohoku-oki (M9, 2011, Japan) and Emilia region (M5.9, 2012,N. Italy). Our retrospective analysis of satellite data has shown the presence of anomalies in the atmosphere. Second, we did a retrospective analysis to check the re-occurrence of similar anomalous behavior in atmosphere/ionosphere over three regions with distinct geological settings and high seismicity: Taiwan, Japan and Kamchatka, which include 40 major earthquakes (M>5.9) for the period of 2005-2009. We found anomalous behavior before all of these events with no false negatives; false positives were less then 10%. Our initial results suggest that multi-instrument space-borne and ground observations show a systematic appearance of atmospheric anomalies near the epicentral area that could be explained by a coupling between the observed physical parameters and earthquake preparation processes.

Ouzounov, D.↗
NASA NTRS2015

Element Load Data Processor (ELDAP) Users Manual

Often, the shear and tensile forces and moments are extracted from finite element analyses to be used in off-line calculations for evaluating the integrity of structural connections involving bolts, rivets, and welds. Usually the maximum forces and moments are desired for use in the calculations. In situations where there are numerous structural connections of interest for numerous load cases, the effort in finding the true maximum force and/or moment combinations among all fasteners and welds and load cases becomes difficult. The Element Load Data Processor (ELDAP) software described herein makes this effort manageable. This software eliminates the possibility of overlooking the worst-case forces and moments that could result in erroneous positive margins of safety and/or selecting inconsistent combinations of forces and moments resulting in false negative margins of safety. In addition to forces and moments, any scalar quantity output in a PATRAN report file may be evaluated with this software. This software was originally written to fill an urgent need during the structural analysis of the Ares I-X Interstage segment. As such, this software was coded in a straightforward manner with no effort made to optimize or minimize code or to develop a graphical user interface.

Ramsey, John K., Jr.↗
NASA NTRS2016

The IPAC Image Subtraction and Discovery Pipeline for the Intermediate Palomar Transient Factory

We describe the near real-time transient-source discovery engine for the intermediate Palomar Transient Factory (iPTF), currently in operations at the Infrared Processing and Analysis Center (IPAC), Caltech. We coin this system the IPAC/iPTF Discovery Engine (or IDE). We review the algorithms used for PSF-matching, image subtraction, detection, photometry, and machine-learned (ML) vetting of extracted transient candidates. We also review the performance of our ML classifier. For a limiting signal-to-noise ratio of 4 in relatively unconfused regions, bogus candidates from processing artifacts and imperfect image subtractions outnumber real transients by approximately equal to 10:1. This can be considerably higher for image data with inaccurate astrometric and/or PSF-matching solutions. Despite this occasionally high contamination rate, the ML classifier is able to identify real transients with an efficiency (or completeness) of approximately equal to 97% for a maximum tolerable false-positive rate of 1% when classifying raw candidates. All subtraction-image metrics, source features, ML probability-based real-bogus scores, contextual metadata from other surveys, and possible associations with known Solar System objects are stored in a relational database for retrieval by the various science working groups. We review our efforts in mitigating false-positives and our experience in optimizing the overall system in response to the multitude of science projects underway with iPTF.

methods: analytical – methods: data analysis –↗
NASA NTRS2019

Statistical Hypothesis Testing in Wavelet Analysis: Theoretical Developments and Applications to Indian Rainfall

Statistical hypothesis tests in wavelet analysis are methods that assess the degree to which a wavelet quantity(e.g., power and coherence) exceeds background noise. Commonly, a point-wise approach is adopted in which a wavelet quantity at every point in a wavelet spectrum is individually compared to the critical level of the point-wise test. However, because adjacent wavelet coefficients are correlated and wavelet spectra often contain many wavelet quantities, the point-wise test can produce many false positive results that occur in clusters or patches. To circumvent the point-wise test drawbacks, it is necessary to implement the recently developed area-wise, geometric, cumulative area-wise, and topological significance tests, which are reviewed and developed in this paper. To improve the computational efficiency of the cumulative area-wise test, a simplified version of the testing procedure is created based on the idea that its output is the mean of individual estimates of statistical significance calculated from the geometric test applied at a set of point-wise significance levels. Ideal examples are used to show that the geometric and cumulative area-wise tests are unable to differentiate wavelet spectral features arising from singularity-like structures from those associated with periodicities. A cumulative arc-wise test is therefore developed to strictly test for periodicities by using normalized arc length, which is defined as the number of points composing a cross section of a patch divided by the wavelet scale in question. A previously proposed topological significance test is formalized using persistent homology profiles (PHPs) measuring the number of patches and holes corresponding to the set of all point-wise significance values. Ideal examples show that the PHPs can be used to distinguish time series containing signal components from those that are purely noise. To demonstrate the practical uses of the existing and newly developed statistical methodologies, a first comprehensive wavelet analysis of Indian rainfall is also provided. An R software package has been written by the author to implement the various testing procedures.

Wind speed↗
NASA NTRS2006

Determination of Silicon in Hydrazine

Inductively coupled plasma-mass spectrometry (ICP-MS) is a highly sensitive technique sometimes used for the trace determination of silicon at a mass-to-charge (m/z) ratio of 28, the most abundant natural isotope of silicon. Unfortunately, ICP-MS is unable to differentiate between other sources of m/z 28 and false positive results for silicon will result when other sources of m/z 28 are present. Nitrogen was a major source of m/z 28 and contributes to the m/z 28 signal when hydrazine sample or nitric acid preservative is introduced into the plasma. Accordingly, this work was performed to develop a sample preparation step coupled with an ICP-MS analysis that minimized non-silicon sources of m/z 28. In the preparatory step of this method, the hydrazine sample was first decomposed predominately to nitrogen gas and water with copper-catalyzed hydrogen peroxide. In the analysis step, ICP-MS was used without nitric acid preservative in samples or standards. Glass, a potential source of silicon contamination, was also avoided where possible. The method was sensitive, accurate, and reliable for the determination of silicon in monopropellant grade hydrazine (MPH) in AF-E-332 elastomer leaching tests. Results for silicon in MPH were comparable to those reported in the literature for other studies.

McClure, Mark B.↗
NASA NTRSDate not supplied

Tensor Decomposition Analysis for UAV Anomaly Detection

Vibrational anomalies can provide valuable insights into the health status of an unmanned aerial vehicle, potentially indicating system degradation including propeller, motor, or sensor damage, as well as environmental anomalies such as strong wind gusts and turbulence. However, many causes for vibrational anomalies are not related to vehicle health, such as sharp shifts in velocity or direction of flight. Thus, depending strictly on vibration signals to detect anomalies can result in false positives for failures. Hence, it is important to include additional telemetries in detecting and diagnosing in-flight anomalies. This paper considers an approach to anomaly detection based on tensor decompositions that incorporates information from vibration signals, as well as additional flight data such as velocity, current draw, voltage drop, and attitude. Using experimental flight data collected by the University of Notre Dame, we construct third-order tensors then apply the CANDECOMP/PARAFAC decomposition to identify trends within each flight and classify flights as nominal or anomalous.

unmanned aviation↗
NASA NTRS2024

Harnessing Collaborative Learning Automata to Guide Multi-objective Optimization based Inverse Analysis for Structural Damage Identification

Structural damage identification based on physical models is often transformed into an optimization problem that minimizes the difference between measurement information of structure being monitored and the model prediction in the parametric space. However, the objective function in this context often exhibits multimodality, involving high-dimensional variables due to the reliance on finite element models for damage identification. These features pose challenges to optimization algorithms, where entrapment in local solutions can lead to false positives and false negatives in damage identification. In this research, we propose a reinforcement learning based multi-swarm optimizer to tackle such challenges in pursuit of a small yet diverse solution set that can capture the true damage scenario as one of the solutions. The proposed method leverages the flexibility of the particle swarm optimizer and incorporates novel strategies of metaheuristics to realize targeted improvement. To enable the particle swarm to adaptively select the appropriate search strategy based on the current environment, we adopt the learning automata technique, which sidesteps the need for reward strategy selection that is usually ad hoc at each step of the search. The integration harnesses the automatic learning and self-adaptation capabilities of learning automata, enabling the particles to navigate based on environmental signals. This leads to accumulated probabilities tied to advantageous movements, fostering an adaptive exploration of particles in the search space. The proposed approach is first validated through implementing into benchmark test cases with comparisons. It is then applied to structural damage identification with piezoelectric admittance experimental signals. `The results highlight the capability of the algorithm to identify a small solution set with high accuracy to match the actual damage scenario.

Yang Zhang↗
NASA NTRS1999

Classification of ion mobility spectra by functional groups using neural networks

Neural networks were trained using whole ion mobility spectra from a standardized database of 3137 spectra for 204 chemicals at various concentrations. Performance of the network was measured by the success of classification into ten chemical classes. Eleven stages for evaluation of spectra and of spectral pre-processing were employed and minimums established for response thresholds and spectral purity. After optimization of the database, network, and pre-processing routines, the fraction of successful classifications by functional group was 0.91 throughout a range of concentrations. Network classification relied on a combination of features, including drift times, number of peaks, relative intensities, and other factors apparently including peak shape. The network was opportunistic, exploiting different features within different chemical classes. Application of neural networks in a two-tier design where chemicals were first identified by class and then individually eliminated all but one false positive out of 161 test spectra. These findings establish that ion mobility spectra, even with low resolution instrumentation, contain sufficient detail to permit the development of automated identification systems.

Non-NASA Center↗
NASA NTRS1995

PCR Based Microbial Monitor for Analysis of Recycled Water Aboard the ISSA: Issues and Prospects

The monitoring of spacecraft life support systems for the presence of health threatening microorganisms is paramount for crew well being and successful completion of missions. Development of technology to monitor spacecraft recycled water based on detection and identification of the genetic material of contaminating microorganisms and viruses would be a substantial improvement over current NASA plans to monitor recycled water samples that call for the use of conventional microbiology techniques which are slow, insensitive, and labor intensive. The union of the molecular biology techniques of DNA probe hybridization and polymerase chain reaction (PCR) offers a powerful method for the detection, identification, and quantification of microorganisms and viruses. This technology is theoretically capable of assaying samples in as little as two hours with specificity and sensitivity unmatched by any other method. A major advance in probe-hybridization/PCR has come about in a technology called TaqMan(TM), which was invented by Perkin Elmer. Instrumentation using TaqMan concepts is evolving towards devices that could meet NASA's needs of size, low power use, and simplicity of operation. The chemistry and molecular biology needed to utilize these probe-hybridization/PCR instruments must evolve in parallel with the hardware. The following issues of chemistry and biology must be addressed in developing a monitor: Early in the development of a PCR-based microbial monitor it will be necessary to decide how many and which organisms does the system need the capacity to detect. We propose a set of 17 different tests that would detect groups of bacteria and fungus, as well as specific eukaryotic parasites and viruses; In order to use the great sensitivity of PCR it will be necessary to concentrate water samples using filtration. If a lower limit of detection of 1 microorganism per 100 ml is required then the microbes in a 100 ml sample must be concentrated into a volume that can be added to a PCR assay; There are not likely to be contaminants in ISSA recycled water that would inhibit PCR resulting in false-negative results; The TaqMan PCR product detection system is the most promising method for developing a rapid, highly automated gene-based microbial monitoring system. The method is inherently quantitative. NASA and other government agencies have invested in other technologies that, although potentially could lead to revolutionary advances, are not likely to mature in the next 5 years into working systems; PCR-based methods cannot distinguish between DNA or RNA of a viable microorganism and that of a non-viable organism. This may or may not be an important issue with reclaimed water on the ISSA. The recycling system probably damages the capacity of the genetic material of any bacteria or viruses killed during processing to serve as a template in a PCR desinged to amplify a large segment of DNA (less than 650 base pairs). If necessary, vital dye staining could be used in addition to PCR, to enumerate the viable cells in a water sample; The quality control methods have been developed to insure that PCR's are working properly, and that reactions are not contaminated with PCR carryover products which could lead to the generation of false-positive results; and The sequences of the small rRNA subunit gene for a large number of microorganisms are known, and they consititue the best database for rational development of the oligonucleotide reagents that give PCR its great specificity. From those gene sequences, sets of oligonucleotide primers for PCR and Taqman detection that could be used in a NASA microbial monitor were constructed using computer based methods. In addition to space utilization, a microbial monitior will have tremendous terrestrial applications. Analysis of patient samples for microbial pathogens, testing industrial effluent for biofouling bacteria, and detection biological warfare agents on the battlefield are but a few of the diverse potential uses for this technology. Once fully developed, gene-based microbial monitors will become the fundamental tool in every lab that tests for microbial contaminants, and serve as a powerful weapon in mankind's war with the germ world.

Cassell, Gail H.↗
NASA NTRSDate not supplied

A Science-Focused Artificial Intelligence (AI) Responding in Real-Time to New Information: Capability Demonstration for Ocean World Missions

Introduction: Artificial intelligence (AI) has long been considered a potential mechanism to explore increasingly challenging environments, including those with extreme temperatures and pressures, limited communication capabilities, or those with demanding terrain. We posit that missions in extreme environments could deploy an onboard AI focused on science observations and goals in order to augment a traditional concept(s) of operations (ConOps). An onboard AI capability could perform functions such as data analysis in order to make high-level decisions, including prioritized data transmission for analysis by ground-based teams or autonomously-guided follow-on analyses that maximize science return. Such a capability would empower missions to respond to scientific data of interest in real-time; a mission could make observations and perform a preliminary analysis to alert ground-based scientists to an observation of interest, enabling an informed, rapid response from Earth-based teams. Enceladus Case Study for Onboard AI: We are developing an onboard AI capability for real-time telemetry response that formulates and carries-out informed decisions in service to established mission goals, enabling increased science return of a mission. We focus our AI development for use on a constellation of SmallSats orbiting Enceladus. Our Enceladus case study tests autonomous decision-making capabilities in scenarios with complex orbital dynamics, plume ejecta, extreme cold environments, power restrictions, and a requirement to maximize science return for a potential positive detection of life, while critically evaluating the potential for false positives. Telemetry includes simulated scientific data, spacecraft onboard operational data (e.g., position, velocity, and rotation), and engineering hardware performance data. Enceladus SmallSat Constellation. Our constellation includes eight SmallSat spacecraft in an 8:35 resonant orbit-based formation, leveraging Saturn’s gravitational forces to maintain stable orbits with global coverage around Enceladus. To our knowledge, we simulate the first stable configuration of multiple spacecraft in closed orbits around Enceladus, using a full ephemeris force model (Russell and Lara, 2009). Each spacecraft’s orbit will precess, causing an eastward ground track shift (from an orbiter’s perspective) of each spacecraft for each orbit. However, all spacecraft return to their original positions relative to Enceladus after eight Enceladus revolutions around Saturn. We model communication pathways between SmallSats to understand how information would need to be transmitted across the constellation to enable AI-driven decision-making and resource allocation across the fleet. Capability Demonstration. Our simulated capability demonstration inputs position, velocity, and rotation telemetry from our Enceladus-focused constellation simulations, and mass spectrometry data collected from abiotic and biotic laboratory-analog ocean world experiments (Theiling et al., 2018; Theiling, 2021; Da Poian et al., 2023). Data from these experiments are used to simulate MS measurements and different scenarios of science observations for onboard analysis performed on each of the eight spacecraft. For these demonstrations, we integrate 24 machine learning (ML) algorithms into an onboard intelligence as a ‘knowledge base’, including algorithms evaluating data quality and those predicting (with % confidence) gas composition, ocean aqueous chemistry, and whether the sample was influenced by microbial life. The onboard AI capability is designed to use the knowledge base to come to a consensus-based decision in the interpretation of the observed data in order to request additional action outside of a pre-defined ConOps. Requested actions could include e.g., prioritized downlink to Earth (for analysis by ground-based teams) or follow-on analyses performed across the constellation. The spacecraft’s intelligent onboard planner must then determine whether sufficient resources (e.g., time, power, etc.) are available and weigh the request with mission priorities. In our simulation, the constellation is able to identify potential biosignatures using onboard ML algorithms, evaluate the confidence of that prediction, and perform follow-on analyses across the fleet to confirm the detection, in order to best prepare a transmission of these data to Earth-based teams.

astrobiology↗
NASA NTRS1975

MITNYS-II - A digital program for on-line analysis of nystagmus

A digital computer program, MITNYS-II, has been developed for on-line analysis of nystagmus which results from visual, vestibular or caloric stimulation. The program accepts sampled records of eye position and yields cumulative slow phase position, slow phase velocity, instantaneous fast phase frequency and other parameters in 25 ms. In this paper the algorithms by which fast phases are detected, and by which slow phase cumulative eye position is extrapolated across the fast phase interval are described. Extensive tests with vestibular, optokinetic and caloric nystagmus yield reliability figures of the order of 2% for false identification of fast phases and missed fast phases. MITNYS-II has been successfully employed to interpret clinical EOG records, examples of which are presented.

Allum, J. H. J.↗
NASA NTRS2011

Lightning Initiation Forecasting: An Operational Dual-Polarimetric Radar Technique

The objective of this NASA MSFC and NOAA CSTAR funded study is to develop and test operational forecast algorithms for the prediction of lightning initiation utilizing the C-band dual-polarimetric radar, UAHuntsville's Advanced Radar for Meteorological and Operational Research (ARMOR). Although there is a rich research history of radar signatures associated with lightning initiation, few studies have utilized dual-polarimetric radar signatures (e.g., Z(sub dr) columns) and capabilities (e.g., fuzzy-logic particle identification [PID] of precipitation ice) in an operational algorithm for first flash forecasting. The specific goal of this study is to develop and test polarimetric techniques that enhance the performance of current operational radar reflectivity based first flash algorithms. Improving lightning watch and warning performance will positively impact personnel safety in both work and leisure environments. Advanced warnings can provide space shuttle launch managers time to respond appropriately to secure equipment and personnel, while they can also provide appropriate warnings for spectators and players of leisure sporting events to seek safe shelter. Through the analysis of eight case dates, consisting of 35 pulse-type thunderstorms and 20 non-thunderstorm case studies, lightning initiation forecast techniques were developed and tested. The hypothesis is that the additional dual-polarimetric information could potentially reduce false alarms while maintaining high probability of detection and increasing lead-time for the prediction of the first lightning flash relative to reflectivity-only based techniques. To test the hypothesis, various physically-based techniques using polarimetric variables and/or PID categories, which are strongly correlated to initial storm electrification (e.g., large precipitation ice production via drop freezing), were benchmarked against the operational reflectivity-only based approaches to find the best compromise between forecast skill and lead-time. Forecast skill is determined by statistical analysis of probability of detection (POD), false alarm ratio (FAR), Operational Utility Index (OUI), and critical success index (CSI).

Woodard, Crystal J.↗
NASA NTRS2012

Multi-Parameter Observation and Detection of Pre-Earthquake Signals in Seismically Active Areas

The recent large earthquakes (M9.0 Tohoku, 03/2011; M7.0 Haiti, 01/2010; M6.7 L Aquila, 04/2008; and M7.9 Wenchuan 05/2008) have renewed interest in pre-anomalous seismic signals associated with them. Recent workshops (DEMETER 2006, 2011 and VESTO 2009 ) have shown that there were precursory atmospheric /ionospheric signals observed in space prior to these events. Our initial results indicate that no single pre-earthquake observation (seismic, magnetic field, electric field, thermal infrared [TIR], or GPS/TEC) can provide a consistent and successful global scale early warning. This is most likely due to complexity and chaotic nature of earthquakes and the limitation in existing ground (temporal/spatial) and global satellite observations. In this study we analyze preseismic temporal and spatial variations (gas/radon counting rate, atmospheric temperature and humidity change, long-wave radiation transitions and ionospheric electron density/plasma variations) which we propose occur before the onset of major earthquakes:. We propose an Integrated Space -- Terrestrial Framework (ISTF), as a different approach for revealing pre-earthquake phenomena in seismically active areas. ISTF is a sensor web of a coordinated observation infrastructure employing multiple sensors that are distributed on one or more platforms; data from satellite sensors (Terra, Aqua, POES, DEMETER and others) and ground observations, e.g., Global Positioning System, Total Electron Content (GPS/TEC). As a theoretical guide we use the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) model to explain the generation of multiple earthquake precursors. Using our methodology, we evaluated retrospectively the signals preceding the most devastated earthquakes during 2005-2011. We observed a correlation between both atmospheric and ionospheric anomalies preceding most of these earthquakes. The second phase of our validation include systematic retrospective analysis for more than 100 major earthquakes (M>5.9) in Taiwan and Japan. We have found anomalous behavior before all of these events with no false negatives. Calculated false alarm ratio for the for the same month over the entire period of analysis (2003-2009) is less than 10% and was d as the earthquakes. The commonalities in detecting atmospheric/ionospheric anomalies show that they may exist over both land and sea in regions of maximum stress (i.e., along plate boundaries) Our results indicate that the ISTF model could provide a capability to observe pre-earthquake atmospheric/ionospheric signals by combining this information into a common framework.

Ouzounov, D.↗