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NASA NTRS2016

Results of a Longer Term NDACC Measurements Comparison Campaign at Mauna Loa Observatory

Between November, 2015 and January, 2015, the Goddard Space Flight Center (GSFC) operated a pair of lidar instruments at the NOAA (National Oceanic and Atmospheric Administration) facility at Mauna Loa on the Big Island of Hawaii (Latitude 19.5 degrees North, Longitude 155.5 degrees West, altitude 3.397 kilometers). Measurements were made during six different four week periods during this time period by both the NASA GSFC Stratospheric Ozone Lidar (STROZ) and the Aerosol and Temperature (ATL) lidar. Also making measurements were the JPL Stratospheric Ozone Lidar and the NOAA Aerosol and Water Vapor Lidar. All instruments participate and archive data with the Network for the Detection of Atmospheric Composition Change (NDACC). Measurement comparisons were made among various instruments in accordance with the standard intercomparison protocols of the NDACC.

McGee, Thomas J.↗
NASA NTRS2016

Faint-Source-Star Planetary Microlensing: The Discovery of the Cold Gas-Giant Planet OGLE-2014-BLG-0676Lb

We report the discovery of a planet OGLE-2014-BLG-0676Lb via gravitational microlensing. Observations for the lensing event were made by the following groups: Microlensing Observations in Astrophysics; Optical Gravitational Lensing Experiment; Wise Observatory; RoboNETLas Cumbres Observatory Global Telescope; Microlensing Network for the Detection of Small Terrestrial Exoplanets; and -FUN. All analyses of the light-curve data favoura lens system comprising a planetary mass orbiting a host star. The most-favoured binary lens model has a mass ratio between the two lens masses of (4.78 +/- 0.13) 10(exp -3). Subject to some important assumptions, a Bayesian probability density analysis suggests the lens system comprises a 3.09(+1.02/-1.12) MJ planet orbiting a 0.62(+0.20/-0.22) solar mass host star at a deprojected orbital separation of 4.40(+2.16/-1.46) au. The distance to the lens system is 2.22(+0.96/-0.83) kpc. Planet OGLE-2014-BLG-0676Lb provides additional data to the growing number of cool planets discover redusing gravitational microlensing against which planetary formation theories may be tested. Most of the light in the baseline of this event is expected to come from the lens and thus high-resolution imaging observations could confirm our planetary model interpretation.

OGLE-2014-BLG-0676Lb↗
NASA NTRS2017

Multi-Decadal Records of Stratospheric Composition and Their Relationship to Stratospheric Circulation Change

Constituent evolution for 1990-2015 simulated using the Global Modeling Initiative chemistry and transport model driven by meteorological fields from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) is compared with three sources of observations: ground-based column measurements of HNO3 and HCl from two stations in the Network for the Detection of Atmospheric Composition Change (NDACC, 1990- ongoing), profiles of CH4 from the Halogen Occultation Experiment (HALOE) on the Upper Atmosphere Research Satellite (UARS, 1992-2005), and profiles of N2O from the Microwave Limb Sounder on the Earth Observing System satellite Aura (2005- ongoing). The differences between observed and simulated values are shown to be time dependent, with better agreement after 2000 compared with the prior decade. Furthermore, the differences between observed and simulated HNO3 and HCl columns are shown to be correlated with each other, suggesting that issues with the simulated transport and mixing cause the differences during the 1990s and that these issues are less important during the later years. Because the simulated fields are related to mean age in the lower stratosphere, we use these comparisons to evaluate the time dependence of mean age. The ongoing NDACC column observations provide critical information necessary to substantiate trends in mean age obtained using fields from MERRA-2 or any other reanalysis products.

Profiles of N2O from the Microwave Limb Sounde↗
NASA NTRS2017

An Update on Ozone Profile Trends for the Period 2000 to 2016

Ozone profile trends over the period 2000 to 2016 from several merged satellite ozone data sets and from ground-based data measured by four techniques at stations of the Network for the Detection of Atmospheric Composition Change indicate significant ozone increases in the upper stratosphere, between 35 and 48 kilometers altitude (5 and 1 hectopascals). Near 2 hectopascals (42 kilometers), ozone has been increasing by about 1.5 percent per decade in the tropics (20 degrees S to 20 degrees N), and by 2 to 2.5 percent per decade in the 35 to 60 degree latitude bands of both hemispheres. At levels below 35 kilometers (5 hectopascals), 2000 to 2016 ozone trends are smaller and not statistically significant. The observed trend profiles are consistent with expectations from chemistry climate model simulations. This study confirms positive trends of upper stratospheric ozone already reported, e.g., in the WMO/UNEP (World Meteorological Organization/United Nations Environmental Programme) Ozone Assessment 2014 or by Harris et al. (2015). Compared to those studies, three to four additional years of observations, updated and improved data sets with reduced drift, and the fact that nearly all individual data sets indicate ozone increase in the upper stratosphere, all give enhanced confidence. Uncertainties have been reduced, for example for the trend near 2 hectopascals in the 35 to 60 degree latitude bands from about plus or minus 5 percent (2 sigma) in Harris et al. (2015) to less than plus or minus 2 percent (2 sigma). Nevertheless, a thorough analysis of possible drifts and differences between various data sources is still required, as is a detailed attribution of the observed increases to declining ozone-depleting substances and to stratospheric cooling. Ongoing quality observations from multiple independent platforms are key for verifying that recovery of the ozone layer continues as expected.

chemistry climate model simulations↗
NASA NTRS2018

Lidar Validation Measurements at the NOAA Mauna Loa Observatory NDACC Station

NASA's Goddard Space Flight Center (GSFC) transported two lidar instruments to the NOAA facility at the Mauna Loa Observatory (MLO) on the Big Island of Hawaii, to participate in an official, extended validation campaign. This site is situated 11,141 ft. above sea level on the side of the mountain. The observatory has been making atmospheric measurements regularly since the 1950's, and has hosted the GSFC Stratospheric Ozone (STROZ) Lidar and the GSFC Aerosol and Temperature (AT) Lidar on several occasions, most recently between November, 2012 and November, 2015. The purpose of this extended deployment was to participate in Network for the Detection of Atmospheric Composition Change (NDACC) Validation campaigns with the JPL Stratospheric Ozone Lidar and the NOAA Temperature, Aerosol and Water Vapor instruments as part of the routine NDACC Validation Protocol.

Lidar↗
NASA NTRS2013

TPSAS-NF1676L-16299-DND

As part of the American Recovery and Reinvestment Act (ARRA) of 2009, the DOE ARM program deployed a new ground-­‐based Arctic High Spectral Resolution Lidar (AHSRL) at the North Slope of Alaska (NSA) site. This lidar, which is based on the AHSRL that was part of the NOAA’s SEARCH (Study of Environmental Arctic Change) contribution to the Canadian Network for the Detection of Atmospheric Change (CANDAC) facility at Eureka, Nunavut Territory, is an autonomous lidar that provides calibrated profiles of particulate (i.e. aerosol and cloud) backscatter cross section, optical thickness and circular depolarization at 532 nm. The AHSRL provides measurements of both intensive and extensive particulate properties. The intensive parameters depend only on the nature of the particles (size, composition, shape), not on the quantity or concentration. Here we show examples of the AHSRL data during both clear and cloudy conditions to show how these measurements can be used to depict aerosols and clouds over the NSA.

Marian Clayton↗
NASA NTRS2002

Lidar system for investigation of aerosols and cirrus clouds near the equator

In the current environment of concern over global warming, climate change, and ozone depletion/recovery, measurements in the tropics, within 5 degrees of the equator are limited and/or sporadic. Clearly, routine observations in this region would be of great benefit to many programs including those dealing with atmospheric chemistry, dynamics and radiation, and validation of satellite instruments. Such observations represent the primary goals of the Network for the Detection of Stratospheric Change (NDSC).

atmosphere↗
NASA NTRS2020

The NANOGrav 11 yr data set: Evolution of Gravitational-wave Background Statistics

An ensemble of inspiraling supermassive black hole binaries should produce a stochastic background of very low frequency gravitational waves. This stochastic background is predicted to be a power law, with a gravitational-wave strain spectral index of −2/3, and it should be detectable by a network of precisely timed millisecond pulsars, widely distributed on the sky. This paper reports a new “time slicing” analysis of the 11 yr data release from the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) using 34 millisecond pulsars. Methods to flag potential “false-positive” signatures are developed, including techniques to identify responsible pulsars. Mitigation strategies are then presented. We demonstrate how an incorrect noise model can lead to spurious signals, and we show how independently modeling noise across 30 Fourier components, spanning NANOGrav’s frequency range, effectively diagnoses and absorbs the excess power in gravitational-wave searches. This results in a nominal, and expected, progression of our gravitational-wave statistics. Additionally, we show that the first interstellar medium event in PSR J1713+0747 pollutes the common red-noise process with low spectral index noise, and we use a tailored noise model to remove these effects.

J S Hazboun↗
NASA NTRSDate not supplied

A Ground-Based Network for Improved Validation of Satellite Carbon Dioxide and Methane Observations Over the Eastern United States

Satellite observations of greenhouse gases (GHGs), notably carbon dioxide and methane, over the Eastern United States, are currently only validated indirectly and/or sporadically. There are only four existing routine, ground-based remote sensing locations in the United States suitable for validation of satellite GHG observations: Edwards and Pasadena, CA, Lamont, OK, and Park Falls, WI as part of the Total Carbon Column Observing Network (TCCON). Among other efforts, e.g. the Network for the Detection of Atmospheric Composition Change (NDACC) and EM27/SUN deployments led by the University of Toronto, the only sites west of the Mississippi River are Park Falls, WI and Toronto, ON. The only remaining validation tools, vicarious calibration and airborne campaigns, are sporadic in space and/or time and thus coincide with only a small subsample of available soundings and conditions. As a result, satellite GHG observations over the east coast of the United States, home to more than half of its population, lack a consistent, widespread means of validation. We describe an ongoing effort to position 8 EM27/SUN spectrometers along the Eastern Seaboard over the next two years. The goals of this effort are to improve both satellite validation and our understanding of human and natural influences on the carbon cycle of the Eastern US, the former enabling the latter. This work is intended to augment past, ongoing, and future inter-agency programs, e.g., the NIST Urban Testbed, routine aircraft and aircore sampling by NOAA, and NASA’s Atmospheric Carbon and Transport (ACT)-America sub-orbital campaign, in particular by offering information on broader time and spatial scales than what is already available while maintaining the high-accuracy constraints of in situ data. We will present early analysis including siting considerations to capture local and/or background conditions and comparison to NASA’s Goddard Earth Observing System (GEOS) modeling and assimilation systems. This includes a 40-day, 3-km horizontal resolution global simulation of early 2020 and a 50-km retrospective analysis of Orbiting Carbon Observatory 2 (OCO-2) observations over 2015-present. Both are valuable tools for analyzing expected and observed signals and are useful boundary conditions for yet higher-resolution studies.

Brad Weir↗
DOE OSTI2014

Data Diode

The Data Diode is a data security technology that can be deployed within an organization's defense-in-depth computer network strategy for information assurance. For internal security, the software creates an environment within the network where an organization's approved users can work freely inside an enclave of protected data, but file transfers out of the enclave is restricted. For external security, once a network intruder has penetrated the network, the intruder is able to "see" the protected data, but is unable to download the actual data. During the time it takes for the intruder to search for a way around the obstacle created by the Data Diode, the network's intrusion detection technologies can locate and thwart the malicious intent of the intruder. Development of the Data Diode technology was made possible by funding from the Intelligence Advanced Research Projects Activity (IARPA).

Source record↗
DOE OSTI2019

Waste Facility Groundwater Monitoring Design Under Uncertainty - 19462

A systematic approach was developed to evaluate current and future monitoring networks designed to detect potential contaminant releases at a number of Dangerous Waste Management Units within the 200 West Area of the Central Plateau at the U.S. Department of Energy's Hanford Site near Richland, Washington. The analyses account for uncertain future flow directions arising from the operation of a high-capacity groundwater pump-and-treat remedy that will operate for the foreseeable future. The developed approach and tools provide a novel means to evaluate and compare the relative efficacy of current or potential monitoring locations in complex and uncertain flow fields without recourse to computationally prohibitive contaminant transport modeling. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2012

DETECTION OF FLUX EMERGENCE, SPLITTING, MERGING, AND CANCELLATION OF NETWORK FIELD. I. SPLITTING AND MERGING

Frequencies of magnetic patch processes on the supergranule boundary, namely, flux emergence, splitting, merging, and cancellation, are investigated through automatic detection. We use a set of line-of-sight magnetograms taken by the Solar Optical Telescope (SOT) on board the Hinode satellite. We found 1636 positive patches and 1637 negative patches in the data set, whose time duration is 3.5 hr and field of view is 112'' Multiplication-Sign 112''. The total numbers of magnetic processes are as follows: 493 positive and 482 negative splittings, 536 positive and 535 negative mergings, 86 cancellations, and 3 emergences. The total numbers of emergence and cancellation are significantly smaller than those of splitting and merging. Further, the frequency dependence of the merging and splitting processes on the flux content are investigated. Merging has a weak dependence on the flux content with a power-law index of only 0.28. The timescale for splitting is found to be independent of the parent flux content before splitting, which corresponds to {approx}33 minutes. It is also found that patches split into any flux contents with the same probability. This splitting has a power-law distribution of the flux content with an index of -2 as a time-independent solution. These results support that the frequency distribution of the flux content in the analyzed flux range is rapidly maintained by merging and splitting, namely, surface processes. We suggest a model for frequency distributions of cancellation and emergence based on this idea.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Rocket Launch Detection with Smartphone Audio and Transfer Learning

Rocket launches generate infrasound signatures that have been detected at great distances. Due to the sparsity of the networks that have made these detections, however, most signals are detected tens of minutes to hours after the rocket launch. In this work, a method of near-real-time detection of rocket launches using data from a network of smartphones located 10–70 km from launch sites is presented. A machine learning model is trained and tested on the open-access Aggregated Smartphone Timeseries of Rocket-generated Acoustics (ASTRA), Smartphone High-explosive Audio Recordings Dataset (SHAReD), and ESC-50 datasets, resulting in a final accuracy of 97% and a false positive rate of <1%. The performance and behavior of the model are summarized, and its suitability for persistent monitoring applications is discussed.

acoustics↗
DOE OSTI2025

Comparison of Sequence Component-Based Fault Detection and Relay Coordination Algorithms in Inverter-Based Networks

Protection of inverter-based microgrids using sequence component-based relaying schemes is a promising solution. These methods offer several advantages, including lower computational requirements, compatibility with commercial relay systems, and cost-effectiveness compared to communication-based approaches. This article investigate the performance of various sequence component based schemes with the objective of identifying the algorithms that provide the best fault detection and relay coordination, solely relying on local voltages and current at relay terminals. Positive, negative and zero sequence impedance, admittance and power detection algorithms were tested on modified IEEE 13 bus test network for various shunt faults (LG, LL, LLG, LLL). Hardware-in-the-loop validation was achieved using the Typhoon real-time simulator, interfacing with a SEL 751 relay. This research demonstrates that while several algorithms are capable of detecting faults with sufficient accuracy, only a few are effective in achieving proper coordination. Validation results indicate that the negative sequence power approach provides the best performance in both fault detection and coordination.

Patel, Deepika [ORNL] (ORCID:0000000341099994)↗
DOE OSTI2025

SQuaD: Smart Quantum Detection for Photon Recognition and Dark Count Elimination

Quantum detectors of single photons are an essential component for quantum information processing across computing, communication and networking. Today's quantum detection system, which consists of single photon detectors, timing electronics, control and data processing software, is primarily used for counting the number of single photon detection events. However, it is largely incapable of extracting other rich physical characteristics of the detected photons, such as their wavelengths, polarization states, photon numbers, or temporal waveforms. This work, for the first time, demonstrates a smart quantum detection system, SQuaD, which integrates a field programmable gate array (FPGA) with a neural network model, and is designed to recognize the features of photons and to eliminate detector dark-count. The SQuaD is a fully integrated quantum system with high timing-resolution data acquisition, onboard multi-scale data analysis, intelligent feature recognition and extraction, and feedback-driven system control. Our \name experimentally demonstrates 1) reliable photon counting on par with the state-of-the art commercial systems; 2) high-throughput data processing for each individual detection events; 3) efficient dark count recognition and elimination; 4) up to 100% accurate feature recognition of photon wavelength and polarization. Additionally, we deploy the SQuaD to an atomic (erbium ion) photon emitter source to realize noise-free control and readout of a spin qubit in the telecom band, enabling critical advances in quantum networks and distributed quantum information processing.

Linne, Karl C. [U. Chicago (main)] (ORCID:00090009↗
DOE OSTI2021

Deep Learning Based Approach to Integrate MyShake's Trigger Data with ShakeAlert for Faster and Robust EEW Alerts

Earthquake Early Warning (EEW) systems depend on the dense seismic network to make fast and accurate earthquake detections to issue useful early alerts. The recent development and implementation of the ShakeAlert system is relying on the west coast seismic network to detect and estimate the earthquake parameters in real-time. While working on making improvements on the current system, one potential approach is to include more seismic recordings from various sources, such as the low-cost smartphone seismic network, such as the MyShake network, or the Google Android smartphones, since these smartphone seismic networks have much more portable stations (phones) to potentially provide useful data to the system. This report summarizes the initial exploration of using a deep learning approach to combine the data from both traditional seismic stations and the smartphone data in California. Due to the heterogeneity nature of the data, we aggregate data from the phones (using simulation data), as well as that from traditional seismic stations to grid cells. By generating real-time triggering ratio grid cell maps, the designed deep learning algorithm can process the data from multiple sources and detect the earthquake faster than only using that from a traditional seismic network.

58 GEOSCIENCES↗
DOE OSTI2021

Evaluating Probability of Containment Effectiveness at a GCS Sites using integrated assessment modeling approach with Bayesian decision Networks

Improved scientific and engineering understanding of the behavior of geologic CO2 storage together with established regulatory framework and incentive structures raise the prospects for accelerated, large-scale deployment of this greenhouse gas emissions reduction approach. Incentive structures call for the establishment of appropriate verification and accounting approaches to support claims of the integrity of a geologic storage complex and to justify taking credit for long-term storage. In this study, we present a framework for assessing the probability of containment effectiveness over the lifetime of a geologic carbon storage site (e.g., after 70 years of injection and post-injection site performance) using forward stochastic model realizations based on site characterization data and using a monitoring-informed Bayesian network based on hypothetical detectability from surface seismic surveys over the site injection and post-injection phases. The National Risk Assessment Partnership’s open-source Integrated Assessment Model (NRAP-Open-IAM) was utilized to develop an ensemble of 10,000 a priori stochastic forecasts of CO2 containment. Those simulations were used to train the Bayesian network model to estimate the prior probabilities of the CO2 leakage mass into overlying, monitorable aquifers considering the uncertainties in the reservoir properties, permeability of potentially leaky wells and the overlying aquifers. The conditional probabilities in the Bayesian network were either learned from the NRAP-Open-IAM simulations or derived from the predefined detection thresholds for the monitoring method. Observations obtained from monitoring, over time during the site operation phases were then used to generate updated posterior probabilities of containment (and any loss from containment) in the Bayesian network by propagating the prior probabilities through the conditional probabilities. We demonstrate how to construct and use the Bayesian network for verifying the long-term storage complex effectiveness informed by monitoring based on the NRAP-Open-IAM simulations previously developed for the FutureGen 2.0 site. This approach may have relevance for stake holders to demonstrate secure geologic storage, provide a defensible, probabilistic approach to claim credit for geologic storage, and to estimate the likelihood that any fraction of the claimed credit may need to be refunded to the creditor based on available monitoring information.

Bayesian network, Risk assessment, Monitoring, car↗
NASA NTRS2017

The Use of D-Criteria to Assess Meteor Shower Significance

In theory, a meteor shower can be distinguished from the sporadic meteor background by its short duration and orbital similarity. In practice, the duration and strength of a shower and the orbital similarity between its constituent meteors varies widely between showers. Further complicating matters is the anisotropy of the sporadic background. These combined factors make it difficult to distinguish between shower and sporadic meteors with a single, static set of criteria. The orbital similarity, or D-, parameters are often used to assess the relationship between meteors [1,2,3]. The more dissimilar two orbits are, the higher their computed D value will be; generally, meteors are considered related if their D-parameter falls below some cutoff value [4]. However, this approach will include some sporadic meteors, and when a weak shower lies near a sporadic source, the false positive rate for shower association can be quite high. Additionally, this cutoff approach does not assess whether the shower itself is significant. We present a method for using D-parameters to extract showers from a dataset that automatically takes shower strength into account and tests for significance [5]. We accomplish this by calculating the false positive rate for shower association using "shower analogs," which are identical to the original shower except in solar longitude. This method is applied to a set of more than 30,000 meteors detected by the NASA All-Sky Fireball Network [6] and the Southern Ontario Meteor Network (SOMN) [7]. We previously detected 29 showers in our data using this method [5]; now, with another year of data, we have several additional detections. Figure 1 presents one example: the 2016 July gamma Draconid outburst. There are several benefits to using our method. First, it provides a test of shower significance (see Fig. 2 for an example of a non-detection). Second, it quantifies the probability that a meteor belongs to a given shower as a function of D-parameter. Finally, it quantifies the strength of a shower, even when individual members cannot be identified with 100% accuracy.

Moorhead, Althea V.↗