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NASA NTRS2019

Soil Moisture Data Assimilation

Accurate knowledge of soil moisture at the continental scale is important for improving predictions of weather, agricultural productivity and natural hazards, but observations of soil moisture at such scales are limited to indirect measurements, either obtained through satellite remote sensing or from meteorological networks. Land surface models simulate soil moisture processes, using observation-based meteorological forcing data, and auxiliary information about soil, terrain and vegetation characteristics. Enhanced estimates of soil moisture and other land surface variables, along with their uncertainty, can be obtained by assimilating observations of soil moisture into land surface models. These assimilation results are of direct relevance for the initialization of hydro-meteorological ensemble forecasting systems. The success of the assimilation depends on the choice of the assimilation technique, the nature of the model and the assimilated observations, and, most importantly, the characterization of model and observation error. Systematic differences between satellite-based microwave observations or satellite-retrieved soil moisture and their simulated counterparts require special attention. Other challenges include inferring root-zone soil moisture information from observations that pertain to a shallow surface soil layer, propagating information to unobserved areas and downscaling of coarse information to finer-scale soil moisture estimates. This chapter summarizes state-of-the-art solutions to these issues with conceptual data assimilation examples, using techniques ranging from simplified optimal interpolation to spatial ensemble Kalman filtering. In addition, operational soil moisture assimilation systems are discussed that support numerical weather prediction at ECMWF and provide value-added soil moisture products for the NASA Soil Moisture Active Passive mission.

radar backscatter↗
NASA NTRS2020

GW190412: Observation of a binary-black-hole coalescence with asymmetric masses

We report the observation of gravitational waves from a binary-black-hole coalescence during the first two weeks of LIGO’s and Virgo’s third observing run. The signal was recorded on April 12, 2019 at05∶30∶44 UTC with a network signal-to-noise ratio of 19. The binary is different from observations during the first two observing runs most notably due to its asymmetric masses: a∼30M⊙black hole merged with a ∼8M⊙black hole companion. The more massive black hole rotated with a dimensionless spin magnitude between 0.22 and 0.60 (90% probability). Asymmetric systems are predicted to emit gravitational waves with stronger contributions from higher multipoles, and indeed we find strong evidence for gravitational radiation beyond the leading quadrupolar order in the observed signal. A suite of tests performed onGW190412 indicates consistency with Einstein’s general theory of relativity. While the mass ratio of this system differs from all previous detections, we show that it is consistent with the population model of stellar binary black holes inferred from the first two observing runs.

R. Abbott↗
NASA NTRS1996

Southern Hemisphere In Situ Observations of OH, HO2, CIO and BrO from the ER-2 Aircraft for the 1994 ASHOE Mission

A summary of the first order scientific conclusions that emerged from the research done under this grant are as follows: (1) For the first time, the concentration of the key hydrogen and halogen radicals OH, H02, ClO and BrO were determined on a global scale extending from the arctic circle to the antarctic circle, over the altitude domain of the ER-2. That domain extends from 15-20 km altitude, covering a critical part of the lower stratosphere; (2) Simultaneous, in situ measurements of the concentrations of OH, H02, ClO, BrO, NO and NO2 demonstrate the predominance of odd-hydrogen and halogen free radical catalysis in determining the rate of removal of ozone in the lower stratosphere over the complete ASHOE mission. This extends to the global scale the "first look" data obtained during the NASA Stratospheric Photochemistry and Dynamics Experiment (SPADE), executed out of Ames Research Center in June 1993. This represents a major rearrangement of our understanding with respect to the hierarchy of dominant catalytic cycles controlling ozone loss in the lower stratosphere. For the past twenty years, it has been assumed that nitrogen radicals dominate the destruction rate of ozone in the lower stratosphere; (3) Throughout the altitude and latitude range covered by ASHOE, it was determined that a single catalytic cycle, HO2 + O3 yields OH + 2O2, accounted for one half of the total O3 removal in this region of the atmosphere. Halogen radical catalytic cycles were found to account for one third of the ozone loss, and nitrogen radicals were found to account for 20% of the loss; (4) Simultaneous observations of the full complement of radicals, tracers, ozone, and water vapor during ASHOE demonstrated quantitatively the coupling that exists between the rate limiting radicals and other reactive species in the photochemical reaction network. Specifically, the concentrations of ClO and HO2 are inversely correlated with the concentration of NOx. This carries the implication that the NOx effluent from the proposed High Speed Civil Transport may be less destructive to stratospheric ozone than had previously been thought. ASHOE brought this conclusion forward for the first time on a global basis; and (5) The density of BrO was measured on a global scale during ASHOE in the lower stratosphere. It was found that bromine is responsible for 55-65% of the local rate of catalytic destruction of ozone by reactions involving bromine and chlorine. Normalizing calculated loss rates to total available inorganic bromine and chlorine explicitly demonstrates that bromine is 60-80 times more efficient than chlorine in removing ozone in the lower stratosphere. An inferred value of total inorganic bromine is in excellent agreement with measurements of their source species, organic bromine compounds in the troposphere.

Anderson, James G.↗
NASA NTRS2011

A Search for Optically Faint GEO Debris

Existing optical surveys for debris at geosynchronous orbit (GEO) have been conducted with meter class telescopes, which have detection limits in the range of 18th-19th magnitude. We report on a new search for optically faint debris at GEO using the 6.5-m Magellan 1 telescope Walter Baade at Las Campanas Observatory in Chile. Our goal is to go as faint as possible and characterize the brightness distribution of debris fainter than R = 20th magnitude, corresponding to a size smaller than 10 cm assuming an albedo of 0.175. We wish to compare the inferred size distribution for GEO debris with that for LEO debris. We describe results obtained during 9.4 hours of observing time during 25-27 March 2011. We used the IMACS f/2 instrument, which has a mosaic of 8 CCDs, and a field of view of 30 arc-minutes in diameter. This is the widest field of view of any instrument on either Magellan telescope. All observations were obtained through a Sloan r filter. The limiting magnitude for 5 second exposures is estimated to be fainter than 22. With this small field of view and the limited observing time, our objective was to search for optically faint objects from the Titan 3C Transtage (1968-081) fragmentation in 1992. Eight debris pieces and the parent rocket body are in the Space Surveillance Network public catalog. We successfully tracked two cataloged pieces of Titan debris (SSN # 25001 and 33519) with the 6.5-m telescope, followed by a survey for objects on similar orbits but with a spread in mean anomaly. To detect bright objects over a wider field of view (1.6x1.6 degrees), we observed the same field centers at the same time through a similar filter with the 0.6-m MODEST (Michigan Orbital DEbris Survey Telescope), located 100 km to the south of Magellan at Cerro Tololo Inter-American Observatory, Chile. We will describe our experiences using Magellan, a telescope never used previously for orbital debris research, and our initial results.

Seitzer, Patrick↗
NASA NTRS2004

How Can AERONET Help with Monitoring Clouds?

When conditions are inappropriate to make AErosol RObotic NETwork (AERONET) measurements for aerosol studies, new measurements related to cloud physics can be made instead. As such, several AERONET CIMEL sunphotometers have been equipped with a new "cloud mode." This mode allows the CIMELs to make measurements of zenith radiance when the Sun in blocked by clouds. When in cloud mode, a CIMEL points straight up every 10-15 minutes and takes 10 measurements over a 9 second time interval at four wavelengths: 440,670,870, and 1020 nm. For cloudy conditions above green vegetation, the spectral contrast in surface albedo dominates over Rayleigh and aerosol effects. We have developed a new method for retrieving cloud optical depth, even for broken clouds, that uses data from the 670 (and/or 440) and 870 nm channels. In addition to cloud optical depth, the method also infers a "radiatively effective" cloud fraction. The results of the retrievals at the ARM Central Facility in Oklahoma are compared with the ones from Microwave Radiometer (MWR) and Multi-Filter Rotating Shadowband Radiometers (MFRSR).

Marshak, A.↗
NASA NTRSDate not supplied

Artificial Neural Networks to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.

machine learning↗
NASA NTRSDate not supplied

Machine Learning Models to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.

machine learning↗
NASA NTRS2016

The Plant Phenology Monitoring Design for the National Ecological Observatory Network

Phenology is an integrative science that comprises the study of recurring biological activities or events. In an era of rapidly changing climate, the relationship between the timing of those events and environmental cues such as temperature, snowmelt, water availability, or day length are of particular interest. This article provides an overview of the observer-based plant phenology sampling conducted by the U.S. National Ecological Observatory Network (NEON), the resulting data, and the rationale behind the design. Trained technicians will conduct regular in situ observations of plant phenology at all terrestrial NEON sites for the 30-yr life of the observatory. Standardized and coordinated data across the network of sites can be used to quantify the direction and magnitude of the relationships between phenology and environmental forcings, as well as the degree to which these relationships vary among sites, among species, among phenophases, and through time. Vegetation at NEON sites will also be monitored with tower-based cameras, satellite remote sensing, and annual high-resolution airborne remote sensing. Ground-based measurements can be used to calibrate and improve satellite-derived phenometrics. NEON's phenology monitoring design is complementary to existing phenology research efforts and citizen science initiatives throughout the world and will produce interoperable data. By collocating plant phenology observations with a suite of additional meteorological, biophysical, and ecological measurements (e.g., climate, carbon flux, plant productivity, population dynamics of consumers) at 47 terrestrial sites, the NEON design will enable continental-scale inference about the status, trends, causes, and ecological consequences of phenological change.

phenology↗
NASA NTRS1992

Micro weather stations for in situ measurements in the Martian planetary boundary layer

Viking Lander meteorology measurements show that the Martian planetary boundary layer (PBL) has large diurnal and seasonal variations in pressure, wind velocity, relative humidity, and airborne dust loading. An even larger range of conditions was inferred from remote sensing observations acquired by the Mariner 9 and Viking orbiters. Numerical models indicate that these changes may be accompanied by dramatic vertical and horizontal wind shears (100 m/s/km) and rapid changes in the static stability. In-situ measurements from a relatively small number surface stations could yield global constraints on the Martian climate and atmospheric general circulation by providing ground truth for remote sensing instruments on orbiters. A more complete understanding of the meteorology of the PBL is an essential precursor to manned missions to Mars because this will be their working environment. In-situ measurements are needed for these studies because the spatial and temporal scales that characterize the important meteorological processes near the surface cannot be resolved from orbit. The Mars Environmental Survey (MESUR) Program will provide the first opportunity to deploy a network of surface weather stations for a comprehensive investigation of the Martian PBL. The feasibility and utility of a network of micro-weather stations for making in-situ meteorological measurements in the Martian PBL are assessed.

Crisp, D.↗
NASA NTRS2020

Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors

Phenology is a distinct marker of the impacts of climate change on ecosystems. Accordingly, monitoring the spatiotemporal patterns of vegetation phenology is important to understand the changing Earth system. A wide range of sensors have been used to monitor vegetation phenology, including digital cameras with different viewing geometries mounted on various types of platforms. Sensor perspective, view-angle, and resolution can potentially impact estimates of phenology. We compared three different methods of remotely sensing vegetation phenology—an unoccupied aerial vehicle (UAV)-based, downward-facing RGB camera, a below-canopy, upward-facing hemispherical camera with blue (B), green (G), and near-infrared (NIR) bands, and a tower-based RGB PhenoCam, positioned at an oblique angle to the canopy—to estimate spring phenological transition towards canopy closure in a mixed-species temperate forest in central Virginia, USA. Our study had two objectives: (1) to compare the above- and below-canopy inference of canopy greenness (using green chromatic coordinate and normalized difference vegetation index) and canopy structural attributes (leaf area and gap fraction) by matching below-canopy hemispherical photos with high spatial resolution (0.03 m) UAV imagery, to find the appropriate spatial coverage and resolution for comparison; (2) to compare how UAV, ground-based, and tower-based imagery performed in estimating the timing of the spring phenological transition. We found that a spatial buffer of 20 m radius for UAV imagery is most closely comparable to below-canopy imagery in this system. Sensors and platforms agree within +/− 5 days of when canopy greenness stabilizes from the spring phenophase into the growing season. We show that pairing UAV imagery with tower-based observation platforms and plot-based observations for phenological studies (e.g., long-term monitoring, existing research networks, and permanent plots) has the potential to scale plot-based forest structural measures via UAV imagery, constrain uncertainty estimates around phenophases, and more robustly assess site heterogeneity.

Jeff W. Atkins↗
NASA NTRS2022

Chapter 10 - Remote Sensing Measurements of Aerosol Properties

Satellite instruments have proven especially capable at monitoring the quantity of airborne particles in columns of atmosphere, globally. This chapter describes the principles of satellite measurements and retrieval algorithms, and surveys current instruments and their capabilities. We outline the issues associated with retrieval algorithms, such as surface characterization and aerosol proximity to clouds, and the challenges with interpretation of the results. The relationship between measured aerosol properties and climate-relevant aerosol properties simulated in models is outlined, as well as how measurements are used to evaluate models. Most space-based aerosol instruments are passive sensors that measure reflected sunlight at multiple wavelengths, some at multiple viewing angles. A few are active sensors that send out their own laser light and measure the returned signal. Except when clouds are present, the excess amount of light scattered back to space, beyond that expected from the surface and atmospheric gas, is attributed to aerosol. Satellite measurements are used in many ways in aerosol research. They often provide the only method for monitoring hazardous phenomena such as major wildfire and volcanic eruption plumes, especially in remote areas. Stable, long-term, near-global-scale satellite data records make it possible to identify regional and global aerosol trends. Aerosol radiative effects on climate can be quantified on a near-global scale and used to estimate the strength of aerosol–radiation and aerosol–cloud interactions as well as to evaluate climate model simulations of these interactions. Aerosol-type mapping from satellite imagery is helpful for source attribution, model validation, and to constrain particle light-absorption properties that are essential for radiative forcing calculations. The range of aerosol properties retrieved from satellite observations has grown considerably since the first global estimates of aerosol optical depth (τ a) over ocean were made in the late 1970s. Methods for retrieving particle size and light-absorption properties were explored in the 1990s using multispectral, multi-angle observations, and polarization in visible and near-infrared wavelengths. Sensitivity to particle light absorption, primarily from black or brown carbon content, improved with the inclusion of UV channels, and sensitivity to very thin aerosol layers in the upper troposphere and lower stratosphere was advanced with the use of limb-sounding instruments and active sensors. There are limitations to every measurement technique, including satellite aerosol remote sensing. For wide-swath, passive instruments, aerosol retrievals near clouds can present substantial challenges as far as 15 km away due to cloud-scattered light contaminating the signal. In nearly all cases, retrievals over bright snow and ice surfaces are precluded because surface reflectance uncertainties can overwhelm the aerosol signal. Similarly, meteorological cloud is identified and masked out where possible. Data from passive sensors also lack vertical resolution except those that view toward the limb or where multi-angle imagery is acquired over plumes from wildfires, erupting volcanoes, and wind-blown dust. Yet, passive sensors provide vastly more coverage than the active instruments that mitigate these issues. Particle microphysical information is qualitative from all remote sensing techniques, relying on proxies to infer particle composition, hygroscopicity, and the amount of light-absorbing material. Further, particles smaller than about 200 nm diameter cannot be distinguished from atmospheric gas molecules with remote sensing, which hinders studies of cloud condensation nuclei and their effects on clouds. Most satellite instruments dedicated to aerosol observations are in low-Earth, near-polar, sun-synchronous orbits, which means they cross the equator at the same local time each day. Most are set on cycles that repeat approximately every 16 days, which makes it difficult to monitor aerosol evolution locally. Geostationary satellites make it possible to observe changes occurring from minutes to hours over regions up to 8000 km in size, but lack coverage of high latitudes, and often provide more limited constraints on aerosol properties. Ground-truth data are vital for satellite aerosol-retrieval validation. The AErosol RObotic NETwork (AERONET) of sun photometers was created in 1993 and has become an established global network of over 350 instruments for validating satellite measurements. The network, as well as global networks of ground-based lidars, solar flux radiometers and other sun photometers, are widely used for evaluating global satellite retrievals and model simulations. NASA's Earth Observing System (EOS) program beginning in 1999 led to improvements in reliability, spatial resolution, and spectral resolution (and hence, to improved particle size discrimination and light absorption properties). Satellite payloads include advanced broad-swath and multi-angle imagers, along with the first space-based active sensor focused largely on long-term aerosol monitoring. Since about 2002, Europe's SENTINEL and operational meteorological satellite fleets are also providing sustained aerosol observations, with planned continuation until at least 2030. Satellite remote sensing instruments offer valuable data for evaluating aerosol representations in global climate models. They have been used to assess aerosol optical and physical properties, trends and distributions, and are applied increasingly as direct model constraints in data assimilation to create global aerosol reanalysis products. Aerosol optical depth is the most common quantity adopted for routine model evaluation, including multiwavelength data to loosely constrain particle-size distributions. These evaluations of multiple models have revealed general biases in their regional aerosol amounts and seasonal patterns of transport and removal. Although satellite measurements have near-global coverage, substantial errors can be introduced into the model observation comparison unless attention is paid to spatial and temporal collocation, cloud screening, subgrid-scale variability, and measurement uncertainties that vary with retrieval conditions.

aerosol properties↗
NASA NTRS2009

Using GPS to Detect Imminent Tsunamis

A promising method of detecting imminent tsunamis and estimating their destructive potential involves the use of Global Positioning System (GPS) data in addition to seismic data. Application of the method is expected to increase the reliability of global tsunami-warning systems, making it possible to save lives while reducing the incidence of false alarms. Tsunamis kill people every year. The 2004 Indian Ocean tsunami killed about 230,000 people. The magnitude of an earthquake is not always a reliable indication of the destructive potential of a tsunami. The 2004 Indian Ocean quake generated a huge tsunami, while the 2005 Nias (Indonesia) quake did not, even though both were initially estimated to be of the similar magnitude. Between 2005 and 2007, five false tsunami alarms were issued worldwide. Such alarms result in negative societal and economic effects. GPS stations can detect ground motions of earthquakes in real time, as frequently as every few seconds. In the present method, the epicenter of an earthquake is located by use of data from seismometers, then data from coastal GPS stations near the epicenter are used to infer sea-floor displacements that precede a tsunami. The displacement data are used in conjunction with local topographical data and an advanced theory to quantify the destructive potential of a tsunami on a new tsunami scale, based on the GPS-derived tsunami energy, much like the Richter Scale used for earthquakes. An important element of the derivation of the advanced theory was recognition that horizontal sea-floor motions contribute much more to generation of tsunamis than previously believed. The method produces a reliable estimate of the destructive potential of a tsunami within minutes typically, well before the tsunami reaches coastal areas. The viability of the method was demonstrated in computational tests in which the method yielded accurate representations of three historical tsunamis for which well-documented ground-motion measurements were available. Development of a global tsunami-warning system utilizing an expanded network of coastal GPS stations was under consideration at the time of reporting the information for this article.

Song, Y. Tony↗
NASA NTRS2016

Properties of the Binary Black Hole Merger GW150914

On September 14, 2015, the Laser Interferometer Gravitational-Wave Observatory (LIGO) detected a gravitational-wave transient (GW150914); we characterize the properties of the source and its parameters. The data around the time of the event were analyzed coherently across the LIGO network using a suite of accurate waveform models that describe gravitational waves from a compact binary system in general relativity. GW150914 was produced by a nearly equal mass binary black hole of masses 36(+5/-4) solar mass and 29(+4/-4) solar mass; for each parameter we report the median value and the range of the 90% credible interval. The dimensionless spin magnitude of the more massive black hole is bound to be less than 0.7 (at 90% probability). The luminosity distance to the source is 410(+160/-180) Mpc, corresponding to a redshift 0.09(+0.03/−0.04) assuming standard cosmology. The source location is constrained to an annulus section of 610 sq deg, primarily in the southern hemisphere. The binary merges into a black hole of mass 62(+4/−4) solar mass and spin 0.67(+0.05/−0.07). This black hole is significantly more massive than any other inferred from electromagnetic observations in the stellar-mass regime.

Abbott, B. P.↗
NASA NTRS2014

Oceanic Storm Characteristics Off the Kennedy Space Center Coast

Natural cloud-to-ground lightning may behave differently depending on the characteristics of the attachment mediums, including the peak current (inferred from radiation fields) and the number of ground strike locations per flash. Existing literature has raised issues over the yea"rs on the behavior of lightning over ocean terrain and these phenomena are not yet well understood. To investigate lightning characteristics over differing terrain we will obtain identical observations over adjacent land and ocean regions during both clear air and thunderstorm periods comparing the electric field behavior over these various terrains. For this, a 3-meter NOAA buoy moored 20NM off the coast of the Kennedy Space Center was instrumented with an electric field mill and New Mexico Tech's slow antenna to measure the electric fields aloft and compared to the existing on-shore electric field mill suite of 31 sensors and a coastal slow antenna. New Mexico Tech's Lightning Mapping Array and the Eastern Range Cloud-to-Ground Lightning Surveillance System, along with the network of high-speed cameras being used to capture cloud-to-ground lightning strikes over the terrain regions to identify a valid data set and verify the electric fields. This is an on-going project with the potential for significant impact on the determination of lightning risk to objects on the ground. This presentation will provide results and instrumentation progress to date.

Wilson, J.↗
NASA NTRS2020

GW190814: Gravitational Waves from the Coalescence of a 23 Solar Mass Black Hole with a 2.6 Solar Mass Compact Object

We report the observation of a compact binary coalescence involving a 22.2–24.3 Me black hole and a compact object with a mass of 2.50–2.67Me (all measurements quoted at the 90% credible level). The gravitational-wavesignal, GW190814, was observed during LIGO’s and Virgo’s third observing run on 2019 August 14 at 21:10:39 UTC and has a signal-to-noise ratio of 25 in the three-detector network. The source was localized to18.5 deg2 at a distance of-+2414541Mpc; no electromagnetic counterpart has been confirmed to date. The source has the most unequal mass ratio yet measured with gravitational waves,-+0.1120.0090.008, and its secondary component is either the lightest black hole or the heaviest neutron star ever discovered in a double compact-object system. The dimensionless spin of the primary black hole is tightly constrained to 0.07. Tests of general relativity reveal no measurable deviations from the theory, and its prediction of higher-multipole emission is confirmed at high confidence. We estimate a merger rate density of 1–23 Gpc−3yr−1 for the new class of binary coalescence sources that GW190814 represents. Astrophysical models predict that binaries with mass ratios similar to this event can form through several channels, but are unlikely to have formed in globular clusters. However, the combination of mass ratio, component masses, and the inferred merger rate for this event challenges all current models of the formation and mass distribution of compact-object binaries.

Gravitational wave astronomy↗
NASA NTRS2022

Lithium-ion Battery Strain Gauge Monitoring and Depth of Discharge Estimation

Lithium-ion battery diagnostics and prognostics rely on measurements of electrical impedance, capacity, and voltage to infer the internal state of the battery. Mechanical changes to the cell structure represent an additional measure of the battery’s state because these changes are related to the overall battery health. As lithium-ion batteries are charged and discharged, lithium ions are inserted or removed from the anode and cathode, a process called intercalation and de-intercalation. As lithium ions intercalate and de-intercalate, they can cause changes to the lattice of the electrode particles, resulting in volumetric changes. These volumetric changes cause mechanical stresses and strains on the lithium-ion battery electrodes, and subsequently, the whole cell’s thickness varies as it is charged and discharged. This paper presents a study on the use of surface-mounted strain gauges for in-situ measurement of structural changes to lithium-ion batteries, along with a characterization of the unit-to-unit differences in strain response. A neural network modeling structure is then used to predict the battery’s depth of discharge under dynamic discharge conditions.

Lithium-ion batteries, depth of discharge, strain ↗
NASA NTRS2016

The Global Methane Budget 2000-2012

The global methane (CH4) budget is becoming an increasingly important component for managing realistic pathways to mitigate climate change. This relevance, due to a shorter atmospheric lifetime and a stronger warming potential than carbon dioxide, is challenged by the still unexplained changes of atmospheric CH4 over the past decade. Emissions and concentrations of CH4 are continuing to increase, making CH4 the second most important human-induced greenhouse gas after carbon dioxide. Two major difficulties in reducing uncertainties come from the large variety of diffusive CH4 sources that overlap geographically, and from the destruction of CH4 by the very short-lived hydroxyl radical (OH). To address these difficulties, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate research on the methane cycle, and producing regular (approximately biennial) updates of the global methane budget. This consortium includes atmospheric physicists and chemists, biogeochemists of surface and marine emissions, and socio-economists who study anthropogenic emissions. Following Kirschke et al. (2013), we propose here the first version of a living review paper that integrates results of top-down studies (exploiting atmospheric observations within an atmospheric inverse-modeling framework) and bottom-up models, inventories and data-driven approaches (including process-based models for estimating land surface emissions and atmospheric chemistry, and inventories for anthropogenic emissions, data-driven extrapolations).For the 2003-2012 decade, global methane emissions are estimated by top-down inversions at 558 TgCH4 yr(exp -1), range 540-568. About 60 of global emissions are anthropogenic (range 50-65%). Since 2010, the bottom-up global emission inventories have been closer to methane emissions in the most carbon-intensive Representative Concentrations Pathway (RCP8.5) and higher than all other RCP scenarios. Bottom-up approaches suggest larger global emissions (736 TgCH4 yr(exp -1), range 596-884) mostly because of larger natural emissions from individual sources such as inland waters, natural wetlands and geological sources. Considering the atmospheric constraints on the top-down budget, it is likely that some of the individual emissions reported by the bottom-up approaches are overestimated, leading to too large global emissions. Latitudinal data from top-down emissions indicate a predominance of tropical emissions (approximately 64% of the global budget, less than 30deg N) as compared to mid (approximately 32%, 30-60deg N) and high northern latitudes (approximately 4%, 60-90deg N). Top-down inversions consistently infer lower emissions in China (approximately 58 TgCH4 yr(exp -1), range 51-72, minus14% ) and higher emissions in Africa (86 TgCH4 yr(exp -1), range 73-108, plus 19% ) than bottom-up values used as prior estimates. Overall, uncertainties for anthropogenic emissions appear smaller than those from natural sources, and the uncertainties on source categories appear larger for top-down inversions than for bottom-up inventories and models. The most important source of uncertainty on the methane budget is attributable to emissions from wetland and other inland waters. We show that the wetland extent could contribute 30-40% on the estimated range for wetland emissions. Other priorities for improving the methane budget include the following: (i) the development of process-based models for inland-water emissions, (ii) the intensification of methane observations at local scale (flux measurements) to constrain bottom-up land surface models, and at regional scale (surface networks and satellites) to constrain top-down inversions, (iii) improvements in the estimation of atmospheric loss by OH, and (iv) improvements of the transport models integrated in top-down inversions. The data presented here can be downloaded from the Carbon Dioxide Information Analysis Center (http://doi.org/10.3334/CDIAC/GLOBAL_ METHANE_BUDGET_2016_V1.1) and the Global Carbon Project.

Saunois, Marielle↗
NASA NTRS2020

Recent Radar Observations of the Sub-Centimeter Orbital Debris Environment

The NASA Orbital Debris Program Office (ODPO) has conducted radar observations of the orbital debris environment since the early 1990’s to provide measurement data that supports orbital debris models and risk mitigation activities in support of NASA mission objectives. Orbital debris radar observations are a unique mode for radar operation, employing a fixed beam configuration to statistically sample the environment. An advantage of conducting operations in this fashion is that it enables observations of smaller classes of orbital debris than would otherwise be available from the same sensor operating in a traditional tracking mode. Orbital debris-mode radar observations are used to fill in the gaps, which exist in the currently available data from the Space Surveillance Network (SSN), on small size orbital debris populations that represent significant risk to NASA programs. These gaps have typically covered orbital debris with characteristic sizes less than approximately 10 cm down to approximately 3 mm in low Earth orbit (LEO) – depending upon the altitude and sensor configuration. The value of orbital debris radar measurements lies in the ability to extract partial orbital element information about orbital debris in the centimeter to several millimeter size regimes in low Earth orbit – which are not available from other measurement sources. This paper will discuss observations of this smaller class of orbital debris observed in recent years from the radars at the MIT Haystack Observatory in Westford, Massachusetts, and the Goldstone Solar System Radar near Barstow, California. The former radar is able to observe orbital debris down to approximately 5 mm, and the latter, orbital debris with characteristic sizes near 3 mm – at altitudes less than 1000 km. The characteristics and inferences about the current LEO orbital debris environment, and the different subpopulations that are identifiable in the observations are highlighted.

Kennedy, Timothy↗