Easterly Waves in the East Pacific during the OTREC 2019 Field Campaign
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Lidia Huaman↗
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Results for “2019”
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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Sea spray effects on typhoon prediction in the Yellow and East China Seas: case studies using a coupled atmosphere-ocean-wave model for Lingling (2019) and Maysak (2020)
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Sinil Yang↗
The Color and Brightness of the F-corona Inferred from the 2019 July 2 Total Solar Eclipse
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Benjamin Boe↗
A near magnetic-to-kinetic energy equipartition flare from the relativistic jet in AO 0235 + 164 during 2013–2019
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Whee Yeon Cheong↗
Increasing anthropogenic methane emissions arise equally from agricultural and fossil fuel sources
Climate stabilization remains elusive, with increased greenhouse gas concentrations already increasing global average surface temperatures 1.1°C above pre-industrial levels (World Meteorological Organization 2019). Carbon dioxide (CO2) emissions from fossil fuel use, deforestation, and other anthropogenic sources reached ~ 43 billion metric tonnes in 2019 (Friedlingstein et al 2019, Jackson et al 2019). Storms, floods, and other extreme weather events displaced a record 7 million people in the first half of 2019 (IDMC 2019). When global mean surface temperature four million years ago was 2°C–3°C warmer than today (a likely temperature increase before the end of the century), ice sheets in Greenland and West Antarctica melted and parts of East Antarctica’s ice retreated, causing sea levels to rise 10–20 m (World Meteorological Organization 2019). Methane (CH4) emissions have contributed almost one quarter of the cumulative radiative forcings for CO2, CH4, and N2O (nitrous oxide) combined since 1750 (Etminan et al 2016). Although methane is far less abundant in the atmosphere than CO2, it absorbs thermal infrared radiation much more efficiently and, in consequence, has a global warming potential (GWP) ~86 times stronger per unit mass than CO2 on a 20-year timescale and 28- times more powerful on a 100-year time scale (IPCC 2014). Global average methane concentrations in the atmosphere reached ~1875 parts per billion (ppb) at the end of 2019, more than two-and-a-half times preindustrial levels (Dlugokencky 2020). The largest methane sources include anthropogenic emissions from agriculture, waste, and the extraction and use of fossil fuels as well as natural emissions from wetlands, freshwater systems, and geological sources (Kirschke et al 2013, Saunois et al 2016a, Ganesan et al 2019). Here, we summarize new estimates of the global methane budget based on the analysis of Saunois et al (2020) for the year 2017, the last year of the new Global Methane Budget and the most recent year data are fully available. We compare these estimates to mean values for the reference ‘stabilization’ period of 2000–2006 when atmospheric CH4 concentrations were relatively stable. We present data for sources and sinks and provide insights for the geographical regions and economic sectors where emissions have changed the most over recent decades.
fossil fuel sources↗
Spatial and Temporal Characterization of Cyanobacteria Blooms in the Mississippi Sound and Their Relationship to the Bonnet Carré Spillway Openings
During the spring and summer of 2019, an unprecedented cyanobacterial harmful algal bloom (cyanoHAB) was responsible for beach advisories on 25 beaches along the Mississippi Sound for over 3 months. Due to the preceding heavy rainfall and flooding within the Mississippi River watershed, for the first time in history, the Bonnet Carré Spillway (BCS) opened twice in one year during 2019. The coastal cyanoHAB coincided with the second BCS opening. The main objectives of this study were: (1) to investigate the potential for using the National Aeronautics and Space Administration (NASA) ocean color standard Cyanobacteria Index (CIcyano) algorithm to characterize the spatial and temporal extent of the 2019 cyanoHAB; (2) to couple the CIcyano data with river discharge, salinity, and modeled-wind data to study the conditions leading to the cyanoHAB and factors aiding the advection and persistence of the bloom within the Mississippi Sound, including a possible relationship to the BCS; (3) to further investigate the relationship with the BCS by repeating the methods using data from 2018, which was a year when the BCS was opened but no evidence of cyanoHABs was reported along the Mississippi coast. Weekly means and monthly frequency CIcyano images, river discharge, salinity, and modeled-wind data from February to September of 2018 and 2019 were analyzed, which coincide with three BCS openings. In March 2018, a cyanobacteria bloom was observed within Lake Pontchartrain coinciding with the BCS opening; however, the month-long bloom was contained to the lake. Two distinct cyanoHABs were observed in 2019 and both blooms were advected into the Mississippi Sound, and likely contributed to the 3-month-long beach water advisories of 2019 along the Mississippi coastline. From March to mid-July 2019, salinity at stations within the Mississippi Sound was consistently near zero indicating high levels of freshwater. During that time, winds were predominantly northwestward, preventing the BCS waters from flushing into the Mississippi Shelf and resulting in BCS waters remaining longer within the estuarine lakes and Mississippi Sound. Although the BCS had an undeniable impact on the presence of the coastal cyanoHAB of 2019, other variables including wind direction, water flow, mixing, and persistence of freshwater within the Sound can determine the intensity and extent of the cyanoHABs. Coupling in situ phytoplankton information from freshwater water bodies to the marine continuum along with water flow, wind data, and satellite imagery could help identify cyanoHABs at early stages and forecast their trajectory and potential impacts on coastal areas.
Bonnet Carré Spillway↗
Characterization of the Nucleus, Morphology, and Activity of Interstellar Comet 2I/Borisov by Optical and Near-infrared GROWTH, Apache Point, IRTF, ZTF, and Keck Observations
We present visible and near-infrared (NIR) photometric and spectroscopic observations of interstellar object (ISO) 2I/Borisov taken from 2019 September 10 to 2019 December 20 using the GROWTH, the Apache Point Observatory Astrophysical Research Consortium 3.5 m, and the NASA Infrared Telescope Facility 3.0 m combined with pre- and postdiscovery observations of 2I obtained by the Zwicky Transient Facility from 2019 March 17 to 2019 May 5. Comparison with imaging of distant solar system comets shows an object very similar to mildly active solar system comets with an outgassing rate of ∼1027 mol s−1. The photometry, taken in filters spanning the visible and NIR range, shows a gradual brightening trend of ∼0.03 mag day−1 since 2019 September 10 UTC for a reddish object becoming neutral in the NIR. The light curve from recent and prediscovery data reveals a brightness trend suggesting the recent onset of significant H2O sublimation with the comet being active with super volatiles such as CO at heliocentric distances >6 au consistent with its extended morphology. Using the advanced capability to significantly reduce the scattered light from the coma enabled by high-resolution NIR images from Keck adaptive optics taken on 2019 October 4, we estimate a diameter for 2I’s nucleus of <~1.4 km. We use the size estimates of 1I/’Oumuamua and 2I/Borisov to roughly estimate the slope of the ISO size distribution, resulting in a slope of ∼3.4 ± 1.2, similar to solar system comets and bodies produced from collisional equilibrium.
Bryce T Bolin↗
Pre- and Post-Production Processes Increasingly Dominate Greenhouse Gas Emissions From Agri-Food Systems
We present results from the FAOSTAT emissions shares database, covering emissions from agri-food systems and their shares to total anthropogenic emissions for 196 countries and 40 territories for the period 1990–2019. We find that in 2019, global agri-food system emissions were 16.5 (95 %; CI range: 11–22) billion metric tonnes (GtCO2 eq. yr(exp -1)), corresponding to 31%(range: 19 %–43 %) of total anthropogenic emissions. Of the agri-food system total, global emissions within the farm gate – from crop and livestock production processes including on-farm energy use – were 7.2 GtCO2 eq. yr(exp -1); emissions from land use change, due to deforestation and peatland degradation, were 3.5 GtCO2 eq. yr(exp -1); and emissions from pre- and post-production processes – manufacturing of fertilizers, food processing, packaging, transport, retail, household consumption and food waste disposal – were 5.8 GtCO2 eq. yr(exp -1). Over the study period 1990–2019, agri-food system emissions increased in total by 17 %, largely driven by a doubling of emissions from pre- and post-production processes. Conversely, the FAOSTAT data show that since 1990 land use emissions decreased by 25 %, while emissions within the farm gate increased 9 %. In 2019, in terms of individual greenhouse gases (GHGs), pre- and postproduction processes emitted the most CO2 (3.9 GtCO2 yr(exp -1)), preceding land use change (3.3 GtCO2 yr(exp -1)) and farm gate (1.2 GtCO2 yr(exp -1)) emissions. Conversely, farm gate activities were by far the major emitter of methane (140 MtCH4 yr(exp -1)) and of nitrous oxide (7.8 MtN2Oyr(exp -1)). Pre- and post-production processes were also significant emitters of methane (49 MtCH4 yr(exp -1)), mostly generated from the decay of solid food waste in landfills and open dumps. One key trend over the 30-year period since 1990 highlighted by our analysis is the increasingly important role of food-related emissions generated outside of agricultural land, in pre- and post-production processes along the agri-food system, at global, regional and national scales. In fact, our data show that by 2019, pre- and post-production processes had overtaken farm gate processes to become the largest GHG component of agri-food system emissions in Annex I parties (2.2 GtCO2 eq. yr(exp -1)). They also more than doubled in non-Annex I parties (to 3.5 GtCO2 eq. yr(exp -1)), becoming larger than emissions from land use change. By 2019 food supply chains had become the largest agri-food system component in China (1100 MtCO2 eq. yr(exp -1)), the USA (700 MtCO2 eq. yr(exp -1)) and the EU-27 (600 MtCO2 eq. yr(exp -1)). This has important repercussions for food-relevant national mitigation strategies, considering that until recently these have focused mainly on reductions of non-CO2 gases within the farm gate and on CO2 mitigation from land use change. The information used in this work is available as open data with DOI https://doi.org/10.5281/zenodo.5615082 (Tubiello et al., 2021d). It is also available to users via the FAOSTAT database (https://www.fao.org/faostat/en/#data/EM; FAO, 2021a), with annual updates.
FAOSTAT agri-food systems emissions database↗
NASA’s Space Launch System: Exploration Mission-1 Hardware Nears Completion
The Space Launch System (SLS) Program completed several significant production milestones in 2018 for the launch vehicle’s first mission (Fig. 1) and is poised for greater accomplishments in 2019. With manufacturing and hardware installation finished, Boeing completed the core stage forward join and shipped the liquid hydrogen tank structural test article to Marshall Space Flight Center for testing. The core stage aft join and LOX tank STA are expected to be completed in 2019 on the way to final stage integration. The four EM-1 engines are poised for stage integration in 2019. The Launch Vehicle Stage Adapter completed outfitting at Marshall and will be shipped to Kennedy Space Center in 2019. All solid rocket motor segments for the EM-1 boosters are cast, inspected and ready for shipment to KSC. The upper stage, the Interim Cryogenic Propulsion Stage (ICPS), and the Orion Stage Adapter (OSA), where 13 6U CubeSats will ride to deep space on EM-1, were completed and delivered to Exploration Ground Systems at KSC in 2017 and 2018, respectively. The Program continues to work toward first launch of the nation’s new super heavy lift deep space capability in fiscal 2020. SLS is designed, engineered and tested to launch the most challenging exploration missions, minimizing risk and providing the greatest opportunity for mission success. This paper will discuss the technical and programmatic successes and challenges of the past year and look ahead to plans for 2019.
Honeycutt, John↗
NASA’s Space Launch System: First Mission Hardware Nears Completion
The Space Launch System (SLS) Program (Fig. 1) completed several significant production milestones in 2018 for its first mission and is poised for greater accomplishments in 2019. With manufacturing and hardware installation complete, Boeing completed the core stage forward join and shipped the liquid hydrogen tank structural test article to Marshall Space Flight Center for testing. The core stage aft join and LOX tank STA are expected to be completed in 2019 on the way to final stage integration. The four EM-1 engines are poised for stage integration in 2019. The Launch Vehicle Stage Adapter completed outfitting at Marshall and will be shipped to Kennedy Space Center in 2019. All solid rocket motor segments for the EM-1 boosters are cast, inspected and ready for shipment to KSC. The Program continues to work toward first launch of the nation’s new super heavy lift deep space capability in fiscal 2020. SLS is designed, engineered and tested to launch the most challenging exploration missions, minimizing risk and providing the greatest opportunity for mission success and scientific discovery. This paper will discuss the technical and programmatic successes and challenges of the past year and look ahead to plans for 2019.
Honeycutt, John↗
Contractors Road Heavy Equipment Area (SWMU055) Hot Spot 1 Bioremediation Interim Measure Performance Monitoring And Site-Wide Interim Groundwater Monitoring Report
This document presents a summary of activities completed from January through December 2019 at the Contractors Road Heavy Equipment (CRHE) area, located at John F. Kennedy Space Center (KSC), Florida. The activities summarized include: 1) Annual Hot Spot 1 (HS1) eastern bioremediation interim measure (IM) performance monitoring (December 2019); 2) Annual HS1 western bioremediation IM performance monitoring (December 2019); 3) Annual Interim Groundwater Monitoring (IGM) activities (December 2019); 4) Sub-slab soil gas (SSSG) sampling to investigate potential vapor intrusion (VI) (June 2019). Bioremediation IM activities were implemented in two phases at the CRHE. The first event was implemented in November and December 2016 in the eastern portion of HS1. In April and May 2018, bioremediation IM was implemented in the western portion of HS1. The bioremediation IM targeted chlorinated volatile organic compound (CVOC) concentrations greater than 10 times their Florida Department of Environmental Protection (FDEP) Natural Attenuation Default Criteria (NADC). Bioremediation IM activities included the injection of a diluted solution of SRS®-SD (electron donor) and microbial culture (KB-1®) into the subsurface via direct push technology (DPT) injection down to 50 feet below land surface (ft bls).
Andrew Scott Starr↗
New Features in the SPOC Pipeline Release 4.0
The Science Processing Operations Center is in the process of testing and deploying Release 4.0 of the codebase in the March 2019 timeframe. This paper describes the new features of the software and their likely impact on the quality of the TESS science data products. The major goals of Release 4.0 are to improve the extraction of photometry from the pixels in light of the non-uniform pointing performance and the identification of instrumental signatures from the light curves. We also describe modifications to the FFI pipeline to allow the generation of FFI light curves, correction of the instrumental systematics therein, and planet searches, primarily for the purpose of validating the 2-min pipeline against the FFI pipeline, but also to be able to provide cotrending basis vectors (CBVs) derived directly from the FFIs to the public to aid them in their extraction and correction of photometry. We also discuss the improvements in photometric performance of the pipeline and its various components.The lapse in funding experienced between 22 December 2018 and 27 January 2019 significantly delayed our ability to conduct integration testing as planned for late December/early January, delaying the start of V&V by one month to the end of February 2019.The TESS Mission is funded by NASA's Science Mission Directorate as an Astrophysics Explorer Mission.The Science Processing Operations Center is in the process of testing and deplo!"ing Release 4.0 of thecodebase In the March 2019 tlmeframe. This paper describes the new features or the software and theirlikely impact on the quality of the TESS science data products. The major goals or Release 4.0 are to Imtheidentification of instrumental signatures from the light cuNes. We also describe modifications tothe FFI pipeline to allow the generation of FFI light curves, correction of the instrumental systematicstherein, and planet searches, primarily for the purpose of validating the 2-min pipelineagainst the FFI pipeline, but also to be able to provide cotrending basis vectors (C3Vs) ,,.~,.;.derived directly from the FFls to the public to aid them in their extractbn and correction , :-; ~'-'\'.'.of photometry. We also discuss the Improvements In photometric performance• of ~~\":;:•the pipeline and its various components. :,;.-.;The lapse In funding experienced between 22 December 2018 and 27January 2019 significantly delayed our ability to conduct Integrationtesting as planned for late December/early January, delaying thestart of V&V by one month to the end of February 2019.The TESS Mission is funded by NASA's Science Mission Directorateas an Astrophysics Explorer Mission.o.iu.c...,.....~~New Features in SPOC 4.01. Use of quatemions In photometry and centroiding.2. Use of quaternions to Identify high-motion cadences and exclude same.3. Use of the TPS detections to deemphasize pathological cadences ("skyline flattenlng"I.4. Improved CAL calculations for black and smear correction.5. PA brightness metric calculation improvements (induo'e crowding in calrulation)./ • 6. Improved POC spike goodness metric.•~• 7. Improved handling of gaps and momentum duni:>S in POC.8. Improved tuning of POC., 9. Improved attitude tweak correction in PDC.10. Improvements in PDC introduced noise and correlation goodness metrics11. Using the improved spike goodness metric to minimize overlitting in the spikeremover ' • :,''l./ 12. Enable FFI processing through planet search.,,. ,• • "« ,;,,,•.~'A , 13.1 D4.V S mtreinaim-relipnoinrgts d aartcah irveetrdie tvoa Ml aAnSdT p ersistence to database 15. Improved management of jobs on the NAS Pleiadss supercomputer
Jenkins, Jon M.↗
DVB-S2 Demonstration Testing for Enhancing Data Rates for CubeSat/SmallSat Missions
The number of National Aeronautics and Space Administration (NASA) CubeSat/SmallSat missions is expected to grow rapidly in the next decade. High data rate is in increasing demanded for science missions, especially for mother/daughter CubeSat constellations. As the number of spacecraft on a ground network grows, loading could be reduced by limiting contact time per day, which is enabled by higher data rates. There is also a need to communicate direct to earth from space from longer distances than low earth orbit (LEO) with CubeSats. These challenges motivate the need for bandwidth and power efficient modulation and coding techniques. Today, DVB-S2 is an industry communications standard for larger satellites. DVB-S2 uses power and bandwidth efficient modulation and coding techniques to deliver performance approaching theoretical limits of RF channels. NASA Near Earth Network (NEN) is conducting demonstration testing at Wallops Flight Facility (WFF) in spring 2019 for CubeSat/SmallSat missions for enhancing data rate performance in NASAs allocated S-band 5 MHz channel. The ultimate goal is to upgrade NEN with DVB-S2 for increasing science data return, and enabling of greater numbers of CubeSats.This paper describes NEN DVB-S2 demonstration testing objectives and performance measurement results. DVB-S2 data rate performance in the NEN S-band 5 MHz channel is presented. Simulation analysis for the expected maximum data rate performance with the DVB-S2 signal family including multiple modulations and codes are presented. Link margin analysis for a typical CubeSat/SmallSat with a 1W/2W power amplifier/patch antenna using DVB-S2 for NEN S-band is discussed. The demonstration testing configurations at NEN Wallops station is described. Results of the demonstration testing are compared with other evolving radios for SmallSats and CubeSats in term of data rate and performance. There are a number of evolving S-band and X-band radios that are compatible with NEN. Some are integrated with commercial CubeSat/SmallSat busses. Some are flying for the first time with NEN in early 2019. The USRP B200mini transceiver S-band radio is one such radio. Results of streamlined compatibility testing with NEN in 2018, performance with the Technology Educational Satellite (TechEdSat-8) in flight in early 2019, potential to add DVB-S2 to the radio, and plans for a CubeSat using the radio for communication from 10 million kilometers from earth are discussed. Another evolving radio is the Syrlinks X-band radio. Results of compatibility testing with NEN in 2018, performance with the Sustained Ocean Color Observations using Nanosatellites (SOCON) in flight in early 2019, and potential for a future SOCON constellation are discussed.
Wong, Yen↗
Modeling the Transport Processes of a Pair of Solar Energetic Particle Events Observed by Parker Solar Probe near Perihelion
We present model calculations of the transport processes of solar energetic particles in the corona and interplanetary medium for two events detected by Parker Solar Probe near its second perihelion on 2019 April 2 and April 4. In the 2019 April 2 event, the <100 keV proton differential intensity measured by the ISOIS EPI-Lo instrument increased by more than a factor of 10 above the pre-event intensity, whereas the ~1 MeV proton differential intensity detected by the EPI-Hi instrument did not show any intensity enhancement. In the 2019 April 4 event, the ~1 MeV proton intensity showed an increase of more than a factor of 100 above the pre-event intensity, but the <100 keV proton intensity enhancement was rather low and gradual. The observational fact that only the 2019 April 4 event displayed a high-energy intensity enhancement indicates that the associated acceleration process in the second event was more energetic than the first event. However, the gradual and low enhancement of the low-energy proton intensity in the second event is at odds with the scenario. In this paper, we investigate the injection and transport processes of protons in the corona and interplanetary magnetic fields with numerical model simulations. Our model calculations suggest that the gradual and low rise of the low-energy protons in the April 4 event was probably due to the different diffusion conditions in these two events and the fact that the spacecraft was moving away from the source region during these two events.
Solar energetic particles↗
An Updated Secondary Lunar Meteoroid Ejecta Model for Engineering Design
Introduction: The surface of the Moon is constantly being bombarded by a flux of meteoroids of various sizes. Impacts due to these meteoroids produce secondary ejecta material at much lower speeds but with a total mass larger than the original impactor. Details about the secondary ejecta are important for planning missions on the lunar surface. In this work, an updated ejecta model is presented called the Meteoroid Model of Secondary Ejecta (MeMoSeE), to re-place the Apollo-era ejecta model, NASA SP-8013 [1], in the SLS-SPEC 159 Design Specification for Natural Environments (DSNE) [2]. The model pro-duces secondary ejecta flux environments for a user-specified location on the lunar surface, and sorts the incoming secondary flux by angular direction and speed. Methods: MeMoSeE is separated into three parts: the inputs, the conversion step, and the integration of fluxes. Inputs to the model include the primary meteoroid fluxes and the primary near-Earth object (NEO) fluxes. Meteoroid fluxes, both asteroidal and cometary, are calculated using the Meteoroid Engineering Model (MEM3) [3] for different locations on the Moon. For each surface location, an ephemeris is generated using the JPL HORIZONS System [4] that feeds into MEM3. The NEO fluxes are approximated by the high-density population of MEM3 (i.e., only the directionality), where the speed distribution of the NEO fluxes is renormalized to match observations [5]. The regolith properties are used as defined in the DSNE [2]. The conversion step utilizes scaling laws given by Housen & Holsapple 2011 [6] to convert the primary impactor flux to the total mass of secondary ejecta. The ejecta distribution, at the point-of-impact (POI), is separated into a zenith angle and azimuthal angle distribution. The zenith angle distribution follows a beta distribution where the peak depends on the impact altitude angle and the impact azimuth [7]. We employ an ejecta azimuth distribution that is based on Rival & Mandeville 1999 [8] which focuses ejecta in the downstream direction for more oblique impacts. Finally, during the integration step, we sum secondary ejecta number fluxes at a particular region-of-interest (ROI) that originated from many POI locations over the entire surface of the Moon. We keep track of both altitude and azimuth angle bins as well as a range of speed bins, following the igloo gridding as done in MEM3 [9]. The ejecta particle size distribution and density is assumed to be the same as the lunar regolith [10, 2]. Results: The primary fluxes are computed for one Metonic cycle (19 years) for various locations over the lunar surface with a fixed orientation. Both the angular and speed distributions of the primary fluxes are dependent on the latitude and longitude. The over-all primary fluxes show a roughly 13% increase from the eastern limb to the western limb. In general, the speed distribution of the secondary fluxes span from a user-defined minimum speed to the escape speed of the Moon (2.38 km/s), roughly following a power-law relation [6]. Different parts of the speed distribution come from different primary impact locations on the Moon. The secondary ejecta is dominated by the slowest speeds, where these particles originate nearby the ROI. For speeds around 71% of the escape speed, the secondary ejecta originates from locations near the antipodal point. On the other hand, for secondary ejecta speeds that exceed roughly 90% the escape speed, the ejecta particles come from all over the lunar surface to the ROI. Comparing the secondary ejecta fluxes from MeMoSeE with NASA SP-8013 [1], there is a reduction by about 2-3 orders of magnitude for secondary ejecta particles greater than 1 µg. These estimates agree with recent findings from Bjorkman & Christiansen 2019 [11]. The secondary ejecta fluxes are also compared with the primary fluxes, where the ejecta fluxes are roughly an order of magnitude greater than the primary fluxes. References: [1] Cour-Palais, B. G., (1969) NASA SP-8013. [2] NASA SLS-SPEC-159 Rev. H (2020). [3] Moorhead, A. V., et al. (2019) JS&R, 1-17. [4] Giorgini, J. D., (2015) IAUGA, 29, 2256293. [5] Moorhead, A. V., (2020) Memo OSMA/MEO/Lunar-001. [6] Housen, K. R., and Holsapple, K. A., (2011) Icarus, 211(1), 856-875. [7] Gault, D. E., and Wedekind, J. A., (1978) L&PSCP, 9, 3843-3875. [8] Rival, M., and Mandeville, J., (1999) Space Debris, 1(1), 45-57. [9] Moorhead, A. V., (2019) MEM3 User Guide. [10] Carrier, W. D., (1973) The Moon, 6(3-4), 250-263. [11] Bjorkman, M. D. & Christiansen, E. L., (2019) ODC
impacts↗
Satellite Remote Sensing to Assess Cyanobacterial Bloom Frequency Across the United States at Multiple Spatial Scales
Cyanobacterial blooms can have negative effects on human health and local ecosystems. Field monitoring of cyanobacterial blooms can be costly, but satellite remote sensing has shown utility for more efficient spatial and temporal monitoring across the United States. Here, satellite imagery was used to assess the annual frequency of surface cyanobacterial blooms, defined for each satellite pixel as the percentage of images for that pixel throughout the year exhibiting detectable cyanobacteria. Cyanobacterial frequency was assessed across 2,196 large lakes in 46 states across the continental United States (CONUS) using imagery from the European Space Agency’s Ocean and Land Colour Imager for the years 2017 through 2019. In 2019, across all satellite pixels considered, annual bloom frequency had a median value of 4% and a maximum value of 100%, the latter indicating that for those satellite pixels, a cyanobacterial bloom was detected by the satellite sensor for every satellite image considered. In addition to annual pixel-scale cyanobacterial frequency, results were summarized at the lake- and state-scales by averaging annual pixel-scale results across each lake and state. For 2019, average annual lake-scale frequencies also had a maximum value of 100%, and Oregon and Ohio had the highest average annual state-scale frequencies at 65% and 52%. Pixel-scale frequency results can assist in identifying portions of a lake that are more prone to cyanobacterial blooms, while lake- and state-scale frequency results can assist in the prioritization of sampling resources and mitigation efforts. Satellite imagery is limited by the presence of snow and ice, as imagery collected in these conditions are quality flagged and discarded. Thus, annual bloom frequencies within nine climate regions were investigated to determine whether missing data biased results in climate regions more prone to snow and ice, given that their annual summaries would be weighted toward the summer months when cyanobacterial blooms tend to occur. Results were unbiased by the time period selected in most climate regions, but a large bias was observed for the Northwest Rockies and Plains climate region. Moderate biases were observed for the Ohio Valley and the Southeast climate regions. Finally, a clustering analysis was used to identify areas of high and low cyanobacterial frequency across CONUS based on average annual lake-scale cyanobacterial frequencies for 2019. Several clusters were identified that transcended state, watershed, and eco-regional boundaries. Combined with additional data, results from the clustering analysis may offer insight regarding large-scale drivers of cyanobacterial blooms.
remote sensing↗
GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run
We report on gravitational wave discoveries from compact binary coalescences detected by Advanced LIGO and Advanced Virgo in the first half of the third observing run (O3a) between 1 April 2019 15:00 UTC and 1 October 2019 15:00 UTC. By imposing a false-alarm-rate threshold of two per year in each of the four search pipelines that constitute our search, we present 39 candidate gravitational wave events. At this threshold, we expect a contamination fraction of less than 10%. Of these, 26 candidate events were reported previously in near real-time through GCN Notices and Circulars; 13 are reported here for the first time. The catalog contains events whose sources are black hole binary mergers up to a redshift of ~ 0.8, as well as events whose components could not be unambiguously identified as black holes or neutron stars. For the latter group, we are unable to determine the nature based on estimates of the component masses and spins from gravitational wave data alone. The range of candidate event masses which are unambiguously identified as binary black holes (both objects ≥ 3 M⨀) is increased compared to GWTC-1, with total masses from ∼ 14M⨀ for GW190924 021846 to ∼ 150M⨀ for GW190521. For the first time, this catalog includes binary systems with significantly asymmetric mass ratios, which had not been observed in data taken before April 2019. We also find that 11 of the 39 events detected since April 2019 have positive effective inspiral spins under our default prior (at 90% credibility), while none exhibit negative effective inspiral spin. Given the increased sensitivity of Advanced LIGO and Advanced Virgo, the detection of 39 candidate events in ∼26 weeks of data (∼1.5 per week) is consistent with GWTC-1.
Gravitational wave detection↗
A Combined Al-Mg/Pb-Pb Age of the Solar System
Astrophysical models of planet formation and protoplanetary disk evolution demand precise and accurate timing of the sequence of events in the solar nebula, relative to a time t=0, usually taken to be during the short epoch of CAI (Ca-rich, Al-rich inclusion) formation. Most CAIs formed withlive26Al (mean-life τ26= 1.034 Myr [1]), with an abundance 26Al/27Al ≈ (26Al/27Al)SS= 5.23 × 10-5[2]. We adopt this as the widespread level of 26Al in the solar nebula at t=0. Assuming spatial homogeneity of 26Al, an inclusion that had less 26Al, (26Al/27Al)0, formed a time Δt26= τ26ln[(26Al/27Al)SS/ (26Al/27Al)0] after t=0.These ages are typical precise to within ±0.1 Myr. Igneous bulk meteorites and inclusions can be relatively dated by the Al-Mg chronometer, but only ifΔt26<6 Myr. The Pb-Pb system is useful as a longer relative chronometer. It yields absolute ages tPb using 207Pb/206Pb, 206Pb/204Pb, and 238U/235U ratios measured indifferent portions of a sample, assuming certain half-lives [4]. These absolute ages are uncertain to within ±9 Myr due to uncertainties in the 235U half-life[3], but times of formation ΔtPb= tCAI–tPb relative to t=0, are more precise(±0.5Myr),iftCAI can be found. Here, tCAI means the Pb-Pb age that would be measured in CAIs using the half-lives the community typically uses, if they achieved isotopic closure at t=0. Unfortunately, direct Pb-Pb dating of CAIs has not definitively determined tCAI. Based on four CAIs with canonical (26Al/27Al)0,[5,6] found tPb= 4567.30 ± 0.16 Myr. No other CAI ages with measured 238U/235U have been reported in the refereed literature, but there are hints of other CAIs with ages tPb= 4568.0 ± 0.2 Myr [7] and tPb= 4568.3 ± 0.2 Myr [8].It is unclear whether anyof these igneous type B CAIs isotopically closed at t=0 or represents tCAI. Instead of measurements, we advocate finding tCAI by minimizing the discrepancies between the Al-Mg and Pb-Pb chronometers. Assuming Δt26=ΔtPb, we find the implied t’CAI= tPb+Δt26, then define t*CAIas the weighted mean of the t’CAI. t*CAIis the best guess for the Pb-Pb age of t=0; the assumption of homogeneity is justified if the t’CAI cluster within errors around t*CAI. This statistical approach is similar to, but improves on, that of[9]. We find t*CAI= 4568.73 ± 0.16 Myr. Below we discuss our methodology and the implications of this age for CAIs, 1.4 Myr older than the reported and typically used age 4567.30±0.16 Myr. Methods: We base our estimate of t*CAIon five achondrites for which published (26Al/27Al)0and Pb-Pb ages exist: the quenched angrites D’Orbigny, Sahara 99555 (SAH 99555), and Northwest Africa (NWA) 1670; the pseudo-eucrite Asuka 881394; and the inner disk achondrite. All are “NC” (non-carbonaceous) achondrites that likely cooled quickly enough that the Al-Mg and Pb-Pb systems achieved isotopic closure simultaneously. We also considered the “CC” (carbonaceous chondrite-like) achondrites NWA 2796 and NWA 6704, butdo not include them in our fit. Al-Mg and Pb-Pb seem not to have closed simultaneously, possibly because formation in the outer disk from volatile-rich composition led to slower cooling. Of the 8 chondrules from NWA 5697 measured by [20,21], we also consider the 4 for which 238U/235U was measured: 2-C1, 5-C2, 3-C5, 11-C1.Depending on their post-formation thermal histories, the Al-Mg and Pb-Pb systems in chondrules may or may not have closed simultaneously. Table 1: (26Al/27Al)0, Pb-Pb ages of selected samplesSample(26Al/27Al)0/ 10-6RefPb-PbRefD’Orbigny3.98±0.15104563.43±0.19♮10-12SAH 995553.64±0.18104563.88±0.2712NWA16705.92±0.59104564.39±0.24*10Asuka 88139413.1±0.5613-154564.98±0.1715NWA 73253.03±0.14164563.4±2.616NWA 27963.94±0.16174562.89±0.5917NWA 67043.03±0.14184562.76±0.26192-C17.56±1.53204567.57±0.56*215-C27.04±1.51204567.54±0.52*213-C58.85±1.83204566.20±0.63*2111-C15.55±1.84204565.84±0.72*21*regression based on one subset of data points ♮weighted mean of two datasets Pb-Pb ages are proportional to the intercept of the line formed by linear regression of 207Pb/206Pb vs. 206Pb/204Pb data from various washes, leachates and residues of acid dissolution of a sample. Because contamination by terrestrial or primordial Pb is pervasive, some fractions must be excluded from regressions to ensure a fit with acceptable mean squares weighted deviation (MSWD). Usually points are excluded based on low [Pb], or low 206Pb/204Pb ratio(low radiogenic component), with single outliers identified [11,12,15,16,17]. In the starred examples (Table 1)and the case of 3 CAI Pb-Pb ages [5], up to half the points were excluded solely because did not fit a pre-determined line. This approach is vulnerable to confirmation bias and produces fits with low MSWD and too-low Pb-Pb age uncertainty. Regressing the same data points as [10], were produce the Pb-Pb age of NWA 1670 of 4564.39±0.24 Myr. But selecting other combinations of data points, other, equally valid, isochrons yield ages from 4563.77±0.21 Myr to 4564.64±0.23 Myr. Similar arguments apply to the Pb-Pb isochrons built by [21] for chondrules 2-C1 (we find 4567.33±0.44 to 4567.85±0.46 Myr), 5-C2 (4566.84±0.53 to 4567.70±0.44 Myr), 3-C5 (4565.84±0.54to 4567.04±0.54)and 11-C1 (4565.36±0.51 to 4565.74±0.45 Myr). Our adopted ages for these and NWA 1670 are listed in Table 2.Table 2. tCAI estimated from various components, using our regressions for the chondrules & NWA 1670.SampleΔt26(Myr)tPb(Myr)t’CAI(Myr)D’Orbigny5.05±0.044563.43±0.194568.48±0.19SAH 995555.14±0.054563.88±0.274569.02±0.27NWA16704.64±0.104564.21±0.634568.85±0.67Asuka 8813943.81±0.044564.98±0.174568.79±0.17NWA 73255.33±0.054563.4±2.64568.7±2.6NWA 27965.06±0.044562.89±0.594567.95±0.59NWA 67045.29±0.134562.76±0.264568.05±0.292-C12.00±0.214567.59±0.704569.59±0.725-C22.07±0.224567.23±0.914569.30±0.933-C51.84±0.214566.44±1.124568.28±1.1411-C12.32±0.344565.52±0.664567.84±0.73achondrite4568.72±0.16chondrules4568.76±0.58combined4568.73±0.16A weighted average of the five NC achondrites(or just D’Orbigny, SAH 99555 and Asuka 881394)yields t*CAI= 4568.72 ± 0.16Myr. All are consistent with this value to within 1.8σ, and MSWD=1.5. Including the 4 U-corrected chondrules, t*CAI= 4568.73± 0.16Myrwith MSWD=1.66, which is statistically significant. All chondrules and NC achondrites are consistent with this to within 1.8σ, (Figure 1).Figure 1. Al-Mg formation times after t=0 vs. Pb-Pb ages. The five NC achondrites and four chondrules are consistent with a Pb-Pb age of t=0 of 4568.7 Myr. Discussion: The data from achondrites and chondrules are consistent with a single Pb-Pb age at t=0, justifying the assumption of 26Al homogeneity. The age, 4568.7 Myr, is ≈1.4 Myr older than the commonly accepted Pb-Pb age of CAIs that formed with canonical 26Al/27Al at t=0 [3]. Others have interpreted the discrepancy to signify 26Al heterogeneity in the CAI-forming region[5,21]. We suggest instead that CAIs were exposed to transient heating events that reset the Pb-Pb system without disturbing the Al-Mg system. Notably, chondrules typically experienced transient heating at these times in the nebula [22]. If so, direct measurements of CAIs will not yield as reliable a Pb-Pb age of t=0 as statistical approaches like this and that of [9].References:[1] Auer et al. 2009. [2] Jacobsen, B et al. 2008, EPSL 272, 353-364. [3] Tissot, Fet al. 2017, GCA 213, 593-617.[4] Villa, I et al. 2016, GCA 172, 387-392.[5] Amelin, Y et al. 2010, EPSL 300, 343-350.[6] Connelly, Jet al. 2012, Science 338, 651.[7] Bouvier, Aet al. 2011, LPICo 1639, 9054. [8] Bouvier, Aand Wadhwa, M2010, Nat Geosci 3, 637-641.[9] Nyquist, Let al. 2009, GCA 73, 5115-5136.[10] Schiller et al. 2015. [11] Wadhwa & Brennecka 2012. [12] Tissot et al. 2017. [13] Nyquist et al. 2003. [14] Wadhwa et al. 2009. [15] Wimpenny et al. 2019, GCA 244, 478-501.[16] Koefoed et al. 2016, GCA 183, 31-45. [17] Bouvier, A et al. 2011, GCA 75, 5310-5323. [18] Sanborn, Met al. 2019, GCA 245, 577-596. [19] Amelin, Yet al. 2019, GCA 245, 628-642.[20] Bollard, Jet al. 2017, Sci Adv 3 ,e1700407. [21] Bollard, Jet al. 2019, GCA 260, 62-83.[22] Villeneuve, J et al. 2009, Science 325, 985
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