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Showing papers by "Goddard Space Flight Center published in 2021"


Journal ArticleDOI
Lennart Lindegren1, Sergei A. Klioner2, Jose M Hernandez3, Alex Bombrun3, M. Ramos-Lerate3, H. Steidelmüller2, Ulrich Bastian4, M. Biermann4, A. de Torres3, E. Gerlach2, R. Geyer2, Thomas Hilger2, David Hobbs1, U. Lammers3, Paul J. McMillan1, C.A. Stephenson3, J. Castañeda5, Michael Davidson6, C. Fabricius5, G. Gracia-Abril4, Jordi Portell5, Nicholas Rowell6, David Teyssier3, F. Torra5, S. Bartolomé5, M. Clotet5, N. Garralda5, J.J. González-Vidal5, J. Torra5, U. Abbas7, Martin Altmann8, Martin Altmann4, E. Anglada Varela3, L. Balaguer-Núñez5, Zoltan Balog4, Zoltan Balog9, C. Barache8, Ugo Becciani7, M. Bernet5, Stefano Bertone10, Stefano Bertone7, Stefano Bertone11, Luciana Bianchi, S. Bouquillon8, Anthony G. A. Brown12, Beatrice Bucciarelli7, D. Busonero7, A. G. Butkevich7, R. Buzzi7, Rossella Cancelliere13, T. Carlucci8, Patrick Charlot14, Maria-Rosa L. Cioni15, Mariateresa Crosta7, C. Crowley3, E. F. del Peloso4, E. del Pozo3, Ronald Drimmel7, P. Esquej3, Agnes Fienga14, Agnes Fienga8, E. Fraile3, Mario Gai7, M. Garcia-Reinaldos3, Raphael Guerra3, Nigel Hambly6, M. Hauser9, K. Janßen15, Stefan Jordan4, Z. Kostrzewa-Rutkowska12, Z. Kostrzewa-Rutkowska16, Massimiliano Lattanzi13, Massimiliano Lattanzi7, S. Liao7, E. Licata7, Tim Lister17, W. Löffler4, Jon Marchant18, A. Masip5, Francois Mignard14, Alexey Mints15, D. Molina5, Alcione Mora3, Roberto Morbidelli7, C. P. Murphy3, C. Pagani19, Pasquale Panuzzo8, X. Peñalosa Esteller5, E. Poggio7, P. Re Fiorentin7, Alberto Riva7, A. Sagristà Sellés4, V. Sanchez Gimenez5, M. Sarasso7, Eva Sciacca7, H. I. Siddiqui20, Richard L. Smart7, D. Souami21, D. Souami8, Alessandro Spagna7, Iain A. Steele18, F. Taris8, E. Utrilla3, W. van Reeven3, Alberto Vecchiato7 
TL;DR: Gaia Early Data Release 3 (Gaia EDR3) as mentioned in this paper contains results for 1.812 billion sources in the magnitude range G = 3-21 based on observations collected by the European Space Agency Gaia satellite during the first 34 months of its operational phase.
Abstract: Context. Gaia Early Data Release 3 (Gaia EDR3) contains results for 1.812 billion sources in the magnitude range G = 3–21 based on observations collected by the European Space Agency Gaia satellite during the first 34 months of its operational phase.Aims. We describe the input data, the models, and the processing used for the astrometric content of Gaia EDR3, as well as the validation of these results performed within the astrometry task.Methods. The processing broadly followed the same procedures as for Gaia DR2, but with significant improvements to the modelling of observations. For the first time in the Gaia data processing, colour-dependent calibrations of the line- and point-spread functions have been used for sources with well-determined colours from DR2. In the astrometric processing these sources obtained five-parameter solutions, whereas other sources were processed using a special calibration that allowed a pseudocolour to be estimated as the sixth astrometric parameter. Compared with DR2, the astrometric calibration models have been extended, and the spin-related distortion model includes a self-consistent determination of basic-angle variations, improving the global parallax zero point.Results. Gaia EDR3 gives full astrometric data (positions at epoch J2016.0, parallaxes, and proper motions) for 1.468 billion sources (585 millionwith five-parameter solutions, 882 million with six parameters), and mean positions at J2016.0 for an additional 344 million.Solutions with five parameters are generally more accurate than six-parameter solutions, and are available for 93% of the sources brighter than the 17th magnitude. The median uncertainty in parallax and annual proper motion is 0.02–0.03 mas at magnitude G = 9–14, and around 0.5 mas at G = 20. Extensive characterisation of the statistical properties of the solutions is provided, including the estimated angular power spectrum of parallax bias from the quasars.

475 citations


Journal ArticleDOI
Richard J. Abbott1, T. D. Abbott2, Sheelu Abraham3, Fausto Acernese4  +1428 moreInstitutions (155)
TL;DR: In this article, the population of 47 compact binary mergers detected with a false-alarm rate of 0.614 were dynamically assembled, and the authors found that the BBH rate likely increases with redshift, but not faster than the star formation rate.
Abstract: We report on the population of 47 compact binary mergers detected with a false-alarm rate of 0.01 are dynamically assembled. Third, we estimate merger rates, finding RBBH = 23.9-+8.614.3 Gpc-3 yr-1 for BBHs and RBNS = 320-+240490 Gpc-3 yr-1 for binary neutron stars. We find that the BBH rate likely increases with redshift (85% credibility) but not faster than the star formation rate (86% credibility). Additionally, we examine recent exceptional events in the context of our population models, finding that the asymmetric masses of GW190412 and the high component masses of GW190521 are consistent with our models, but the low secondary mass of GW190814 makes it an outlier.

468 citations


Journal ArticleDOI
Richard J. Abbott1, T. D. Abbott2, Sheelu Abraham3, Fausto Acernese4  +1692 moreInstitutions (195)
TL;DR: In this article, the authors reported the observation of gravitational waves from two compact binary coalescences in LIGO's and Virgo's third observing run with properties consistent with neutron star-black hole (NSBH) binaries.
Abstract: We report the observation of gravitational waves from two compact binary coalescences in LIGO’s and Virgo’s third observing run with properties consistent with neutron star–black hole (NSBH) binaries. The two events are named GW200105_162426 and GW200115_042309, abbreviated as GW200105 and GW200115; the first was observed by LIGO Livingston and Virgo and the second by all three LIGO–Virgo detectors. The source of GW200105 has component masses 8.9−1.5+1.2 and 1.9−0.2+0.3M⊙ , whereas the source of GW200115 has component masses 5.7−2.1+1.8 and 1.5−0.3+0.7M⊙ (all measurements quoted at the 90% credible level). The probability that the secondary’s mass is below the maximal mass of a neutron star is 89%–96% and 87%–98%, respectively, for GW200105 and GW200115, with the ranges arising from different astrophysical assumptions. The source luminosity distances are 280−110+110 and 300−100+150Mpc , respectively. The magnitude of the primary spin of GW200105 is less than 0.23 at the 90% credible level, and its orientation is unconstrained. For GW200115, the primary spin has a negative spin projection onto the orbital angular momentum at 88% probability. We are unable to constrain the spin or tidal deformation of the secondary component for either event. We infer an NSBH merger rate density of 45−33+75Gpc−3yr−1 when assuming that GW200105 and GW200115 are representative of the NSBH population or 130−69+112Gpc−3yr−1 under the assumption of a broader distribution of component masses.

374 citations


Journal ArticleDOI
TL;DR: In this article, the authors reported a radius measurement based on fits of rotating hot spot patterns to Neutron Star Interior Composition Explorer (NICER) and X-ray Multi-Mirror (XMM-Newton) observations.
Abstract: PSR J0740$+$6620 has a gravitational mass of $2.08\pm 0.07~M_\odot$, which is the highest reliably determined mass of any neutron star. As a result, a measurement of its radius will provide unique insight into the properties of neutron star core matter at high densities. Here we report a radius measurement based on fits of rotating hot spot patterns to Neutron Star Interior Composition Explorer (NICER) and X-ray Multi-Mirror (XMM-Newton) X-ray observations. We find that the equatorial circumferential radius of PSR J0740$+$6620 is $13.7^{+2.6}_{-1.5}$ km (68%). We apply our measurement, combined with the previous NICER mass and radius measurement of PSR J0030$+$0451, the masses of two other $\sim 2~M_\odot$ pulsars, and the tidal deformability constraints from two gravitational wave events, to three different frameworks for equation of state modeling, and find consistent results at $\sim 1.5-3$ times nuclear saturation density. For a given framework, when all measurements are included the radius of a $1.4~M_\odot$ neutron star is known to $\pm 4$% (68% credibility) and the radius of a $2.08~M_\odot$ neutron star is known to $\pm 5$%. The full radius range that spans the $\pm 1\sigma$ credible intervals of all the radius estimates in the three frameworks is $12.45\pm 0.65$ km for a $1.4~M_\odot$ neutron star and $12.35\pm 0.75$ km for a $2.08~M_\odot$ neutron star.

365 citations


Journal ArticleDOI
TL;DR: In this article, the authors estimate the radius, mass, and hot surface regions of the massive millisecond pulsar PSR J0740$+$6620, conditional on pulse profile modeling of Neutron Star Interior Composition Explorer X-ray Timing Instrument (NICER XTI) event data.
Abstract: We report on Bayesian estimation of the radius, mass, and hot surface regions of the massive millisecond pulsar PSR J0740$+$6620, conditional on pulse-profile modeling of Neutron Star Interior Composition Explorer X-ray Timing Instrument (NICER XTI) event data. We condition on informative pulsar mass, distance, and orbital inclination priors derived from the joint NANOGrav and CHIME/Pulsar wideband radio timing measurements of arXiv:2104.00880. We use XMM European Photon Imaging Camera spectroscopic event data to inform our X-ray likelihood function. The prior support of the pulsar radius is truncated at 16 km to ensure coverage of current dense matter models. We assume conservative priors on instrument calibration uncertainty. We constrain the equatorial radius and mass of PSR J0740$+$6620 to be $12.39_{-0.98}^{+1.30}$ km and $2.072_{-0.066}^{+0.067}$ M$_{\odot}$ respectively, each reported as the posterior credible interval bounded by the 16% and 84% quantiles, conditional on surface hot regions that are non-overlapping spherical caps of fully-ionized hydrogen atmosphere with uniform effective temperature; a posteriori, the temperature is $\log_{10}(T$ [K]$)=5.99_{-0.06}^{+0.05}$ for each hot region. All software for the X-ray modeling framework is open-source and all data, model, and sample information is publicly available, including analysis notebooks and model modules in the Python language. Our marginal likelihood function of mass and equatorial radius is proportional to the marginal joint posterior density of those parameters (within the prior support) and can thus be computed from the posterior samples.

353 citations


Journal ArticleDOI
TL;DR: In this paper, the Global Ecosystem Dynamics Investigation (GEDI) lidar instrument onboard the International Space Station has been collecting unique data on vegetation structure since April 2019, and the authors employed global Landsat analysis-ready data to extrapolate GEDI footprint-level forest canopy height measurements.

326 citations


Journal ArticleDOI
Richard J. Abbott1, T. D. Abbott2, Sheelu Abraham3, Fausto Acernese4  +1335 moreInstitutions (144)
TL;DR: The data recorded by these instruments during their first and second observing runs are described, including the gravitational-wave strain arrays, released as time series sampled at 16384 Hz.

320 citations


Journal ArticleDOI
TL;DR: Li et al. as mentioned in this paper proposed a Space-Time Extra-Trees (STET) model to capture the spatiotemporal variations of PM2.5 at different spatial scales.

311 citations


Journal ArticleDOI
TL;DR: In this paper, the authors integrated ground and Earth observation data to map annual forest-related greenhouse gas emissions and removals globally at a spatial resolution of 30'm over the years 2001-2019.
Abstract: Managing forests for climate change mitigation requires action by diverse stakeholders undertaking different activities with overlapping objectives and spatial impacts. To date, several forest carbon monitoring systems have been developed for different regions using various data, methods and assumptions, making it difficult to evaluate mitigation performance consistently across scales. Here, we integrate ground and Earth observation data to map annual forest-related greenhouse gas emissions and removals globally at a spatial resolution of 30 m over the years 2001–2019. We estimate that global forests were a net carbon sink of −7.6 ± 49 GtCO2e yr−1, reflecting a balance between gross carbon removals (−15.6 ± 49 GtCO2e yr−1) and gross emissions from deforestation and other disturbances (8.1 ± 2.5 GtCO2e yr−1). The geospatial monitoring framework introduced here supports climate policy development by promoting alignment and transparency in setting priorities and tracking collective progress towards forest-specific climate mitigation goals with both local detail and global consistency.

275 citations


Journal ArticleDOI
TL;DR: In this article, the authors present a metadata analysis of methane fluxes from all major natural, impacted and human-made aquatic ecosystems and conclude that aquatic emissions will probably increase due to urbanization, eutrophication and positive climate feedbacks.
Abstract: Atmospheric methane is a potent greenhouse gas that plays a major role in controlling the Earth’s climate The causes of the renewed increase of methane concentration since 2007 are uncertain given the multiple sources and complex biogeochemistry Here, we present a metadata analysis of methane fluxes from all major natural, impacted and human-made aquatic ecosystems Our revised bottom-up global aquatic methane emissions combine diffusive, ebullitive and/or plant-mediated fluxes from 15 aquatic ecosystems We emphasize the high variability of methane fluxes within and between aquatic ecosystems and a positively skewed distribution of empirical data, making global estimates sensitive to statistical assumptions and sampling design We find aquatic ecosystems contribute (median) 41% or (mean) 53% of total global methane emissions from anthropogenic and natural sources We show that methane emissions increase from natural to impacted aquatic ecosystems and from coastal to freshwater ecosystems We argue that aquatic emissions will probably increase due to urbanization, eutrophication and positive climate feedbacks and suggest changes in land-use management as potential mitigation strategies to reduce aquatic methane emissions Methane emissions from aquatic systems contribute approximately half of global methane emissions, according to meta-analysis of natural, impacted and human-made aquatic ecosystems and indicating potential mitigation strategies to reduce emissions

239 citations


Journal ArticleDOI
01 Aug 2021-Nature
TL;DR: In this paper, the authors used daily satellite imagery at 250-metre resolution to estimate flood extent and population exposure for 913 large flood events from 2000 to 2018, and determined a total inundation area of 2.23 million square kilometres with 255-290 million people directly affected by floods.
Abstract: Flooding affects more people than any other environmental hazard and hinders sustainable development1,2. Investing in flood adaptation strategies may reduce the loss of life and livelihood caused by floods3. Where and how floods occur and who is exposed are changing as a result of rapid urbanization4, flood mitigation infrastructure5 and increasing settlements in floodplains6. Previous estimates of the global flood-exposed population have been limited by a lack of observational data, relying instead on models, which have high uncertainty3,7–11. Here we use daily satellite imagery at 250-metre resolution to estimate flood extent and population exposure for 913 large flood events from 2000 to 2018. We determine a total inundation area of 2.23 million square kilometres, with 255–290 million people directly affected by floods. We estimate that the total population in locations with satellite-observed inundation grew by 58–86 million from 2000 to 2015. This represents an increase of 20 to 24 per cent in the proportion of the global population exposed to floods, ten times higher than previous estimates7. Climate change projections for 2030 indicate that the proportion of the population exposed to floods will increase further. The high spatial and temporal resolution of the satellite observations will improve our understanding of where floods are changing and how best to adapt. The global flood database generated from these observations will help to improve vulnerability assessments, the accuracy of global and local flood models, the efficacy of adaptation interventions and our understanding of the interactions between landcover change, climate and floods. Satellite imagery for the period 2000–2018 reveals that population growth was greater in flood-prone regions than elsewhere, thus exposing a greater proportion of the population to floods.

Journal ArticleDOI
TL;DR: The International Geomagnetic Reference Field (IGRF) was adopted by the IGA Division V Working Group (V-MOD) in 2019 as discussed by the authors, which provides the equations defining the IGRF, the spherical harmonic coefficients for this thirteenth generation model, maps of magnetic declination, inclination, and total field intensity for the epoch 2020.0, and maps of their predicted rate of change for the 2020 to 2025.0 time period.
Abstract: In December 2019, the International Association of Geomagnetism and Aeronomy (IAGA) Division V Working Group (V-MOD) adopted the thirteenth generation of the International Geomagnetic Reference Field (IGRF). This IGRF updates the previous generation with a definitive main field model for epoch 2015.0, a main field model for epoch 2020.0, and a predictive linear secular variation for 2020.0 to 2025.0. This letter provides the equations defining the IGRF, the spherical harmonic coefficients for this thirteenth generation model, maps of magnetic declination, inclination and total field intensity for the epoch 2020.0, and maps of their predicted rate of change for the 2020.0 to 2025.0 time period.

Journal ArticleDOI
TL;DR: This commentary is a call to action for the hydrology community to focus on developing a quantitative understanding of where and when hydrological process understanding is valuable in a modeling discipline increasingly dominated by machine learning.
Abstract: We suggest that there is a potential danger to the hydrological sciences community in not recognizing how transformative machine learning will be for the future of hydrological modeling. Given the recent success of machine learning applied to modeling problems, it is unclear what the role of hydrological theory might be in the future. We suggest that a central challenge in hydrology right now should be to clearly delineate where and when hydrological theory adds value to prediction systems. Lessons learned from the history of hydrological modeling motivate several clear next steps toward integrating machine learning into hydrological modeling workflows.

Journal ArticleDOI
B. P. Abbott1, Richard J. Abbott1, T. D. Abbott2, Sheelu Abraham3  +1273 moreInstitutions (140)
TL;DR: In this article, the first and second observing runs of the Advanced LIGO and Virgo detector network were used to obtain the first standard-siren measurement of the Hubble constant (H 0).
Abstract: This paper presents the gravitational-wave measurement of the Hubble constant (H 0) using the detections from the first and second observing runs of the Advanced LIGO and Virgo detector network. The presence of the transient electromagnetic counterpart of the binary neutron star GW170817 led to the first standard-siren measurement of H 0. Here we additionally use binary black hole detections in conjunction with galaxy catalogs and report a joint measurement. Our updated measurement is H 0 = km s−1 Mpc−1 (68.3% of the highest density posterior interval with a flat-in-log prior) which is an improvement by a factor of 1.04 (about 4%) over the GW170817-only value of km s−1 Mpc−1. A significant additional contribution currently comes from GW170814, a loud and well-localized detection from a part of the sky thoroughly covered by the Dark Energy Survey. With numerous detections anticipated over the upcoming years, an exhaustive understanding of other systematic effects are also going to become increasingly important. These results establish the path to cosmology using gravitational-wave observations with and without transient electromagnetic counterparts.

Journal ArticleDOI
19 Mar 2021-Science
TL;DR: Using spectral matched filtering of radio data from the Green Bank Telescope, this article detected two nitrile-group-functionalized polycyclic aromatic hydrocarbons (PAHs) in the interstellar medium.
Abstract: Unidentified infrared emission bands are ubiquitous in many astronomical sources. These bands are widely, if not unanimously, attributed to collective emissions from polycyclic aromatic hydrocarbon (PAH) molecules, yet no single species of this class has been identified in space. Using spectral matched filtering of radio data from the Green Bank Telescope, we detected two nitrile-group-functionalized PAHs, 1- and 2-cyanonaphthalene, in the interstellar medium. Both bicyclic ring molecules were observed in the TMC-1 molecular cloud. In this paper, we discuss potential in situ gas-phase PAH formation pathways from smaller organic precursor molecules.

Journal ArticleDOI
Richard J. Abbott1, T. D. Abbott2, Sheelu Abraham3, Fausto Acernese4  +1678 moreInstitutions (193)
TL;DR: In this article, the authors report results of a search for an isotropic gravitational-wave background (GWB) using data from Advanced LIGO's and Advanced Virgo's third observing run (O3) combined with upper limits from the earlier O1 and O2 runs.
Abstract: We report results of a search for an isotropic gravitational-wave background (GWB) using data from Advanced LIGO’s and Advanced Virgo’s third observing run (O3) combined with upper limits from the earlier O1 and O2 runs. Unlike in previous observing runs in the advanced detector era, we include Virgo in the search for the GWB. The results of the search are consistent with uncorrelated noise, and therefore we place upper limits on the strength of the GWB. We find that the dimensionless energy density Ω GW ≤ 5.8 × 10 − 9 at the 95% credible level for a flat (frequency-independent) GWB, using a prior which is uniform in the log of the strength of the GWB, with 99% of the sensitivity coming from the band 20–76.6 Hz; Ω GW ( f ) ≤ 3.4 × 10 − 9 at 25 Hz for a power-law GWB with a spectral index of 2 / 3 (consistent with expectations for compact binary coalescences), in the band 20–90.6 Hz; and Ω GW ( f ) ≤ 3.9 × 10 − 10 at 25 Hz for a spectral index of 3, in the band 20–291.6 Hz. These upper limits improve over our previous results by a factor of 6.0 for a flat GWB, 8.8 for a spectral index of 2 / 3 , and 13.1 for a spectral index of 3. We also search for a GWB arising from scalar and vector modes, which are predicted by alternative theories of gravity; we do not find evidence of these, and place upper limits on the strength of GWBs with these polarizations. We demonstrate that there is no evidence of correlated noise of magnetic origin by performing a Bayesian analysis that allows for the presence of both a GWB and an effective magnetic background arising from geophysical Schumann resonances. We compare our upper limits to a fiducial model for the GWB from the merger of compact binaries, updating the model to use the most recent data-driven population inference from the systems detected during O3a. Finally, we combine our results with observations of individual mergers and show that, at design sensitivity, this joint approach may yield stronger constraints on the merger rate of binary black holes at z ≳ 2 than can be achieved with individually resolved mergers alone.

Journal ArticleDOI
TL;DR: In this article, the authors evaluated the temporal dynamics of 18 state-of-the-art near-surface soil moisture products, including six based on satellite retrievals, 6 based on models without satellite data assimilation (referred to hereafter as open-loop models), and 6 models that assimilate satellite soil moisture or brightness temperature data.
Abstract: . Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as open-loop models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo and six estimates from the HBV model with three precipitation inputs (ERA5, IMERG, and MSWEP) and with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5-cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. The median R ± interquartile range across all sites and products in each category was 0.66 ± 0.30 for the satellite products, 0.69 ± 0.25 for the open-loop models, and 0.72 ± 0.22 for the models with satellite data assimilation. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3E, SMOS, AMSR2, and ASCAT, with the L-band-based SMAPL3E (median R of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCI), MeMo performed better on average (median R of 0.72 versus 0.67), mainly due to the inclusion of SMAPL3E. The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.

Journal ArticleDOI
Natalia Guerrero1, Sara Seager1, Chelsea X. Huang1, Andrew Vanderburg2, Andrew Vanderburg3, Aylin Garcia Soto4, Ismael Mireles1, Katharine Hesse1, William Fong1, Ana Glidden1, Avi Shporer1, David W. Latham5, Karen A. Collins5, Samuel N. Quinn5, Jennifer Burt6, Diana Dragomir7, Ian J. M. Crossfield1, Roland Vanderspek1, Michael Fausnaugh1, Christopher J. Burke1, George R. Ricker1, Tansu Daylan1, Zahra Essack1, Maximilian N. Günther1, H. P. Osborn8, H. P. Osborn1, Joshua Pepper9, Pamela Rowden10, Lizhou Sha1, Steven Villanueva1, Daniel A. Yahalomi11, Liang Yu1, Sarah Ballard12, Natalie M. Batalha13, David Berardo1, Ashley Chontos, Jason A. Dittmann1, Gilbert A. Esquerdo5, Thomas Mikal-Evans1, Rahul Jayaraman1, Akshata Krishnamurthy1, Dana R. Louie14, Nicholas Mehrle1, Prajwal Niraula1, Benjamin V. Rackham1, Joseph E. Rodriguez5, Stephen J. L. Rowden15, Clara Sousa-Silva1, David Watanabe, Ian Wong1, Zhuchang Zhan1, Goran Zivanovic1, Jessie L. Christiansen6, David R. Ciardi6, M. Swain6, Michael B. Lund6, Susan E. Mullally16, Scott W. Fleming16, David R. Rodriguez16, Patricia T. Boyd17, Elisa V. Quintana17, Thomas Barclay18, Thomas Barclay17, Knicole D. Colón17, S. Rinehart17, Joshua E. Schlieder17, Mark Clampin17, Jon M. Jenkins19, Joseph D. Twicken19, Joseph D. Twicken20, Douglas A. Caldwell20, Douglas A. Caldwell19, Jeffrey L. Coughlin19, Jeffrey L. Coughlin20, Chris Henze19, Jack J. Lissauer19, Robert L. Morris19, Robert L. Morris20, Mark E. Rose19, Jeffrey C. Smith19, Jeffrey C. Smith20, Peter Tenenbaum20, Peter Tenenbaum19, Eric B. Ting19, Bill Wohler19, Bill Wohler20, Gáspár Á. Bakos21, Jacob L. Bean22, Zachory K. Berta-Thompson23, Allyson Bieryla5, Luke G. Bouma21, Lars A. Buchhave24, Nathaniel R. Butler25, David Charbonneau5, John P. Doty, Jian Ge12, Matthew J. Holman5, Andrew W. Howard6, Lisa Kaltenegger26, Stephen R. Kane27, Hans Kjeldsen28, Laura Kreidberg29, Douglas N. C. Lin13, Charlotte Minsky1, Norio Narita, Martin Paegert5, András Pál, Enric Palle30, Dimitar Sasselov5, Alton Spencer31, Alessandro Sozzetti32, Keivan G. Stassun33, Keivan G. Stassun34, Guillermo Torres5, Stéphane Udry35, Joshua N. Winn21 
TL;DR: In this article, the authors presented 2241 exoplanet candidates identified with data from the Transiting Exoplanet Survey Satellite (TESS) during its 2-year Prime Mission.
Abstract: We present 2241 exoplanet candidates identified with data from the Transiting Exoplanet Survey Satellite (TESS) during its 2 yr Prime Mission. We list these candidates in the TESS Objects of Interest (TOI) Catalog, which includes both new planet candidates found by TESS and previously known planets recovered by TESS observations. We describe the process used to identify TOIs, investigate the characteristics of the new planet candidates, and discuss some notable TESS planet discoveries. The TOI catalog includes an unprecedented number of small planet candidates around nearby bright stars, which are well suited for detailed follow-up observations. The TESS data products for the Prime Mission (sectors 1-26), including the TOI catalog, light curves, full-frame images, and target pixel files, are publicly available at the Mikulski Archive for Space Telescopes.

Journal ArticleDOI
R. L. Smart1, L. M. Sarro2, Jan Rybizki3, Céline Reylé4  +455 moreInstitutions (82)
TL;DR: In this paper, a clean and well-characterised catalogue of objects within 100 pc of the Sun from the Gaia Early Data Release 3 is presented, which contains at least 92% of stars of stellar type M9 within 100pc of the sun.
Abstract: Aims. We produce a clean and well-characterised catalogue of objects within 100 pc of the Sun from the Gaia Early Data Release 3. We characterise the catalogue through comparisons to the full data release, external catalogues, and simulations. We carry out a first analysis of the science that is possible with this sample to demonstrate its potential and best practices for its use.Methods. Theselection of objects within 100 pc from the full catalogue used selected training sets, machine-learning procedures, astrometric quantities, and solution quality indicators to determine a probability that the astrometric solution is reliable. The training set construction exploited the astrometric data, quality flags, and external photometry. For all candidates we calculated distance posterior probability densities using Bayesian procedures and mock catalogues to define priors. Any object with reliable astrometry and a non-zero probability of being within 100 pc is included in the catalogue.Results. We have produced a catalogue of 331 312 objects that we estimate contains at least 92% of stars of stellar type M9 within 100 pc of the Sun. We estimate that 9% of the stars in this catalogue probably lie outside 100 pc, but when the distance probability function is used, a correct treatment of this contamination is possible. We produced luminosity functions with a high signal-to-noise ratio for the main-sequence stars, giants, and white dwarfs. We examined in detail the Hyades cluster, the white dwarf population, and wide-binary systems and produced candidate lists for all three samples. We detected local manifestations of several streams, superclusters, and halo objects, in which we identified 12 members of Gaia Enceladus. We present the first direct parallaxes of five objects in multiple systems within 10 pc of the Sun.Conclusions. We provide the community with a large, well-characterised catalogue of objects in the solar neighbourhood. This is a primary benchmark for measuring and understanding fundamental parameters and descriptive functions in astronomy.

Journal ArticleDOI
TL;DR: The 2017-2027 National Academies' Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a "designated targeted observable" (DO) as discussed by the authors.

Journal ArticleDOI
TL;DR: In this article, ground-based validation results of the atmospheric NO2 data produced operationally since April 2018 by the TROPOMI instrument on board of the ESA/EU Copernicus Sentinel-5 Precursor (S5p) satellite are compared to correlative measurements collected from, respectively, 19 Multi-Axis DOAS (MAX-DOAS), 26 NDACC Zenith-Scattered-Light DOAS, and 25 PGN/Pandora instruments distributed globally.
Abstract: This paper reports on consolidated ground-based validation results of the atmospheric NO2 data produced operationally since April 2018 by the TROPOMI instrument on board of the ESA/EU Copernicus Sentinel-5 Precursor (S5p) satellite. Tropospheric, stratospheric, and total NO2 column data from S5p are compared to correlative measurements collected from, respectively, 19 Multi-Axis DOAS (MAX-DOAS), 26 NDACC Zenith-Scattered-Light DOAS (ZSL-DOAS), and 25 PGN/Pandora instruments distributed globally. The validation methodology gives special care to minimizing mismatch errors due to imperfect spatio-temporal co-location of the satellite and correlative data, e.g., by using tailored observation operators to account for differences in smoothing and in sampling of atmospheric structures and variability, and photochemical modelling to reduce diurnal cycle effects. Compared to the ground-based measurements, S5p data show, on an average: (i) a negative bias for the tropospheric column data, of typically −23 to −37 % in clean to slightly polluted conditions, but reaching values as high as −51 % over highly polluted areas; (ii) a slight negative bias for the stratospheric column data, of about −0.2 Pmolec/cm2, i.e. approx. −2 % in summer to −15 % in winter; and (iii) a bias ranging from zero to −50 % for the total column data, found to depend on the amplitude of the total NO2 column, with small to slightly positive bias values for columns below 6 Pmolec/cm2 and negative values above. The dispersion between S5p and correlative measurements contains mostly random components, which remain within mission requirements for the stratospheric column data (0.5 Pmolec/cm2), but exceed those for the tropospheric column data (0.7 Pmolec/cm2). While a part of the biases and dispersion may be due to representativeness differences, it is known that errors in the S5p tropospheric columns exist due to shortcomings in the (horizontally coarse) a-priori profile representation in the TM5-MP chemistry transport model used in the S5p retrieval, and to a lesser extent, to the treatment of cloud effects. Although considerable differences (up to 2 Pmolec/cm2 and more) are observed at single ground-pixel level, the near-real-time (NRTI) and off-line (OFFL) versions of the S5p NO2 operational data processor provide similar NO2 column values and validation results when globally averaged, with the NRTI values being on average 0.79 % larger than the OFFL values.

Journal ArticleDOI
TL;DR: In this paper, the authors used an analytical framework that integrates an ensemble of global aerosol model simulations with observationally informed constraints on the dust size distribution, extinction efficiency, and regional dust aerosol optical depth (DAOD).
Abstract: . Even though desert dust is the most abundant aerosol by mass in Earth's atmosphere, the relative contributions of the world's major source regions to the global dust cycle remain poorly constrained. This problem hinders accounting for the potentially large impact of regional differences in dust properties on clouds, the Earth's energy balance, and terrestrial and marine biogeochemical cycles. Here, we constrain the contribution of each of the world's main dust source regions to the global dust cycle. We use an analytical framework that integrates an ensemble of global aerosol model simulations with observationally informed constraints on the dust size distribution, extinction efficiency, and regional dust aerosol optical depth (DAOD). We obtain a dataset that constrains the relative contribution of nine major source regions to size-resolved dust emission, atmospheric loading, DAOD, concentration, and deposition flux. We find that the 22–29 Tg (1 standard error range) global loading of dust with a geometric diameter up to 20 µm is partitioned as follows: North African source regions contribute ∼ 50 % (11–15 Tg), Asian source regions contribute ∼ 40 % (8–13 Tg), and North American and Southern Hemisphere regions contribute ∼ 10 % (1.8–3.2 Tg). These results suggest that current models on average overestimate the contribution of North African sources to atmospheric dust loading at ∼ 65 %, while underestimating the contribution of Asian dust at ∼ 30 %. Our results further show that each source region's dust loading peaks in local spring and summer, which is partially driven by increased dust lifetime in those seasons. We also quantify the dust deposition flux to the Amazon rainforest to be ∼ 10 Tg yr−1, which is a factor of 2–3 less than inferred from satellite data by previous work that likely overestimated dust deposition by underestimating the dust mass extinction efficiency. The data obtained in this paper can be used to obtain improved constraints on dust impacts on clouds, climate, biogeochemical cycles, and other parts of the Earth system.

Journal ArticleDOI
TL;DR: In this paper, a homogeneous analysis of the optical/UV light curves, including 22 previously known TDEs from the literature, reveals a clean separation of light-curve properties with spectroscopic class.
Abstract: While tidal disruption events (TDEs) have long been heralded as laboratories for the study of quiescent black holes, the small number of known TDEs and uncertainties in their emission mechanism have hindered progress toward this promise. Here we present 17 new TDEs that have been detected recently by the Zwicky Transient Facility along with Swift UV and X-ray follow-up observations. Our homogeneous analysis of the optical/UV light curves, including 22 previously known TDEs from the literature, reveals a clean separation of light-curve properties with spectroscopic class. The TDEs with Bowen fluorescence features in their optical spectra have smaller blackbody radii, lower optical luminosities, and higher disruption rates compared to the rest of the sample. The small subset of TDEs that show only helium emission lines in their spectra have the longest rise times, the highest luminosities, and the lowest rates. A high detection rate of Bowen lines in TDEs with small photometric radii could be explained by the high density that is required for this fluorescence mechanism. The stellar debris can provide a source for this dense material. Diffusion of photons through this debris may explain why the rise and fade timescale of the TDEs in our sample are not correlated. We also report, for the first time, the detection of soft X-ray flares from a TDE on ~day timescales. Based on the fact that the X-ray flares peak at a luminosity similar to the optical/UV blackbody luminosity, we attribute them to brief glimpses through a reprocessing layer that otherwise obscures the inner accretion flow.

Journal ArticleDOI
TL;DR: The ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) project is a five-year NASA EVS-2 (Earth Venture Suborbital-2) investigation with three Intensive Observation Periods designed to study key atmospheric processes that determine the climate impacts of these aerosols.
Abstract: . Southern Africa produces almost a third of the Earth’s biomass burning (BB) aerosol particles, yet the fate of these particles and their influence on regional and global climate is poorly understood. ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) is a five-year NASA EVS-2 (Earth Venture Suborbital-2) investigation with three Intensive Observation Periods designed to study key atmospheric processes that determine the climate impacts of these aerosols. During the Southern Hemisphere winter and spring (June-October), aerosol particles reaching 3–5 km in altitude are transported westward over the South-East Atlantic, where they interact with one of the largest subtropical stratocumulus subtropical stratocumulus (Sc) cloud decks in the world. The representation of these interactions in climate models remains highly uncertain in part due to a scarcity of observational constraints on aerosol and cloud properties, and due to the parameterized treatment of physical processes. Three ORACLES deployments by the NASA P-3 aircraft in September 2016, August 2017 and October 2018 (totaling ~350 science flight hours), augmented by the deployment of the NASA ER-2 aircraft for remote sensing in September 2016 (totaling ~100 science flight hours), were intended to help fill this observational gap. ORACLES focuses on three fundamental science questions centered on the climate effects of African BB aerosols: (a) direct aerosol radiative effects; (b) effects of aerosol absorption on atmospheric circulation and clouds; (c) aerosol-cloud microphysical interactions. This paper summarizes the ORACLES science objectives, describes the project implementation, provides an overview of the flights and measurements in each deployment, and highlights the integrative modeling efforts from cloud to global scales to address science objectives. Significant new findings on the vertical structure of BB aerosol physical and chemical properties, chemical aging, cloud condensation nuclei, rain and precipitation statistics, and aerosol indirect effects are emphasized, but their detailed descriptions are the subject of separate publications. The main purpose of this paper is to familiarize the broader scientific community with the ORACLES project and the data set it produced.

Journal ArticleDOI
Tamsin L. Edwards1, Sophie Nowicki2, Sophie Nowicki3, Ben Marzeion4, Regine Hock5, Regine Hock6, Heiko Goelzer7, Heiko Goelzer8, Heiko Goelzer9, Helene Seroussi10, Nicolas C. Jourdain11, Donald Slater12, Donald Slater13, Donald Slater14, Fiona Turner1, Christopher J. Smith15, Christopher J. Smith16, Christine M. McKenna15, Erika Simon2, Ayako Abe-Ouchi17, Jonathan M. Gregory18, Jonathan M. Gregory19, Eric Larour10, William H. Lipscomb20, Antony J. Payne21, Andrew Shepherd15, Cécile Agosta22, Patrick Alexander23, Patrick Alexander24, Torsten Albrecht25, Brian Anderson26, Xylar Asay-Davis27, Andy Aschwanden5, Alice Barthel27, Andrew Bliss28, Reinhard Calov25, Christopher Chambers29, Nicolas Champollion11, Nicolas Champollion4, Youngmin Choi30, Youngmin Choi10, Richard I. Cullather2, J. K. Cuzzone10, Christophe Dumas22, Denis Felikson31, Denis Felikson2, Xavier Fettweis32, Koji Fujita33, Benjamin K. Galton-Fenzi34, Benjamin K. Galton-Fenzi35, Rupert Gladstone36, Nicholas R. Golledge26, Ralf Greve29, Tore Hattermann37, Tore Hattermann38, Matthew J. Hoffman27, Angelika Humbert4, Angelika Humbert39, Matthias Huss40, Matthias Huss41, Matthias Huss42, Philippe Huybrechts43, Walter W. Immerzeel7, Thomas Kleiner39, Philip Kraaijenbrink7, Sébastien Le clec'h43, Victoria Lee21, Gunter R. Leguy20, Christopher M. Little, Daniel P. Lowry44, Jan Hendrik Malles4, Daniel F. Martin45, Fabien Maussion46, Mathieu Morlighem30, James F. O’Neill1, Isabel Nias2, Isabel Nias47, Frank Pattyn9, Tyler Pelle30, Stephen Price27, Aurélien Quiquet22, Valentina Radić48, Ronja Reese25, David R. Rounce5, David R. Rounce49, Martin Rückamp39, Akiko Sakai33, Courtney Shafer45, Nicole Schlegel10, Sarah Shannon21, Robin S. Smith19, Fiammetta Straneo14, Sainan Sun9, Lev Tarasov50, Luke D. Trusel51, Jonas Van Breedam43, Roderik S. W. van de Wal7, Michiel R. van den Broeke7, Ricarda Winkelmann25, Ricarda Winkelmann52, Harry Zekollari, Cheng Zhao35, Tong Zhang53, Tong Zhang27, Thomas Zwinger54 
King's College London1, Goddard Space Flight Center2, University at Buffalo3, University of Bremen4, University of Alaska Fairbanks5, University of Oslo6, Utrecht University7, Bjerknes Centre for Climate Research8, Université libre de Bruxelles9, California Institute of Technology10, University of Grenoble11, University of Edinburgh12, University of St Andrews13, University of California, San Diego14, University of Leeds15, International Institute for Applied Systems Analysis16, University of Tokyo17, Met Office18, University of Reading19, National Center for Atmospheric Research20, University of Bristol21, Université Paris-Saclay22, Goddard Institute for Space Studies23, Columbia University24, Potsdam Institute for Climate Impact Research25, Victoria University of Wellington26, Los Alamos National Laboratory27, Colorado State University28, Hokkaido University29, University of California, Irvine30, Universities Space Research Association31, University of Liège32, Nagoya University33, Australian Antarctic Division34, University of Tasmania35, University of Lapland36, Norwegian Polar Institute37, University of Tromsø38, Alfred Wegener Institute for Polar and Marine Research39, ETH Zurich40, University of Fribourg41, Swiss Federal Institute for Forest, Snow and Landscape Research42, Vrije Universiteit Brussel43, GNS Science44, Lawrence Berkeley National Laboratory45, University of Innsbruck46, University of Liverpool47, University of British Columbia48, Carnegie Mellon University49, Memorial University of Newfoundland50, Pennsylvania State University51, University of Potsdam52, Beijing Normal University53, CSC – IT Center for Science54
06 May 2021-Nature
TL;DR: In this article, the authors estimate probability distributions for these projections under the new scenarios using statistical emulation of the ice sheet and glacier models, and find that limiting global warming to 1.5 degrees Celsius would halve the land ice contribution to twenty-first-century sea level rise, relative to current emissions pledges.
Abstract: The land ice contribution to global mean sea level rise has not yet been predicted1 using ice sheet and glacier models for the latest set of socio-economic scenarios, nor using coordinated exploration of uncertainties arising from the various computer models involved. Two recent international projects generated a large suite of projections using multiple models2,3,4,5,6,7,8, but primarily used previous-generation scenarios9 and climate models10, and could not fully explore known uncertainties. Here we estimate probability distributions for these projections under the new scenarios11,12 using statistical emulation of the ice sheet and glacier models. We find that limiting global warming to 1.5 degrees Celsius would halve the land ice contribution to twenty-first-century sea level rise, relative to current emissions pledges. The median decreases from 25 to 13 centimetres sea level equivalent (SLE) by 2100, with glaciers responsible for half the sea level contribution. The projected Antarctic contribution does not show a clear response to the emissions scenario, owing to uncertainties in the competing processes of increasing ice loss and snowfall accumulation in a warming climate. However, under risk-averse (pessimistic) assumptions, Antarctic ice loss could be five times higher, increasing the median land ice contribution to 42 centimetres SLE under current policies and pledges, with the 95th percentile projection exceeding half a metre even under 1.5 degrees Celsius warming. This would severely limit the possibility of mitigating future coastal flooding. Given this large range (between 13 centimetres SLE using the main projections under 1.5 degrees Celsius warming and 42 centimetres SLE using risk-averse projections under current pledges), adaptation planning for twenty-first-century sea level rise must account for a factor-of-three uncertainty in the land ice contribution until climate policies and the Antarctic response are further constrained.

Journal ArticleDOI
TL;DR: In this paper, the authors present observations of the Moon at 6'µm using the NASA/DLR Stratospheric Observatory for Infrared Astronomy (SOFIA) and find that the distribution of water over the small latitude range is a result of local geology and is probably not a global phenomenon.
Abstract: Widespread hydration was detected on the lunar surface through observations of a characteristic absorption feature at 3 µm by three independent spacecraft1–3. Whether the hydration is molecular water (H2O) or other hydroxyl (OH) compounds is unknown and there are no established methods to distinguish the two using the 3 µm band4. However, a fundamental vibration of molecular water produces a spectral signature at 6 µm that is not shared by other hydroxyl compounds5. Here, we present observations of the Moon at 6 µm using the NASA/DLR Stratospheric Observatory for Infrared Astronomy (SOFIA). Observations reveal a 6 µm emission feature at high lunar latitudes due to the presence of molecular water on the lunar surface. On the basis of the strength of the 6 µm band, we estimate abundances of about 100 to 400 µg g−1 H2O. We find that the distribution of water over the small latitude range is a result of local geology and is probably not a global phenomenon. Lastly, we suggest that a majority of the water we detect must be stored within glasses or in voids between grains sheltered from the harsh lunar environment, allowing the water to remain on the lunar surface. The Stratospheric Observatory for Infrared Astronomy (SOFIA) looked at the Moon in the 6 µm wavelength region and found a signature of molecular water, distinguishing it from other forms of hydration. The authors estimate water abundances between 100 and 400 µg g−1 at high latitudes, trapped within impact glasses or possibly in between grains.

Journal ArticleDOI
TL;DR: In this paper, the authors reported the detection with Konus-Wind of a hard X-ray event of 28 April 2020 temporally coincident with a bright, two-peak radio burst in the direction of the Galactic magnetar SGR 1935+2154, with properties remarkably similar to those of FRBs.
Abstract: Fast radio bursts (FRBs) are bright, millisecond-scale radio flashes of unknown physical origin1. Young, highly magnetized, isolated neutron stars—magnetars—have been suggested as the most promising candidates for FRB progenitors owing to their energetics and high X-ray flaring activity2,3. Here we report the detection with Konus-Wind of a hard X-ray event of 28 April 2020 temporally coincident with a bright, two-peak radio burst4,5 in the direction of Galactic magnetar SGR 1935+2154, with properties remarkably similar to those of FRBs. We show that the two peaks of the double-peaked X-ray burst coincide in time with the radio peaks and infer a common source and the association of these phenomena. An unusual hardness of the X-ray spectrum strongly distinguishes the 28 April event among multiple ‘ordinary’ flares from SGR 1935+2154. A recent non-detection5–7 of radio emission from about 100 typical soft bursts from SGR 1935+2154 favours the idea that bright, FRB-like magnetar signals are associated with rare, hard-spectrum X-ray bursts. The implied rate of these hard X-ray bursts (~0.04 yr−1 magnetar−1) appears consistent with the rate estimate4 of SGR 1935+2154-like radio bursts (0.007–0.04 yr−1 magnetar−1). In April 2020, the Konus-Wind instrument registered two X-ray bursts temporally coincident with two radio bursts from the Galactic magnetar SGR 1935+2154. The unusual spectral hardness of the X-ray bursts may be an indicator of fast-radio-burst-like radio emission from magnetars.


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TL;DR: In 2018, the 25th year of development of radar altimetry was celebrated and the progress achieved by this methodology in the fields of global and coastal oceanography, hydrology, geodesy and cryospheric sciences as discussed by the authors.

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TL;DR: In this paper, the authors developed and applied a methodology for monthly estimates and uncertainties during the period 1998-2019, which combines satellite retrievals of aerosol optical depth, chemical transport modeling, and ground-based measurements to allow for the characterization of seasonal and episodic exposure, as well as aid air-quality management.
Abstract: Annual global satellite-based estimates of fine particulate matter (PM2.5) are widely relied upon for air-quality assessment. Here, we develop and apply a methodology for monthly estimates and uncertainties during the period 1998-2019, which combines satellite retrievals of aerosol optical depth, chemical transport modeling, and ground-based measurements to allow for the characterization of seasonal and episodic exposure, as well as aid air-quality management. Many densely populated regions have their highest PM2.5 concentrations in winter, exceeding summertime concentrations by factors of 1.5-3.0 over Eastern Europe, Western Europe, South Asia, and East Asia. In South Asia, in January, regional population-weighted monthly mean PM2.5 concentrations exceed 90 μg/m3, with local concentrations of approximately 200 μg/m3 for parts of the Indo-Gangetic Plain. In East Asia, monthly mean PM2.5 concentrations have decreased over the period 2010-2019 by 1.6-2.6 μg/m3/year, with decreases beginning 2-3 years earlier in summer than in winter. We find evidence that global-monitored locations tend to be in cleaner regions than global mean PM2.5 exposure, with large measurement gaps in the Global South. Uncertainty estimates exhibit regional consistency with observed differences between ground-based and satellite-derived PM2.5. The evaluation of uncertainty for agglomerated values indicates that hybrid PM2.5 estimates provide precise regional-scale representation, with residual uncertainty inversely proportional to the sample size.