Ground-based Observations Research Articles

Multi-index evaluation of drought conditions in Northeastern Algeria using remote sensing tool

Drought is a complex natural disaster with profound impacts on environmental, social, and economic systems globally. This research investigates drought conditions in the Koudiet Mdaouar Basin, located in northeastern Algeria, through a comprehensive analysis of satellite-derived indicators from 2019 to 2023. Employing advanced remote sensing and GIS-based methodological approaches, the study systematically evaluates vegetation health, thermal stress patterns, and water resource dynamics, with particular focus on the Koudiet Mdaouar reservoir region. The analysis reveals a significant progressive decline in ecological conditions, characterized by deteriorating vegetation cover, intensified thermal stress, and severe water scarcity. These findings not only highlight the region's environmental vulnerability but also demonstrate the intricate interplay between climatic changes and local ecosystem dynamics. The research emphasizes the necessity of developing adaptive water resource policies and underscores the potential of integrated satellite and ground-based observation techniques for more precise local drought assessments, offering critical insights for environmental management and climate resilience strategies.

Sensitivity of Band-Pass Filtered In Situ Low-Earth Orbit and Ground-Based Ionosphere Observations to Lithosphere-Atmosphere-Ionosphere Coupling Over the Aegean Sea: Spectral Analysis of Two-Year Ionospheric Data Series.

This study demonstrates a rich complexity of the time-frequency ionospheric signal spectrum, dependent on the measurement type and platform. Different phenomena contributing to satellite-derived and ground-derived geophysical data that only selected signal bands can be potentially sensitive to seismicity over time, and they are applicable in lithosphere-atmosphere-ionosphere coupling (LAIC) studies. In this study, satellite-derived and ground-derived ionospheric observations are filtered by a Fourier-based band-pass filter, and an experimental selection of potentially sensitive frequency bands has been carried out. This work focuses on band-pass filtered ionospheric observations and seismic activity in the region of the Aegean Sea over a two-year time period (2020-2021), with particular focus on the entire system of tectonic plate junctions, which are suspected to be a potential source of ionospheric disturbances distributed over hundreds of kilometers. The temporal evolution of seismicity power in the Aegean region is represented by the record of earthquakes characterized by M ≥ 4.5, used for the estimation of cumulative seismic energy. The ionospheric response to LAIC is explored in three data types: short inspections of in situ electron density (Ne) over a tectonic plate boundary by Swarm satellites, stationary determination of three Ne density profile parameters by the Athens Digisonde station AT138 (maximum frequency of the F2 layer: foF2; maximum frequency of the sporadic E layer: foEs; and frequency spread: ff), and stationary measure of vertical total electron content (VTEC) interpolated from a UPC-IonSAT Quarter-of-an-hour time resolution Rapid Global ionospheric map (UQRG) near Athens. The spectrograms are made with the use of short-term Fourier transform (STFT). These frequency bands in the spectrograms, which show a notable coincidence with seismicity, are filtered out and compared to cumulative seismic energy in the Aegean Sea, to the geomagnetic Dst index, to sunspot number (SN), and to the solar radio flux (F10.7). In the case of Swarm, STFT allows for precise removal of long-wavelength Ne signals related to specific latitudes. The application of STFT to time series of ionospheric parameters from the Digisonde station and GIM VTEC is crucial in the removal of seasonal signals and strong diurnal and semi-diurnal signal components. The time series formed from experimentally selected wavebands of different ionospheric observations reveal a moderate but notable correlation with the seismic activity, higher than with any solar radiation parameter in 8 out of 12 cases. The correlation coefficient must be treated relatively and with caution here, as we have not determined the shift between seismic and ionospheric events, as this process requires more data. However, it can be observed from the spectrograms that some weak signals from selected frequencies are candidates to be related to seismic processes.

Validation of millimetre and sub-millimetre atmospheric collision-induced absorption at Chajnantor

Due to the importance of a reference atmospheric radiative transfer model for both planning and calibrating ground-based observations at millimetre and sub-millimetre wavelengths, we have undertaken a validation campaign consisting of acquiring atmospheric spectra under different weather conditions, in different diurnal moments and seasons, with the Atacama Pathfinder EXperiment (APEX), due to the excellent stability of its receivers and the very high frequency resolution of its back-ends. As a result, a dataset consisting of 56 spectra within the 157.3-742.1 GHz frequency range, at kilohertz resolution (smoothed to sim 2-10 MHz for analysis), and spanning one order of magnitude (sim 0.35-3.5 mm) in precipitable water vapour columns, has been gathered from October 2020 to September 2022. These data are unique for their quality and completeness and, due to the proximity of APEX to the Atacama Large Millimeter/Submillimeter Array (ALMA), they provide an excellent opportunity to validate the atmospheric radiative transfer model currently installed in the ALMA software. The main issues addressed in the study are possible missing lines in the model, line shapes, vertical profiles of atmospheric physical parameters and molecular abundances, seasonal and diurnal variations, and collision-induced absorption (CIA), to which this paper is devoted, in its N$_2$-N$_2$ + N$_2$-O$_2$ + O$_2$-O$_2$ (dry) and N$_2$-H$_2$O + O$_2$-H$_2$O (`foreign' wet) mechanisms. All these CIA terms should remain unchanged in the above-mentioned ALMA atmospheric model as a result of this work.

Ground-Based Cloud Image Segmentation Method Based on Improved U-Net

Cloud image segmentation is a technique that divides images captured by meteorological satellites or ground-based observations into different regions or categories. By extracting the distribution, shape, and dynamic features of clouds, it provides precise data support for the meteorological and environmental fields, significantly influencing photovoltaic (PV) power generation forecasting, astronomical telescope observatory site selection, and weather forecasting. A ground-based cloud image segmentation model based on an improved U-Net is proposed, which adopts an overall encoder–decoder structure. In the encoder phase, this paper constructs a dilated convolution–atrous spatial pyramid pooling (ASPP)–dilated convolution structure to enhance early cloud feature extraction. Dilated convolution is a novel type of convolution that expands the receptive field by inserting holes into standard convolution, thereby capturing a larger range of contextual information. ASPP maintains high resolution while paying attention to both local details and global structures of the image. In the decoder stage, the bicubic interpolation method is used for up-sampling to restore the feature map resolution and improve the clarity of the segmented image. The bicubic interpolation method refers to the use of cubic polynomial functions to interpolate the pixel values of the input image. In addition, this paper designs a novel skip connection layer structure between the encoder and decoder, composed of a depthwise separable path (DS path) and an improved channel spatial attention module (Im-CSAM) connected in sequence. The DS path combines depthwise separable convolutions and residual structures to facilitate information exchange between high-level and low-level features. The Im-CSAM is a modular attention mechanism that focuses on important cloud features in spatial and channel dimensions to enhance segmentation accuracy. Experiments show that compared to the traditional U-Net, the accuracy, precision, and MIoU of this model improved by 2.2%, 4.1%, and 5.0%, respectively, in the SWINySEG dataset, and by 3.2%, 3.6%, and 5.8%, respectively, in the TCDD dataset, proving that the improved method has a better generalization ability and segmentation performance.

Performance of Kilometer-Scale CARAS Precipitation Product Against Ground-based Observations During 2008–2021 over Hubei, China

Performance of Kilometer-Scale CARAS Precipitation Product Against Ground-based Observations During 2008–2021 over Hubei, China

A depth-based land-use semantic extraction method for landscape images

Integrated aerial and ground-based observations are essential for rapid land-use classification in modern land surveys, requiring efficient analysis of large volumes of landscape images. These images often contain background noise that impedes classification accuracy and exhibit spatial compression of the main scene, which causes semantic spatial distortion as depth increases. To address these challenges, this paper proposes a depth-based land-use semantic extraction (DLSE) method that effectively reduces background noise and corrects spatial distortions. The DLSE method follows three main steps: (1) depth estimation per pixel using a multi-resolution neural network to delineate the main scene range, filtering semantic noise; (2) conversion of perspective projection to orthographic projection per pixel to correct spatial distortion; and (3) improved land-use semantic extraction through MobileViT-enhanced feature extraction in SegNet. Experimental results demonstrate that DLSE achieves a 96.84% accuracy with more detailed outputs and operates at 23 fps, positioning it as an efficient tool for automated land surveys and land-use decision-making.

Advances in Remote Sensing for Monitoring Soil Conditions in Forest Ecosystems: Techniques, Challenges, and Applications

ABSTRACT Advances in remote sensing technologies have revolutionized the monitoring of soil conditions in forest ecosystems, providing valuable insights into soil moisture, nutrient content, and degradation without requiring physical access to remote areas. This article explores the application of key techniques, including satellite-based L-band radiometry, UAV-enabled LiDAR, and visible–NIR spectroscopy, in assessing forest soil properties. Challenges such as canopy interference, spatial resolution limitations, and data validation are discussed, alongside innovative solutions like machine learning and high-resolution digital elevation models. Case studies highlight the effectiveness of remote sensing in addressing environmental and forestry challenges, such as tracking the effects of climate change, logging, and erosion. By integrating advanced imaging technologies with ground-based observations, remote sensing supports sustainable forest management, conservation practices, and ecological research. Future developments in sensor technology, data integration, and machine learning hold promise for even greater precision and scalability in forest soil monitoring.

The Response of Tropopause-Overshooting Convection over North America to Climate Change

Abstract Recent field campaigns, observational studies, and modeling work have demonstrated that extratropical tropopause-overshooting convection has a substantial and previously underestimated impact on stratospheric water vapor concentrations. This necessitates improved understanding of how tropopause-overshooting convection will respond to a warming climate. A growing body of research indicates that environments conducive to severe thunderstorms will occur more often and be increasingly unstable in the future, but no study has examined how this may be related to increased overshooting. To rectify this, this study leverages an existing pseudo–global warming (PGW) experiment to evaluate potential future changes in tropopause-overshooting convection over North America. We examine two 10-yr simulations consisting of 1) a retrospective period (2003–12) forced by ERA-Interim initial and boundary conditions (the control simulation) and 2) the same retrospective period with CMIP5 ensemble-mean high-end emission scenario climate changes added to the initial and boundary conditions (the PGW simulation). Tropopause-overshooting convection in the control simulation is validated against observed overshoots from both ground-based radar observations in the United States and GOES observations over North America. The model is shown to effectively simulate the observed regional distribution, annual cycle, and diurnal cycle of tropopause-overshooting convection. In the PGW simulation, tropopause-overshooting convection is found to increase more than 250% across the model domain, and the projected seasonal period of frequent tropopause-overshooting convection is shown to extend into late summer. Additionally, tropopause-overshooting convection with extreme tropopause-relative heights (>4 km) is more frequent in a warmed climate scenario.

Fragmentation in Collisions of Snow with Graupel/Hail: New Formulation from Field Observations

Abstract Secondary ice production (SIP) has been attributed to the generation of most ice particles observed in precipitating clouds with cloud tops warmer than −36°C, from various aircraft- and ground-based field observations across the globe. One of the known SIP mechanisms is fragmentation during collisions among ice particles. It has been studied with our theoretical formulation, which has been applied in microphysical schemes of atmospheric models in a few studies. These have predicted an extensive impact on cloud glaciation and radiative properties. However, there has been a lack of experimental field studies, especially involving naturally falling snowflakes, to better understand this particular mechanism of SIP. This study reports the first field measurements with modern technology for fragmentation during collisions between naturally falling snowflakes and graupel/hail particles. This was observed with an innovatively designed portable chamber that was deployed outdoors in northern Sweden. Applying the observations from this field-based study, we optimized the existing formulation for predicting numbers of fragments from collisions of snow with graupel/hail. The observations show the average numbers of fragments per collision for dendritic (3–12 mm) and nondendritic (1–3 mm) snow were about 12 and 1, respectively. This represents a boost of predicted fragment numbers relative to our original formulation published in 2017. The updated formulation for breakup in ice–ice collisions can be implemented in the microphysical schemes of atmospheric models.

Initial Orbit Determination of Periodic Orbits in the Earth–Moon System With Ground-Based Optical Observations

Initial Orbit Determination of Periodic Orbits in the Earth–Moon System With Ground-Based Optical Observations

Luminous Efficiency Determination of Spacecraft Materials in Ground Test Facilities

This paper reports the approach to determine the luminous efficiency of spacecraft materials experimentally. The idea is that knowing the luminous efficiency of spacecraft materials allows for deriving mass estimates from observation data linking meteor science methods to man-made artificial meteors. This way, observation data from airborne missions and known ground-based meteor observation networks become a versatile tool to analyze destructive spacecraft entry. Material samples were scaled to a typical re-entry condition. The authors recently developed a method to determine the radiant flux in the passbands U, B, and V and the corresponding color indices. The measured mass loss during the experiment is used to determine the luminous efficiency τ for the different passbands. These values are reported for three different materials under flow conditions corresponding to two trajectory points at altitudes of 70km and 65km in a typical decaying re-entry orbit. The resulting luminous efficiency values are of the order 10-5, which is about two orders of magnitude lower than meteoroid efficiencies. The found data is applied to fragments observed during the CYGNUS OA-6 observation campaign. The mass loss was extrapolated for the entire duration of the re-entry resulting in a mass of around 68-80% of literature values for the mass at the entry interface.

A Fourth Planet in the Kepler-51 System Revealed by Transit Timing Variations

Kepler-51 is a ≲1 Gyr old Sun-like star hosting three transiting planets with radii ≈6–9 R ⊕ and orbital periods ≈45–130 days. Transit timing variations (TTVs) measured with past Kepler and Hubble Space Telescope (HST) observations have been successfully modeled by considering gravitational interactions between the three transiting planets, yielding low masses and low mean densities (≲0.1 g cm−3) for all three planets. However, the transit time of the outermost transiting planet Kepler-51d recently measured by the James Webb Space Telescope 10 yr after the Kepler observations is significantly discrepant from the prediction made by the three-planet TTV model, which we confirmed with ground-based and follow-up HST observations. We show that the departure from the three-planet model is explained by including a fourth outer planet, Kepler-51e, in the TTV model. A wide range of masses (≲M Jup) and orbital periods (≲10 yr) are possible for Kepler-51e. Nevertheless, all the coplanar solutions found from our brute-force search imply masses ≲10 M ⊕ for the inner transiting planets. Thus, their densities remain low, though with larger uncertainties than previously estimated. Unlike other possible solutions, the one in which Kepler-51e is around the 2:1 mean motion resonance with Kepler-51d implies low orbital eccentricities (≲0.05) and comparable masses (∼5 M ⊕) for all four planets, as is seen in other compact multiplanet systems. This work demonstrates the importance of long-term follow-up of TTV systems for probing longer-period planets in a system.

Observations of Uranus at High Phase Angle as Seen by New Horizons

We present flux measurements of Uranus observed at phase angles of 43.°9, 44.°0, and 52.°4 by the Multispectral Visible Imaging Camera on the New Horizons spacecraft during 2023, 2010, and 2019, respectively. New Horizons imaged Uranus at a distance of about 24–70 au (2023) in four color filters, with bandpasses of 400–550 nm, 540–700 nm, 780–975 nm, and 860–910 nm. High-phase-angle observations are of interest for studying the energy balance of Uranus, constraining the atmospheric scattering behavior, and understanding the planet as an analog for ice giant exoplanets. The new observations from New Horizons provide access to a wider wavelength range and different season compared with previous observations from the Voyager spacecraft. We performed aperture photometry on the New Horizons observations of Uranus to obtain its brightness in each photometric band. The photometry suggests that Uranus may be darker than predicted by a Lambertian phase curve in the blue and red filters. Comparison to simultaneous low-phase Hubble WFC3 and ground-based community-led observations indicates a lack of large-scale features at full phase that would introduce variation in the rotational light curve. The New Horizons reflectance in the blue (492 nm) and red (624 nm) filters does not exhibit statistically significant variation and is consistent with the expected error bars. These results place new constraints on the atmospheric model of Uranus and its reflectivity. The observations are analogous to those from future exoplanet direct-imaging missions, which will capture unresolved images of exoplanets at partial phases. These results will serve as a “ground truth” with which to interpret exo-ice-giant data.

From Space to Health: The Impact of Earth Observation Research on the Smart4COV19 Telemedicine project

Abstract This study investigates the potential of Earth observation research in improving air quality management and supporting telemedicine initiatives in response to the COVID-19 pandemic. Utilizing ground stations in Sofia and Burgas, we collected hourly measurements of NO2, CO, PM10, and PM2.5. Satellite data from TROPOMI-S5p and ground-based air quality observations were integrated to assess the spatial distribution of surface particulate matter concentrations. Our results demonstrate the feasibility of leveraging satellite-derived atmospheric chemistry data to enhance air pollution modeling and urban-scale air quality management. Furthermore, the technology developed for Burgas has the potential for expansion to other Bulgarian cities and replication in different urban centers. Overall, this research highlights the importance of Earth observation research in addressing critical environmental and public health challenges. Telemedicine via smartphones can help manage these risks by offering assistance to patients with mild symptoms, thereby minimizing their exposure to COVID-19 patients. The study also presents the findings of models used to convert TROPOMI – S5p aerosol data into PM concentrations in Burgas, Bulgaria.

GRB 211024B: An Ultra-long GRB Powered by Magnetar

Ultra-long gamma-ray bursts are characterized by exceptionally long-duration central engine activities, with characteristic timescales exceeding 1000 s. We present ground-based optical afterglow observations of the ultra-long gamma-ray burst GRB 211024B, detected by Swift. Its X-ray light curve exhibits a characteristic “internal plateau” with a shallow decay phase lasting approximately ∼15 ks, followed by a steep decline (α drop ∼ −7.5). Moreover, the early optical emission predicted by the late r-band optical afterglow is significantly higher than the observed value, indicating an external shock with energy injection. To explain these observations, we propose a magnetar central engine model. The magnetar collapses into a black hole due to spin-down or hyperaccretion, leading to the observed steep break in the X-ray light curve. The afterglow model fitting reveals that the afterglow injection luminosity varies with different assumptions of the circumburst medium density, implying different potential energy sources. For the interstellar medium case with a fixed injection end time, the energy may originate from the magnetar’s dipole radiation. However, in other scenarios, relativistic jets produced by the magnetar/black hole system could be the primary energy source.

Connecting the X-Ray/UV Variability of Fairall 9 with NICER: A Possible Warm Corona

The Seyfert 1 active galactic nucleus Fairall 9 was targeted by NICER, Swift, and ground-based observatories for a ∼1000 day long reverberation mapping campaign. The following analysis of NICER spectra taken at a 2 day cadence provides new insights into the structure and heating mechanisms of the central black hole environment. Observations of Fairall 9 with NICER and Swift revealed a strong relationship between the flux of the UV continuum and the X-ray soft excess, indicating the presence of a “warm” Comptonized corona that likely lies in the upper layers of the innermost accretion flow, serving as a second reprocessor between the “hot” X-ray corona and the accretion disk. The X-ray emission from the hot corona lacks sufficient energy and variability to power slow changes in the UV light curve on timescales of 30 days or longer, suggesting an intrinsic disk-driven variability process in the UV and soft X-rays. Fast variability in the UV on timescales shorter than 30 days can be explained through X-ray reprocessing, and the observed weak X-ray/UV correlation suggests that the corona changes dynamically throughout the campaign.

Retrieval of Boundary Layer Precipitable Water from GOES ABI Using Machine Learning Techniques

Abstract Low-level moisture is an important ingredient for forecasting severe storms, especially over the Great Plains where severe storms often develop along the dryline. Although ground-based observation systems such as lidar or radiosondes on weather balloons provide accurate information on low-level moisture, data are provided at limited locations, and the low temporal resolution of radiosondes makes it difficult to track a rapidly developing dryline. Geostationary satellites provide high spatial and temporal observation, but the channels of current geostationary satellites are mostly sensitive to water vapor at mid- to upper levels. However, the split window difference (SWD) between the “clean” window channel (10.3 μm) and the “dirty” window channel (12.3 μm) is commonly used for estimating low-level water vapor. However, this estimation is complicated by surface temperature contributions, dependence on the lapse rate, and nonlinear relationships between SWD and moisture. This study applies machine learning techniques to infer boundary layer precipitable water (BLPW) from Geostationary Operational Environmental Satellite (GOES) Advanced Baseline Imager (ABI) data. Since there are few observations that cover wide regions for training convolutional neural networks, especially for the atmosphere above the surface, High-Resolution Rapid Refresh (HRRR) model outputs are used as the truth for training. Statistical results show that using ABI channel 13, 14, and 15 brightness temperatures, skin temperature, solar zenith angle, and GOES total precipitable water product as additional inputs shows the lowest mean-square error (MSE). Case study results show that the model developed in this study is good at capturing strong moisture gradients and depicting a dryline. Significance Statement Knowledge of low-level moisture is important for monitoring drylines and moisture convergence preceding convective initiation. Traditional observations lack the spatial resolution, spatial coverage, or temporal update frequency needed by forecasters. The latest generation of Geostationary Operational Environmental Satellite (GOES), the GOES-R series, provides high spatial resolution and rapid temporal updates. However, the estimation of low-level moisture using the Advanced Baseline Imager (ABI) is complicated by dependencies on other variables and nonlinear relationships. Machine learning is shown to provide good estimates of low-level moisture from ABI observations. This application of machine learning to GOES-R can improve the ability to monitor low-level moisture and forecast convective initiation.

Description and validation of the Japanese algorithm for radiative flux and heating rate products with all four EarthCARE instruments: pre-launch test with A-Train

Abstract. This study developed an algorithm for the Level 2 (L2) atmospheric radiation flux and heating rate product by a Japanese team for Earth Cloud, Aerosol and Radiation Explorer (EarthCARE). This product offers vertical profiles of downward and upward longwave (LW) and shortwave (SW) radiative fluxes and their atmospheric heating rates. This paper describes the algorithm developed for generating products, including the atmospheric radiative transfer model and input datasets, and its validation against measurement data of radiative fluxes. In the testing phase before the EarthCARE launch, we utilized A-Train data that provided input and output variables analogous to EarthCARE, so that the developed algorithm could be directly applied to EarthCARE after its launch. The results include comparisons of radiative fluxes between radiative transfer simulations and satellite and ground-based observations that quantify errors in computed radiative fluxes at the top of the atmosphere against Clouds and the Earth's Radiant Energy System (CERES) observations and their dependence on cloud type with varying thermodynamic phases. For SW fluxes, the bias was 24.4 W m−2, and the root mean square error (RMSE) was 36.3 W m−2 relative to the CERES observations at spatial and temporal scales of 5° and 1 month, respectively. On the other hand, LW exhibits a bias of −10.7 W m−2 and an RMSE of 14.2 W m−2. When considering different cloud phases, the SW water cloud exhibited a bias of −11.7 W m−2 and an RMSE of 46.2 W m−2, while the LW showed a bias of 0.8 W m−2 and an RMSE of 6.0 W m−2. When ice clouds were included, the SW bias ranged from 58.7 to 81.5 W m−2 and the RMSE from 72.8 to 91.6 W m−2 depending on the ice-containing cloud types, while the corresponding LW bias ranged from −8.8 to −28.4 W m−2 and the RMSE from 25.9 to 31.8 W m−2, indicating that the primary source of error was ice-containing clouds. The comparisons were further extended to various spatiotemporal scales to investigate the scale dependency of the flux errors. The SW component of this product exhibited an RMSE of approximately 30 W m−2 at spatial and temporal scales of 40° and 40 d, respectively, whereas the LW component did not show a significant decrease in RMSE with increasing spatiotemporal scale. Radiative transfer simulations were also compared with ground-based observations of the surface downward SW and LW radiative fluxes at selected locations. The results show that the bias and RMSE for SW are −17.6 and 172.0 W m−2, respectively, which are larger than those for LW that are −5.6 and 19.0 W m−2, respectively.

The X-Ray Luminous Type Ibn SN 2022ablq: Estimates of Preexplosion Mass Loss and Constraints on Precursor Emission

Type Ibn supernovae (SNe Ibn) are rare stellar explosions powered primarily by interaction between the SN ejecta and H-poor, He-rich material lost by their progenitor stars. Multiwavelength observations, particularly in the X-rays, of SNe Ibn constrain their poorly understood progenitor channels and mass-loss mechanisms. Here we present Swift X-ray, ultraviolet, and ground-based optical observations of the Type Ibn SN 2022ablq, only the second SN Ibn with X-ray detections to date. While similar to the prototypical Type Ibn SN 2006jc in the optical, SN 2022ablq is roughly an order of magnitude more luminous in the X-rays, reaching unabsorbed luminosities L X ∼ 4 × 1040 erg s−1 between 0.2–10 keV. From these X-ray observations we infer time-varying mass-loss rates between 0.05 and 0.5 M ⊙ yr−1 peaking 0.5–2 yr before explosion. This complex mass-loss history and circumstellar environment disfavor steady-state winds as the primary progenitor mass-loss mechanism. We also search for precursor emission from alternative mass-loss mechanisms, such as eruptive outbursts, in forced photometry during the 2 yr before explosion. We find no statistically significant detections brighter than M ≈ −14—too shallow to rule out precursor events similar to those observed for other SNe Ibn. Finally, numerical models of the explosion of an ∼15 M ⊙ helium star that undergoes an eruptive outburst ≈1.8 yr before explosion are consistent with the observed bolometric light curve. We conclude that our observations disfavor a Wolf–Rayet star progenitor losing He-rich material via stellar winds and instead favor lower-mass progenitor models, including Roche-lobe overflow in helium stars with compact binary companions or stars that undergo eruptive outbursts during late-stage nucleosynthesis stages.

Refining the Global Picture: The Impact of Increased Resolution on CO2 Atmospheric Inversions Using OCO-2 XCO2 Retrievals.

The threat posed by the increasing concentration of carbon dioxide (CO2) in the atmosphere motivates a detailed and precise estimation of CO2 emissions and removals over the globe. This study refines the spatial resolution of the CAMS/LSCE inversion system, achieving a global resolution of 0.7° latitude and 1.4° longitude, or three times as many grid boxes as the current operational setup. In a 2-year inversion assimilating the midday clear-sky retrievals of the column-averaged dry air mole fraction of carbon dioxide (XCO2) from NASA's second Orbiting Carbon Observatory (OCO-2), the elevated resolution demonstrates an improvement in the representation of atmospheric CO2, particularly at the synoptic timescale, as validated against independent surface measurements. Vertical profiles of the CO2 concentration differ slightly above 22km between resolutions compared to AirCore profiles, and highlight differences in the vertical distribution of CO2 between resolutions. However, this disparity is not evident for XCO2, as evaluated against independent reference ground-based observations. Global and regional estimates of natural fluxes for 2015-2016 are similar between the two resolutions, but with North America exhibiting a higher natural sink at high resolution for 2016. Overall, both inversions seem to yield reasonable estimates of global and regional natural carbon fluxes. The increase in calculation time is less than the increase in the number of operations and in the volume of input data, revealing greater efficiency of the code executed on a graphics processing unit. This allows us to make this higher resolution the new standard for the CAMS/LSCE system.