Ground-based Observations Research Articles

Application of integrated GNSS tomography in observation study over the area of southern Poland

Despite the growing number of GNSS sites, GNSS meteorology still suffers from insufficient observational data. This deficiency is particularly evident in GNSS tomography processing in the lowermost part of the troposphere, where the highest variability of weather conditions is observed. To address this challenge, we propose an integrated tomography solution that uses both ground-based GNSS observations and radio occultation (RO) excess phase in the same tomography model. Our integrated approach benefits from improved geometry and increased number of observations. In order to verify presented methodology, we use 41 GNSS stations and 78 RO profiles gathered during four different high precipitation events over the region of southern Poland. The results are validated against radiosonde observations and WRF reanalysis. Our study indicates an enhancement in the representation of wet refractivity on the order of 20 to 30% RMSE decrease when RO are integrated into the tomography solution with the mean error of final integrated product equal 3.5 ppm.

Calibration of CAMS PM2.5 data over Hungary: a machine learning approach

Air pollution is a major environmental problem, and reliable monitoring of particulate matter (PM) concentrations is critical for assessing its impact on human health and the environment. The Copernicus Atmosphere Monitoring Service (CAMS) offers vital data on PM2.5 concentrations by applying a worldwide modelling system. This study compares in situ PM2.5 measurements and raw CAMS data at 0.1° × 0.1° resolutions for 2019 and 2020 in Hungary. It proposes a calibration method to improve the accuracy of CAMS PM2.5 data at the scale of air monitoring stations. In the study, the accuracy of the raw CAMS PM2.5 data is assessed based on the chosen air quality stations. Then, to improve the precision, we employed machine learning algorithms (LightGBM, Random Forest (RF), and Multiple Linear Regression (MLR)) for calibration. Initial assessment of the raw CAMS PM2.5 data showed positive hourly Spearman correlation coefficient values (SR between 0.64 and 0.87 for the 14 air quality stations used), indicating a positive relationship between the datasets but a systemic underestimation. Our findings highlight LightGBM as the most effective method, consistently demonstrating elevated correlation SR and coefficient of determination R2 values reaching up to 0.95 and 0.93, respectively, and very good RSR (Root mean square error ratio) and NSE (Nash-Sutcliffe Efficiency) values (lower than 0.5 and higher than 0.75 for RSR and NSE, respectively). In contrast, RF yields mixed results, and MLR exhibits variable performance. By correcting underestimation and lowering modelling biases, the calibrated PM2.5 data better matches ground-based observations, which can be promising for using the obtained model for accurate estimation at individual air monitoring stations.

Direct imaging of exoplanets: Legacy and prospects

Understanding how giant and terrestrial planets form and evolve, what is their internal structure and that of their atmosphere, represents one of the major challenges of modern astronomy, which is directly connected to the ultimate search for life at the horizon 2030–2050. However, several astrophysical (understanding of the formation and physics of giant and terrestrial exoplanets), biological (identification of the best biomarkers) and technological (technical innovations for the new generations of telescopes and instruments) obstacles must be overcome. From the astrophysical point of view, it is indeed crucial to understand the mechanisms of formation and evolution of giant planets, including planet and disk interactions, which will completely sculpt the planetary architectures and thus dominate the formation of terrestrial planets, especially in regions around the host star capable of supporting life. It is also important to develop dedicated instrumentation and techniques to study in their totality the population of giant and terrestrial planets, but also to reveal in the near future the first biological markers of life in the atmospheres of terrestrial planets. In that perspective, direct imaging from ground-based observatories or in space is playing a central role in concert with other observing techniques. In this paper, I will briefly review the genesis of this observing technique, the main instrumental innovation and challenges, stellar targets and surveys, to then present the main results obtained so far about the physics and the mechanisms of formation and evolution of young giant planets and planetary system architectures. I will then present the exciting perspectives offered by the upcoming generation of planet imagers about to come online, particularly on the future extremely large telescopes. On the timescale of a human Life, we may well be witnessing the first discovery of an exoplanet and the first detection of indices of life in the atmosphere of a nearby exo-Earth!

Characterization and modelling of exoplanetary atmospheres

The spectral characterization of exoplanetary atmospheres is a relatively new subject. Until recently, space and ground-based observatories allowed only a few planets to be observed with spectroscopic instruments. The arrival of JWST data and the development of new instruments on Earth will drastically modify the situation, giving access to deeper modelling contraints and to a better understanding on planet formation and evolution. This paper is devoted to describe the turning point experienced by the astronomy of exoplanets that will continue in particular with the launch of the ESA/Ariel mission at the end of the decade.

Comparison of datasets citation coverage in Google Scholar, Web of Science, Scopus, Crossref, and DataCite

The rapid increase of Earth science data from remote sensing, models, and ground-based observations highlights an urgent need for effective data management practices. Data repositories track provenance and usage metrics which are crucial for ensuring data integrity and scientific reproducibility. Although the introduction of Digital Object Identifiers (DOIs) for datasets in the late 1990s has significantly aided in crediting creators and enhancing dataset discoverability (akin to traditional research citations), considerable challenges persist in establishing linkage of datasets used with scholarly documents. This study evaluates the citation coverage of datasets from NASA’s Earth Observing System Data and Information System (EOSDIS) across several major bibliographic sources ‒ namely Google Scholar (GS), Web of Science (WoS), Scopus, Crossref, and DataCite—which helps data managers in making informed decisions when selecting bibliographic sources. We provide a robust and comprehensive understanding of the citation landscape, crucial for advancing data management practices and advancing open science. Our study searched and analyzed temporal trends across the bibliographic sources for publications that cite approximately 11,000 DOIs associated with EOSDIS datasets, yielding 17,000 unique journal and conference articles, reports, and book records linked to 3,000 dataset DOIs. GS emerged as the most comprehensive source while Crossref lagged significantly behind the other major sources. Crossref’s record references revealed that the absence of dataset DOIs and shortcomings in the Crossref Event data interface likely contributed to its underperformance. Scopus initially outperformed WoS until 2020, after which WoS began to show superior performance. Overall, our study underscores the necessity of utilizing multiple bibliographic sources for citation analysis, particularly for exploring dataset-to-document connections.

10-year progress on Forest Carbon Research in Canada

In 2012 <i>A Blueprint for Forest Carbon Science in Canada:2012–2020</i> was published to guide policy and research and to support Canada’s obligations for climate change mitigation and adaptation, sustainable forest management, and international reporting. Over the past decade, the body of scientific research focused on forest carbon dynamics in Canada has significantly evolved, providing crucial insights into the intricate interplay between various forest ecosystems and the global carbon cycle. This comprehensive review synthesizes key findings from this period, highlighting improvements to Canada’s estimates of current forest greenhouse gas emissions and removals, the effects of global changes on Canada’s future forest carbon, and forest contributions to mitigate climate change. Collaborating with the forest carbon science community, we identified 426 peer-reviewed published articles on landscape-scale forest carbon research for Canada from 2012 to 2021. The review emphasizes anthropogenic influences and natural disturbances in contemporary GHG emissions and removals for managed forests in the national GHG inventory, highlights the existing dichotomy between management and research models, and stresses the need for integrating disturbance and climate effects for comprehensive GHG estimates. Despite significant progress in estimating and tracking forest carbon using modelled, remotely sensed and ground-based observations, challenges remain in reducing uncertainties, particularly regarding climate impacts on forest growth, decay, and disturbances. Furthermore, the review showcases recent advancements in climate change mitigation strategies and the use of a systems approach that includes forest ecosystem emissions and removals, wood product emissions and substitution benefits of avoided fossil emissions. Through comprehensive scenario analyses, the review underscores regional variations in assessing GHG reductions and notes a shift towards a more holistic approach that considers environmental, economic, and social values. By providing a nuanced understanding of the complexities underlying forest carbon dynamics in Canada, this review sets the stage for future research and policy initiatives aimed at fostering sustainable forest management practices and mitigating the impact of climate change on these vital ecosystems.

Observations and emission constraints of trichlorofluoromethane (CFC-11) in southeastern China: first-year results from a new AGAGE station

The recovery of the ozone layer relies on decreasing atmospheric mixing ratios of ozone-depleting substances (ODSs), including chlorofluorocarbons (CFCs). A significant decline in the mixing ratio of trichlorofluoromethane (CFC-11 or CCl3F ), the second most abundant CFC, has been observed since the mid-1990s. However, a slowdown in the decline after 2012 indicates a rise in emissions, particularly in Eastern Asia. Ground-based observations are lacking in southeastern China, limiting a thorough evaluation of CFC-11 levels and emissions in this region. A new Advanced Global Atmospheric Gases Experiment background station was established at Xichong (XCG), Shenzhen, China, to provide high-frequency continuous in situ observations. The annual mean CFC-11 mixing ratio, recorded from May 2022 to April 2023, is 221.64 ± 2.29 ppt. When compared with a monthly (MHD) or daily (MLO) observation, this value is found to be 0.45% to 5.36% higher than the northern hemispheric background. With the inverse modeling and interspecies correlation method, we estimate CFC-11 emissions in southeastern China between 1.23 ± 0.25 Gg yr−1 and 1.58 ± 0.21 Gg yr−1, in line with the bottom-up estimation of 1.50 Gg yr−1. Results indicate that CFC-11 emissions in the Pearl River Delta region have returned to levels before 2010, aligning with regional and global trends. Observations from XCG would compensate for the deficiency of CFC-11 measurements in southeastern China, paving the road for ODS studies in this region and beyond.

First light simultaneous triple-channel optical observations of the OPTICAM system at the OAN-SPM

OPTICAM is a triple-band optical system developed for the 2.1 m telescope of the National Astronomical Observatory in the Sierra de San Pedro Mártir (OAN-SPM). Partial engineering tests were conducted in 2019, with the complete system experiencing its first light in March 2022. The system incorporates two beam splitters, enabling simultaneous observations on three channels. Users can choose three out of the five available filters from the SDSS filter set (u′g′r′i′z′), covering the wavelength range from 320 to 1000 nm. It offers an effective field of view of approximately 4.7, 4.7, and 5.6 arcminutes in each of its arms, respectively. Due to its design and capabilities, OPTICAM is suitable for coordinated observations with other ground-based and space-based observatories. This document presents the final instrument design and the current system status. Some of the optical tests carried out are described. We also present the results of scientific observations conducted during its first light and first year of operations.

Photoperiod alone does not explain the variations of leaf senescence onset across Europe

The onset of autumnal leaf senescence (TLSO) plays a crucial role in understanding and modeling how plants prepare for winter dormancy, but it has remained under-explored. Previous research, based on limited in situ observations, have proposed that the year-to-year invariant photoperiod is the primary trigger of TLSO, indicating potential insensitivity to climate change. This study verifies this hypothesis using high spatial resolution satellite data and ground-based observations throughout the European continent. Surprisingly, our findings reveal a widespread delay in TLSO during 2001–2019 (approximately 73 %, significant in 19 % of the continent, p < 0.05), closely aligned with delayed leaf senescence end (TLSD) as reported in previous studies. Moreover, as the delaying trend was larger for TLSO than for TLSD, it is apparent that climate change does not only postpone the onset of the leaf senescence process but also shortens its duration across the continent. This finding indicates that a reduced duration for plants to get ready for dormancy could potentially undermine their resilience to cold and other winter stresses. Contrary to the hypothesized photoperiodic control, TLSO exhibited opposing inter-annual temporal trends along temperature gradients, despite similar daylengths. Ground based observations failed to support photoperiodic influence either, highlighting the substantial spatial variation in TLSO responses to climate change. Intriguingly, complex climate-TLSO interactions were observed, with radiation dominating TLSO changes in 37.0 % of the study area. This analysis underscores the necessity of considering multiple variables to comprehend the patterns of TLSO at continental and vegetation scales.

Confirmation and Characterization of the Eccentric, Warm Jupiter TIC 393818343 b with a Network of Citizen Scientists

NASA’s Transiting Exoplanet Survey Satellite (TESS) has identified over 7000 candidate exoplanets via the transit method, with gas giants among the most readily detected due to their large radii. Even so, long intervals between TESS observations for much of the sky lead to candidates for which only a single transit is detected in one TESS sector, leaving those candidate exoplanets with unconstrained orbital periods. Here, we confirm the planetary nature of TIC 393818343 b, originally identified via a single TESS transit, using radial velocity data and ground-based photometric observations from citizen scientists with the Unistellar Network and Exoplanet Watch. We determine a period of P = 16.24921 −0.00011+0.00010 days, a mass M P = 4.34 ± 0.15 M J, and semimajor axis a = 0.1291 −0.0022+0.0021 au, placing TIC 393818343 b in the “warm Jupiter” population of exoplanets. With an eccentricity e = 0.6058 ± 0.0023, TIC 393818343 b is the most eccentric warm Jupiter to be discovered by TESS orbiting less than 0.15 au from its host star and therefore an excellent candidate for follow-up, as it may inform our future understanding of how hot and warm Jupiter populations are linked.

Telescopic Network of Zhulong for Orbit Determination and Prediction of Space Objects

The increasing proliferation of space debris, intermittent space incidents, and the rapid emergence of massive LEO satellite constellations pose significant threats to satellites in orbit. Ground-based optical observations play a crucial role in space surveillance and space situational awareness (SSA). The Zhulong telescopic observation network stands as a pivotal resource in the realm of space object tracking and prediction. This publicly available network plays a critical role in furnishing essential data for accurately delineating and forecasting the orbit of space objects in Earth orbit. Comprising a sophisticated array of hardware components including precise telescopes, optical sensors, and image sensors, the Zhulong network synergistically collaborates to achieve unparalleled levels of precision in tracking and observing space objects. Central to the network’s efficacy is its ability to extract positional information, referred to as angular data, from consecutive images. These angular data serve as the cornerstone for precise orbit determination and prediction. In this study, the CPF (Consolidated Prediction Format) orbit serves as the reference standard against which the accuracy of the angular data is evaluated. The findings reveal that the angular data error of the Zhulong network remains consistently below 3 arcseconds, attesting to its remarkable precision. Moreover, through the accumulation of angular data over time, coupled with the utilization of numerical integration and least squares methods, the Zhulong network facilitates highly accurate orbit determination and prediction for space objects. These methodologies leverage the wealth of data collected by the network to extrapolate trajectories with unprecedented accuracy, offering invaluable insights into the behavior and movement of celestial bodies. The results presented herein underscore the immense potential of electric optic telescopes in the realm of space surveillance. By harnessing the capabilities of the Zhulong network, researchers and astronomers can gain deeper insights into the dynamics of space objects, thereby advancing our understanding of the cosmos. Ultimately, the Zhulong telescopic observation network emerges as a pioneering tool in the quest to unravel the mysteries of the universe.

Exceptionally gigantic aurora in the polar cap on a day when the solar wind almost disappeared.

Revealing the origins of aurorae in Earth's polar cap has long been a challenge since direct precipitation of energetic electrons from the magnetosphere is not always expected in this region of open magnetic field lines. Here, we introduce an exceptionally gigantic aurora filling the entire polar cap region on a day when the solar wind had almost disappeared. By combining ground-based and satellite observations, we proved that this unique aurora was produced by suprathermal electrons streaming directly from the Sun, which is known as "polar rain." High-sensitivity imaging from the ground has visualized complex spatial structures of the polar rain aurora possibly manifesting the internal pattern of the solar wind or even the organizations in the chromosphere of the Sun.

Study of the response of the upper atmosphere during the annular solar eclipse on October 14, 2023

A solar eclipse is a fascinating event that gives a unique opportunity to study the ionospheric characteristics in a controlled blockage of solar radiation. We present the upper atmosphere’s response, mostly F-layer, during the annular eclipse on October 14, 2023. This eclipse was mostly visible from the North and South American regions with a maximum obscuration of around 91%. We study the response of the ionospheric Total Electron Content (TEC) and the critical frequency of the F2 layer (f0F2) for three consecutive days (October 13 to 15, 2023) using ground and space-based techniques. We present the ionospheric response for a large area that covers the path of the solar eclipse of latitude and longitude range from 50°N to 10°S and 30°W to 150°W. For the ground-based observation, we use fifty-five International GNSS Service (IGS) stations and eleven Global Ionospheric Radio Observatory (GIRO) Digisonde stations that cover the annular and partial solar eclipse and are in the close vicinity of the annularity belt. We compute the vertical TEC (VTEC) and f0F2 from this database and check their temporal and spatial variations. It is found that the VTEC, as computed from the IGS stations, mostly shows decrements during the partial blockage of the sun by the lunar disc. The VTEC, as computed from the GIRO database, also shows a decreasing trend but with comparatively more oscillating in nature. The f0F2 shows a similar decreasing trend due to the solar eclipse. For space-based observation, we use the Swarm satellite data for the tracks that have gone through the annularity belt. Also, a spatiotemporal study is executed using the Global Ionospheric Map (GIM) database. For both cases, the VTEC shows a prominent depletion. We investigate the variation in the percentage change in VTEC as a function of obscuration and observe a positive correlation between them. We also compute the time difference between the maximum eclipse and the maximum TEC modulation to investigate the response time profiles of the ionosphere at different receiving stations. All the methods give satisfactory and consistent outcomes that corroborate the physical mechanism of ionospheric variabilities due to the effect of the solar eclipse. We check the geomagnetic condition to eliminate the scope of contamination and observe that their values are normal during the study.

Verification of Astrometrically Accelerating Stars from Hipparcos and Gaia. I. Methodology and Application to HIP 44842

A large number of candidate binary stars with apparent acceleration on the sky has emerged from analysis of astrometric data collected by the Hipparcos, Tycho-2, and Gaia space missions. Although the apparent acceleration can serve as a relatively reliable indicator of binarity, it provides scarce information about the orbital and physical parameters of the components. With an emphasis on the search for stellar-mass black holes and neutron stars hidden in binary systems, we start a broader effort to characterize the most promising candidates using follow-up ground-based observations. Accurate quantification of orbital and physical parameters of systems with dim or invisible companions requires combination of Hipparcos, Gaia, and precision spectroscopic measurements. In this paper, we review the necessary steps in this implementation and describe the improved Hipparcos–Gaia sample of long-term astrometric accelerations, which includes correction of sky-correlated systematic errors using the vector spherical decomposition method. As an example, we study one Hipparcos star with a large acceleration, HIP 44842, where the companion is revealed to be a normal main-sequence star.

Addressing observational gaps in aerosol parameters using machine learning: Implications to aerosol radiative forcing

The role of atmospheric aerosols has remained hugely uncertain due to lack of long-term systematic observations. This is particularly the case in the global emission hotspot the Indo-Gangetic Plain. In this paper, we apply a machine learning (ML) approach to fill gaps in aerosol observations by combining meteorological reanalysis, and satellite-observations. Aerosol Optical Depth (AOD), Single Scattering Albedo (SSA), and Asymmetry Parameter (AP) are critical parameters for understanding the atmospheric aerosol properties, radiative forcing and climate patterns. We considered AOD, SSA, and AP over an urban site, Kanpur, in the Indo-Gangetic Plain, which is a global hot-spot of high aerosol loading. We used ML technique to fill the observational gaps in AOD, SSA, and AP at 440 nm. Accurate ground-based daily observations of AOD, SSA, and AP from AERONET (Aerosol Robotic Network) over a period of ∼2 decades (2001–2022) are used in the analysis, which have ∼ 37%, ∼62%, and ∼58% data gap, respectively. To reduce observational gaps, an ML model was trained with reanalysis (meteorological parameters) and satellite (angstrom exponent and absorbing aerosol index) datasets by optimally tuning the hyperparameters. The model tested on unseen data showed that it could simulate the AOD (coefficient of determination, R2 = 0.51 and refined index of agreement, RIA = 0.66), SSA (R2 = 0.60 and RIA = 0.70), and AP (R2 = 0.64 and RIA = 0.71), successfully with moderate accuracies: root mean square error of 0.25, 0.018, and 0.018, respectively. This magnitude of error in AOD is comparable to the errors, 0.26 and 0.25, in satellite (MODIS-Moderate Resolution Imaging Spectroradiometer) derived and reanalysis (MERRA-2- Modern-Era Retrospective analysis for Research and Applications, Version 2) AODs, respectively for the study region. When MODIS AOD was included as a predictor in the ML model, it could reproduce AOD with much smaller errors of 0.14, reproducing 84% of total variability. Thus, based on available input data, we reduced observational gaps of daily AOD, SSA, and AP by ∼10% (∼2 years), ∼23% (∼5 years), and ∼21% (∼4 years), respectively, at the study region. Aerosol Radiative Forcing (ARF) at the top-of-atmosphere was estimated using AOD, SSA, and AP both with and without gap-filling. We found that data gaps can significantly impact the estimations of aerosol climate forcing. A considerable change in ARF, ∼ ± 3 Wm−2 indicating that the observational gaps can mislead our understanding of aerosols' impact on climate. This points toward the need for more comprehensive measurements with minimal observational gaps. Our study highlights the possibility of compensating observational gaps by an optimised ML model and that such innovative approach can potentially lead to better understanding of impact of aerosols on climate. Further, the present study could be of immense interest for the community as our validated approach can be applied for other pollutants and for other data-scarce regions in the South and Southeast Asia.

Interpreting Geostationary Environment Monitoring Spectrometer (GEMS) geostationary satellite observations of the diurnal variation in nitrogen dioxide (NO2) over East Asia

Abstract. Nitrogen oxide radicals (NOx≡NO+NO2) emitted by fuel combustion are important precursors of ozone and particulate matter pollution, and NO2 itself is harmful to public health. The Geostationary Environment Monitoring Spectrometer (GEMS), launched in space in 2020, now provides hourly daytime observations of NO2 columns over East Asia. This diurnal variation offers unique information on the emission and chemistry of NOx, but it needs to be carefully interpreted. Here we investigate the drivers of the diurnal variation in NO2 observed by GEMS during winter and summer over Beijing and Seoul. We place the GEMS observations in the context of ground-based column observations (Pandora instruments) and GEOS-Chem chemical transport model simulations. We find good agreement between the diurnal variations in NO2 columns in GEMS, Pandora, and GEOS-Chem, and we use GEOS-Chem to interpret these variations. NOx emissions are 4 times higher in the daytime than at night, driving an accumulation of NO2 over the course of the day, offset by losses from chemistry and transport (horizontal flux divergence). For the urban core, where the Pandora instruments are located, we find that NO2 in winter increases throughout the day due to high daytime emissions and increasing NO2/NOx ratio from entrainment of ozone, partly balanced by loss from transport and with a negligible role of chemistry. In summer, by contrast, chemical loss combined with transport drives a minimum in the NO2 column at 13:00–14:00 local time (LT). Segregation of the GEMS data by wind speed further demonstrates the effect of transport, with NO2 in winter accumulating throughout the day at low winds but flat at high winds. The effect of transport can be minimized in summer by spatially averaging observations over the broader metropolitan scale, under which conditions the diurnal variation in NO2 reflects a dynamic balance between emission and chemical loss.

Precipitation-induced landslide risk escalation in China’s urbanization with high-resolution soil moisture and multi-source precipitation product

Landslides pose a formidable natural hazard. Accurate risk assessment of landslides triggered by precipitation heavily relies on hydrometeorological factors, specifically precipitation and soil moisture. However, the insufficient ground-based observations and the coarse spatio-temporal resolution hinder the performance of landslide prediction. It is not clear what hydrometeorological thresholds and triggering mechanisms are more likely to trigger landslides in China, particularly in the context of rapid urbanization. To address these questions, this study investigated 1504 shallow landslide events in Chinese urban and non-urban areas from 2007 to 2019. It utilized daily 1 km soil moisture at various depths (20–100 cm) and multi-source precipitation datasets, including gauge-based gridded dataset, in conjunction with three multi-source fusion precipitation products (Multi-Source Weighted-Ensemble Precipitation − MSWEP, the Climate Hazards Group InfraRed Precipitation with Station dataset − CHIRPS, and the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement − GPM-IMERG), along with dynamic urban impervious area datasets. It aims to determine the optimal multi-source precipitation predictor, the critical soil moisture depth that triggers landslides, and to establish the hydrometeorological thresholds for landslides. Additionally, the impact of urbanization on landslide occurrences was estimated by comparing antecedent precipitation accumulation, soil moisture, and impervious surface ratio dynamics between urban and non-urban areas. The results indicated that a combination of 2-day cumulative CHIRPS precipitation and 100 cm soil moisture provided the most accurate predictions for landslides in urban regions of China (accuracy = 88.5 %), outperforming interpolated ground-based observations and other fusion products. Specifically, landslides become more prone when antecedent cumulative rainfall surpasses 97.42 mm in 2 days and soil moisture exceeds 39.6 % m3/m3 saturation in China. Urban areas experienced high antecedent precipitation levels, lower precipitation (64.40 mm) threshold and soil moisture threshold (38.9 %), and shorter durations at landslide sites compared to non-urban areas (71.90 mm, 41.4 %, and 7 days, respectively). The process of urbanization is observed to decrease soil moisture levels while concurrently elevating rainfall amounts. This phenomenon, combined with anthropogenic activities, including distance from roads and urban impervious surface expansion, contributes to 44.6 % of the causes of landslides by reducing slope stability and increasing the presence of loose material. These findings have implications for landslide warnings in urban areas with limited measurements and contribute to understanding urbanization’s impact on landslide risks in developing nations.

The 18 May 2024 Iberian superbolide from a sunskirting orbit: USG space sensors and ground-based independent observations

ABSTRACT On 18 May 2024, a superbolide traversed the western part of the Iberian Peninsula, culminating its flight over the Atlantic Ocean and generating significant media attention. This event was caused by a weak carbonaceous meteoroid of 1 m, entering the atmosphere at 40.4 km s$^{-1}$ with an average slope of 8.5$^\circ$. The luminous phase started at 133 km and ended at an altitude of 54 km. The meteoroid’s heliocentric orbit had an inclination of 16.4$^\circ$, a high eccentricity of 0.952, a semimajor axis of 2.4 au, and a short perihelion distance of 0.12 au. The superbolide was recorded by multiple ground-based stations of the Spanish Fireball and Meteorite Network and the European Space Agency, as well as by the U.S. Government sensors from space. Due to the absence of observable deceleration, we successfully reconciled satellite radiometric data with a purely dynamic atmospheric flight model, constraining the meteoroid’s mass and coherently fitting its velocity profile. Our analysis shows a good agreement with the radiant and velocity data reported by the Center for Near-Earth Object Studies, with a deviation of 0.56$^\circ$ and 0.1 km s$^{-1}$, respectively. The presence of detached fragments in the lower part of the luminous trajectory suggests that the meteoroid was a polymict carbonaceous chondrite, containing higher-strength macroscopic particles in its interior due to collisional gardening, or a thermally processed C-type asteroid. The orbital elements indicate that the most likely source is the Jupiter-Family Comet region, aligning with the Solar and Heliospheric Observatory comet family, as its sunskirting orbit is decoupled from Jupiter. This event provides important information to characterize the disruption mechanism of near-Sun objects.

TOI-2374 b and TOI-3071 b: two metal-rich sub-Saturns well within the Neptunian desert

ABSTRACT We report the discovery of two transiting planets detected by the Transiting Exoplanet Survey Satellite (TESS), TOI-2374 b and TOI-3071 b, orbiting a K5V and an F8V star, respectively, with periods of 4.31 and 1.27 d, respectively. We confirm and characterize these two planets with a variety of ground-based and follow-up observations, including photometry, precise radial velocity monitoring and high-resolution imaging. The planetary and orbital parameters were derived from a joint analysis of the radial velocities and photometric data. We found that the two planets have masses of (57 ± 4) M⊕ or (0.18 ± 0.01) MJ, and (68 ± 4) M⊕ or (0.21 ± 0.01) MJ, respectively, and they have radii of (6.8 ± 0.3) R⊕ or (0.61 ± 0.03) RJ and (7.2 ± 0.5) R⊕ or (0.64 ± 0.05) RJ, respectively. These parameters correspond to sub-Saturns within the Neptunian desert, both planets being hot and highly irradiated, with Teq ≈ 745 K and Teq ≈ 1812 K, respectively, assuming a Bond albedo of 0.5. TOI-3071 b has the hottest equilibrium temperature of all known planets with masses between 10 and 300 M⊕ and radii less than 1.5 RJ. By applying gas giant evolution models we found that both planets, especially TOI-3071 b, are very metal-rich. This challenges standard formation models which generally predict lower heavy-element masses for planets with similar characteristics. We studied the evolution of the planets’ atmospheres under photoevaporation and concluded that both are stable against evaporation due to their large masses and likely high metallicities in their gaseous envelopes.

Generation and annihilation of three dimensional magnetic nulls in extrapolated solar coronal magnetic field: data-based Implicit Large Eddy simulation

Three-dimensional (3D) magnetic nulls are the points where magnetic field vanishes and are preferential sites for magnetic reconnection: a fundamental process which converts magnetic energy into kinetic energy, heat, and energy of non-thermal particles along with a rearrangement of magnetic field lines. Reconnection is ubiquitous in nature and plays a major role in various magnetically confined laboratory and space/astrophysical plasmas. In the solar corona, the reconnection manifests as coronal transients including solar flares, coronal mass ejections and coronal jets—often associated with 3D nulls. The nulls are generally found to be collocated with complex active regions on the solar photosphere and merits further attention, particularly in terms of their generation. A recent idealized magnetohydrodynamics simulation initiated with an analytically constructed preexisting proper radial null has identified magnetic reconnection to be responsible for spontaneous generation of these 3D nulls. It is then imperative to further explore the plausibility of spontaneous generation of nulls in naturally occurring plasmas, identify the mechanism and verify the outcome vis-à-vis observations. An apt test bed for such an initiative is the solar atmosphere, as abundant space and ground-based observations are available. In the above backdrop, the paper attempts to investigate 3D null generation by carrying out a data-based simulation of a C6.6 class flare associated with the photospheric active region NOAA 11 977. The simulation confirms spontaneous pairwise generation of 3D nulls with magnetic reconnections as the underlying cause. Importantly, magnetic field lines associated with the spontaneously generated nulls are found to trace observed chromospheric bright points—highlighting their observational relevance. Overall, such spontaneous generation and annihilation of nulls through magnetic reconnections opens up a new avenue for solar coronal and chromospheric heating.