Feed aggregator

Alongside magnetic reconnection, turbulence is another fundamental nonlinear plasma phenomenon that plays a key role in energy transport and conversion in space and astrophysical plasmas. From a numerical, theoretical, and observational point of view there is a long history of exploring the interplay between these two phenomena in space plasma environments; however, recent high-resolution, multi-spacecraft observations have ushered in a new era of understanding this complex topic. The interplay between reconnection and turbulence is both complex and multifaceted, and can be viewed through a number of different interrelated lenses - including turbulence acting to generate current sheets that undergo magnetic reconnection (turbulence-driven reconnection), magnetic reconnection driving turbulent dynamics in an environment (reconnection-driven turbulence) or acting as an intermediate step in the excitation of turbulence, and the random diffusive/dispersive nature of the magnetic field lines embedded in turbulent fluctuations enabling so-called stochastic reconnection. In this paper, we review the current state of knowledge on these different facets of the interplay between turbulence and reconnection in the context of collisionless plasmas, such as those found in many near-Earth astrophysical environments, from a theoretical, numerical, and observational perspective. Particular focus is given to several key regions in Earth’s magnetosphere – namely, Earth’s magnetosheath, magnetotail, and Kelvin-Helmholtz vortices on the magnetopause flanks – where NASA’s Magnetospheric Multiscale mission has been providing new insights into the topic.

We propose a benchmark for comparing gap-filling techniques used on global time-variable gravity field time-series. The Gravity Recovery and Climate Experiment (GRACE) and the GRACE Follow-On missions provide products to study the Earth’s time-variable gravity field. However, the presence of missing months in the measurements poses challenges for understanding specific Earth processes through the gravity field. We reproduce, adapt, and compare satellite-monitoring and interpolation techniques for filling these missing months in GRACE and GRACE Follow-On products on a global scale. Satellite-monitoring techniques utilize solutions from Swarm and satellite laser ranging, while interpolation techniques rely on GRACE and/or Swarm solutions. We assess a wide range of interpolation techniques, including least-squares fitting, principal component analysis, singular spectrum analysis, multichannel singular spectrum analysis, auto-regressive models, and the incorporation of prior data in these techniques. To inter-compare these techniques, we employ a remove-and-restore approach, removing existing GRACE products and predicting missing months using interpolation techniques. We provide detailed comparisons of the techniques and discuss their strengths and limitations. The auto-regressive interpolation technique delivers the best score according to our evaluation metric. The interpolation based on a least-squares fitting of constant, trend, annual, and semi-annual cycles offers a simple and effective prediction with a good score. Through this assessment, we establish a starting benchmark for gap-filling techniques in Earth’s time-variable gravity field analysis.

Inverting fault geometry and slip distribution simultaneously with geodetic observations based on Bayesian theory is becoming increasingly prevalent. A widely used approach, proposed by (Fukuda and Johnson, Geophys J Int 181:1441–1458, 2010) (F-J method), employs the least-squares method to solve the linear parameters of slip distribution after sampling the nonlinear parameters, including fault geometry, data weights and smoothing factor. Here, we present a modified version of the F-J method (MF-J method), which treats data weights and the smoothing factor as hyperparameters not directly linked to surface deformation. Additionally, we introduce the variance component estimation (VCE) method to resolve these hyperparameters. To validate the effectiveness of the MF-J method, we conducted inversion tests using both synthetic data and a real earthquake case. In our comparison of the MF-J and F-J methods using synthetic experiments, we found that the F-J method's inversion results for fault geometry were highly sensitive to the initial values and step sizes of hyperparameters, whereas the MF-J method exhibited greater robustness and stability. The MF-J method also exhibited a higher and more stable acceptance rate, enabling convergence to simulated values and ensuring greater accuracy of the parameter estimation. Furthermore, treating the fault length and width as unknown parameters and solving them simultaneously with other fault geometry parameters and hyperparameters using the MF-J method successfully resolved the issue of non-uniqueness in fault location solutions caused by the excessively large no-slip areas. In the 2017 Mw 7.3 Sarpol-e Zahab earthquake case study, the MF-J method produced a fault slip distribution with low uncertainty that accurately fit surface observation data, aligning with results from other research institutions. This validated the method's applicability and robustness in real-world scenarios. Additionally, we inferred that the second slip asperity was caused by early afterslip.

Seismic and electromagnetic (EM) imaging are essential tools for characterizing velocity and conductivity. However, the separate inversion of seismic and EM data is challenging due to the noisy measurements, inadequate data collection, and reliance on prior information, consequently resulting in uncertainty and ambiguity of the solutions. Moreover, the two methods are different in sensitivity and spatial resolution, making it difficult to discover consistencies in the inverted models. Joint inversion of seismic and EM data takes advantage of both methods and significantly improves the imaging capability of subsurface structures. In this paper, we review various coupling strategies for the joint inversion of seismic and EM data and highlight the application advances from 1-D to 3-D inversion. Specifically, we investigate the integration of machine learning techniques to tackle ill-posed inverse problems and showcase their effectiveness in coupling. Following this, we construct a deep-learning-based joint inversion workflow and provide a synthetic test to demonstrate its superiority by applying an attention mechanism, which enhances the model’s capability to focus on specific features within the data. This study proves the potential of integrating artificial intelligence into joint inversion and understanding the deep Earth interior by incorporating multiple geophysical data.

NASA’s Europa Clipper mission is designed to provide a diversity of measurements to further our understanding of the potential habitability of this intriguing ocean world. The Europa mission’s Ultraviolet Spectrograph (Europa-UVS), built at the Southwest Research Institute (SwRI), is primarily a “plume finder” and tenuous atmosphere investigation. The science objectives of Europa-UVS are to: 1) Search for and characterize any current activity, notably plumes; and 2) Characterize the composition and sources of volatiles to identify the signatures of non-ice materials, including organic compounds, in the atmosphere and local space environment. Europa-UVS observes photons in the 55–206 nm wavelength range at moderate spectral and spatial resolution along a 7.5° slit composed of 7.3°×0.1° and 0.2°×0.2° contiguous sections. A variety of observational techniques including nadir pushbroom imaging, disk scans, stellar and solar occultations, Jupiter transit observations, and neutral cloud/plasma torus stares are employed to perform a comprehensive study of Europa’s atmosphere, plumes, surface, and local space environment. This paper describes the Europa-UVS investigation’s science plans, instrument details, concept of operations, and data formats in the context of the Europa Clipper mission’s primary habitability assessment goals.

This review article provides a synthesis and perspective on how weather and climate extreme events can play a role in influencing tipping elements and triggering tipping points in the Earth System. An example of a potential critical global tipping point, induced by climate extremes in an increasingly warmer climate, is Amazon rainforest dieback that could be driven by regional increases in droughts and exacerbated by fires, in addition to deforestation. A tipping element associated with the boreal forest might also be vulnerable to heat, drought and fire. An oceanic example is the potential collapse of the Atlantic meridional overturning circulation due to extreme variability in freshwater inputs, while marine heatwaves and high acidity extremes can lead to coral reef collapse. Extreme heat events may furthermore play an important role in ice sheet, glacier and permafrost stability. Regional severe extreme events could also lead to tipping in ecosystems, as well as in human systems, in response to climate drivers. However, substantial scientific uncertainty remains on mechanistic links between extreme events and tipping points. Earth observations are of high relevance to evaluate and constrain those links between extreme events and tipping elements, by determining conditions leading to delayed recovery with a potential for tipping in the atmosphere, on land, in vegetation, and in the ocean. In the subsurface ocean, there is a lack of consistent, synoptic and high frequency observations of changes in both ocean physics and biogeochemistry. This review article shows the importance of considering the interface between extreme events and tipping points, two topics usually addressed in isolation, and the need for continued monitoring to observe early warning signs and to evaluate Earth system response to extreme events as well as improving model skill in simulating extremes, compound extremes and tipping elements.

The 3D ionosphere structure is of interest in many fields such as radio frequency communication and global navigation satellite system (GNSS) applications. However, the limited temporal and spatial coverage of measurements poses a challenge for 3D electron density modeling. To overcome this challenge, we explore the use of kriging interpolation technique. The kriging interpolation is performed to obtain 3D representation of the ionosphere over electron density measurements retrieved by GNSS radio-occultation (RO) data. RO measurements are first reduced to “shape function,” the ratio of electron density to vertical total electron content (VTEC), aiming to create a background model. Then, the empirical residual semivariogram is analyzed for variation characteristics of the shape functions under different solar geomagnetic conditions. Finally, 3D kriging is adopted for shape function interpolation. Compared to the modeling results without kriging, the maximum root mean square error (RMSE) reduction reaches \(3.4\times {10}^{-4}~\text {km}^{-1}\) , which amounts to \(3.4\times {10}^{11}~\text {el/m}^{3}\) of electron density when VTEC is assumed as 100 TECU. This improvement accounts for 17.8% of root mean square (RMS) of shape function.

The spherical radial basis function (SRBF) approach, widely used in gravity modeling, is theoretically surveyed from a viewpoint of random field theory. Let the gravity potential be a random field which is represented as an integral functional of another random field, namely an isotropic Gaussian random field (IGRF) on a sphere inside the Bjerhammar sphere with the SRBF as the integral kernel. When the integration is approximated by a discrete sum within a local region, one gets the widely applicable SRBF model. With this theoretical study, the following two findings are made. First, the IGRF implies a Gaussian prior on the spherical harmonic coefficients (SHCs) of the gravity potential; for this prior the SHCs are independent with each other and their variances are degree-only dependent. This should be reminiscent of two well-known priors, namely the power-law Kaula’s rule and the asymptotic power-law Tscherning-Rapp model. Second, the IGRF-SRBF representation is non-unique. Benefiting from this redundant representation, one can employ a simple IGRF, e.g., the simplest white field, and then design the SRBF accordingly to represent a potential with desired prior statistical properties. This can simplify the corresponding SRBF modeling significantly; to be more specific, the regularization matrix in parameter estimation of the SRBF modeling can be chosen to be a diagonal matrix, or even the naïve identity matrix.

In recent years, coseismic velocity from high-rate global navigation satellite systems (GNSS) carrier phase data has been widely utilized to estimate instrumental seismic intensity, thereby guiding earthquake early warning and emergency response. However, using carrier phase data only yields displacement, displacement increment, and average velocity but not instantaneous velocity at the epoch level. In large earthquakes, using average velocity over a brief time span (e.g., 1 s) to quantify instantaneous coseismic velocity is less reliable for recovering accurate deformation dynamics, especially for the near-field region. In this study, we first introduce GNSS raw Doppler-based instantaneous velocity into seismology, expanding carrier phase-based traditional GNSS seismology. We also propose a new integrated GNSS velocity estimation method that employs a Kalman filter to integrate raw Doppler-based instantaneous velocity and carrier phase-based average velocity. The GNSS data from shake table experiments and two real-world earthquake events (i.e., the 2016 Mw 6.6 Norcia earthquake and the 2011 Mw 9.1 Tohoku-oki earthquake) are used to investigate the impact of high-rate GNSS raw Doppler on capturing coseismic velocity waveforms and predicting instrumental seismic intensity. The simulated sine wave experiment results indicate that the accuracy of instantaneous and average velocity for the 1 Hz sampling rate case is 1.20 cm/s and 12.67 cm/s, respectively. A similar case holds for the simulated quake wave experiment. The retrospective analysis of the ultra-high-rate (20 Hz) GNSS data for the Norcia earthquake shows the average velocities exhibit more aliasing and have a smaller peak ground velocity value than instantaneous velocities in all cases (i.e., 1, 2, 4, 5, 10, and 20 Hz). For the 2011 Mw 9.1 Tohoku-oki earthquake, results show that incorporating raw Doppler data enhances the consistency between the GNSS intensity map and the United States Geological Survey intensity map for near-field regions. Therefore, high-rate GNSS RD data as it becomes more widely available should be incorporated into data processing of high-rate GNSS seismology to capture more accurate instantaneous coseismic velocity waveforms and predict more realistic instrumental seismic intensity in future analyses.

Global Navigation Satellite Systems (GNSS) applications require computation of the geometric range between the satellite vehicle at the time-of-signal transmission and the receiver antenna location at the time-of-signal reception. This computation requires attention to the frames of reference due to the rotation of the Earth-Centered Earth-Fixed (ECEF) frame during the time-of-signal propagation. Three range computation approaches are commonplace and will be discussed herein. The first is the Global Positioning System Interface Control Document recommendation to rotate the ECEF frames to a common reference time. The other two are forms of the Sagnac correction. The Sagnac derivations already in the literature are either limited to stationary receivers or lack the connection between the Earth-centered inertial (ECI) and ECEF frames. Neither form of the Sagnac correction exactly reproduces the geometric range. They are approximations. The literature does not currently contain an analysis of the error involved in using either form of the Sagnac correction. This article makes two contributions: (1) it presents derivations for both forms of the Sagnac correction that are valid for moving receivers and that maintain the connection between the ECI and ECEF frames; and (2) it analyzes the error of the Sagnac correction for orbits of different radius. The analysis shows that Sagnac corrections introduce range errors less than \(7.57\times 10^{-4}\) meters for GNSS satellites at medium Earth orbit.

We provide an overview of the isotopic signatures of presolar supernova grains, specifically focusing on 44Ti-containing grains with robustly inferred supernova origins and their implications for nucleosynthesis and mixing mechanisms in supernovae. Recent technique advancements have enabled the differentiation between radiogenic (from 44Ti decay) and nonradiogenic 44Ca excesses in presolar grains, made possible by enhanced spatial resolution of Ca-Ti isotope analyses with the Cameca NanoSIMS (Nano-scale Secondary Ion Mass Spectrometer) instrument. Within the context of presolar supernova grain data, we discuss (i) the production of 44Ti in supernovae and the impact of interstellar medium heterogeneities on the galactic chemical evolution of 44Ca/40Ca, (ii) the nucleosynthesis processes of neutron bursts and explosive H-burning in Type II supernovae, and (iii) challenges in identifying the progenitor supernovae for 54Cr-rich presolar nanospinel grains. Drawing on constraints and insights derived from presolar supernova grain data, we also provide an overview of our current understanding of the roles played by various supernova types – including Type II, Type Ia, and electron capture supernovae – in accounting for the diverse array of nucleosynthetic isotopic variations identified in bulk meteorites and meteoritic components. We briefly overview the potential mechanisms that have been proposed to explain these nucleosynthetic variations by describing the transport and distribution of presolar dust carriers in the protoplanetary disk. We highlight existing controversies in the interpretation of presolar grain data and meteoritic nucleosynthetic isotopic variations, while also outlining potential directions for future research.

Strong gravitational lensing at the galaxy scale is a valuable tool for various applications in astrophysics and cosmology. Some of the primary uses of galaxy-scale lensing are to study elliptical galaxies’ mass structure and evolution, constrain the stellar initial mass function, and measure cosmological parameters. Since the discovery of the first galaxy-scale lens in the 1980s, this field has made significant advancements in data quality and modeling techniques. In this review, we describe the most common methods for modeling lensing observables, especially imaging data, as they are the most accessible and informative source of lensing observables. We then summarize the primary findings from the literature on the astrophysical and cosmological applications of galaxy-scale lenses. We also discuss the current limitations of the data and methodologies and provide an outlook on the expected improvements in both areas in the near future.

Satellite remote sensing serves as a crucial means to acquire cloud physical parameters. However, existing official cloud products from the advanced geostationary radiation imager (AGRI) onboard the Fengyun-4A geostationary satellite lack spatiotemporal continuity and important micro-physical properties. In this study, an image-based transfer learning ResUnet (TL-ResUnet) model was applied to realize all-day and high-precision retrieval of cloud physical parameters from AGRI thermal infrared measurements. Combining the observation advantages of geostationary and polar-orbiting satellites, the TL-ResUnet model was pre-trained with official cloud products from advanced Himawari imager (AHI) and transfer-trained with official cloud products from moderate resolution imaging spectroradiometer (MODIS), respectively. For comparison, a pixel-based transfer learning random forest (TL-RF) model was trained using the equally distributed data sets. Taking MODIS official products as the benchmarks, the TL-ResUnet model achieved an overall accuracy of 79.82% for identifying cloud phase and root mean squared errors of 1.99 km, 7.11 μm, and 12.87 for estimating cloud top height, cloud effective radius, and cloud optical thickness, outperforming the precision of AGRI and AHI official products. Compared to the TL-RF model, the TL-ResUnet model utilized the spatial information of clouds to significantly improve the retrieval performance and achieve more than a 6-fold increase in speed for single full-disk retrieval. Moreover, AGRI TL-ResUnet products with spatiotemporal continuity and high precision were used to accurately describe the spatial distribution characteristics of cloud fractions and cloud properties over the Tibetan Plateau, and provide the diurnal variation of cloud cover and cloud properties across different seasons for the first time.

Substorms are a rapid release of energy that is redistributed throughout the magnetosphere-ionosphere system, resulting in many observable signals, such as enhancements in the aurora, energetic particle injections, and ground magnetic field perturbations. Numerous substorm identification techniques and onset lists based on each of these signals have been provided in the literature, but often with no cross-calibration. Since the signals produced are not necessarily unique to substorms and may not be sufficiently similar to be identified for each and every substorm, individual event lists may miss or misidentify substorms, hindering our understanding and the development and validation of substorm models. To gauge the scale of this problem, we use metrics derived from contingency tables to quantify the association between lists of substorms derived from SuperMAG SML/SMU indices, midlatitude magnetometer data, particle injections, and auroral enhancements. Overall, although some degree of pairwise association is found between the lists, even lists generated by applying conceptually similar gradient-based identification to ground magnetometer data achieve an association with less than 50% event coincidence. We discuss possible explanations of the levels of association seen from our results, as well as their implications for substorm analyses.

Several of the icy moons in the Jupiter and Saturn systems appear to possess internal liquid water oceans. Our knowledge of the Uranian moons is more limited but a future tour of the system has the potential to detect subsurface oceans. Planning for this requires an understanding of how the moons' internal structures—with and without oceans—relate to observable quantities. Here, we show that the amplitude of forced physical librations could be diagnostic of the presence or absence of subsurface oceans within the Uranian moons. In the presence of a decoupling global ocean, ice shell libration amplitudes at Miranda, Ariel, and Umbriel will exceed 100 m if the shells are <30 ${< } 30$ km $\mathrm{k}\mathrm{m}$ thick. The presence of oceans could also imply significant tidal heating within the last few hundred million years. Combining librations with the quadrupole gravity field could provide comprehensive constraints on the internal structures and histories of the Uranian moons.

Recent increase in extreme wildfire events has led to major health and environmental consequences across the globe. These adverse impacts underlined the need for better understanding of this phenomenon and to formulate mitigating actions. While previous research has focused on local weather drivers of wildfires, our knowledge about their large-scale climatic controls remains limited, especially in tropical Africa, which stands out as a global hotspot for fire emissions. Here, we show that interannual variability of carbon emission due to fires in the southern Congo Basin is strongly linked to low-level winds that are controlled by the Indian Ocean subtropical high. The interhemispheric transport of these emissions to West Africa relies on the intensity and position of both Indian and South Atlantic subtropical highs. Combined effects of this transport mechanism and carbon production in the source region explain a majority of the interannual variability of black carbon in West Africa.

The Northeast Greenland Ice Stream (NEGIS) has experienced substantial dynamic thinning in recent years. Here, we examine the evolving behavior of NEGIS, with focus on summer speedup at Zachariae Isstrøm, one of the NEGIS outlet glaciers, which has exhibited rapid retreat and acceleration, indicative of its vulnerability to changing climate conditions. Through a combination of Sentinel-1 data, in-situ GPS observations, and numerical ice flow modeling from 2007, we investigate the mechanisms driving short-term changes. Our analysis reveals a summer speedup in ice flow both near the terminus and inland, with satellite data detecting changes up to 60 km inland, while GPS data capture changes up to 190 km inland along the glacier center line. We attribute this summer speedup to variations in subglacial hydrology, where surface meltwater runoff influences basal friction over the melt season. Incorporating subglacial hydrology into numerical models makes it possible to replicate observed ice velocity patterns.

We evaluate tropical cyclones (TCs) in a set of thermodynamic global warming (TGW) simulations over the continental United States (CONUS). A 12 km simulation forced by ERA5 provides a 40-year historical (1980–2019) control. Four complimentary future scenarios are generated using thermodynamic deltas applied to lateral boundary, interior, and surface forcing. We curate a data set of 4,498 6-hourly TC snapshots in the control and find a corresponding “twin” in each counterfactual, permitting a paired comparison. Warming results in an increase in mean dynamical TC intensity and moisture-related quantities, with the latter being more pronounced. TC inner cores contract slightly but outer storm size remains unchanged. The frequency with which TCs become more intense is only moderately consistent, with snapshots having increased hazards ranging from 50% to 80% depending on warming level. The fractions of TCs undergoing rapid intensification and weakening both increase across all warming simulations, suggesting elevated short-term intensity variability.

We conduct a global hybrid simulation of an observation event to affirm that an interplanetary (IP) shock can drive significant suprathermal (tens to hundreds of eV) H+ outflows from the polar cap. The event showed that a spacecraft in the lobe at ∼6.5 R E altitude above the polar cap observed the appearance of suprathermal outflowing H+ ions about 8 min after observing enhanced downward DC Poynting fluxes caused by the shock impact. The simulation includes H+ ions from both the solar wind and the ionospheric sources. The cusp/mantle region can be accessed by ions from both sources, but only the outflow ions can get into the lobe. Despite that upward flowing solar wind ions can be seen within part of the cusp/mantle region and their locations undergo large transient changes in response to the magnetosphere compression caused by the shock impact, the simulation rules out the possibility that the observed outflowing H+ ions was due to the spacecraft encountering the moving cusp/mantle. On the other hand, the enhanced downward DC Poynting fluxes caused by the shock impact drive more upward suprathermal outflows, which reach higher altitudes a few minutes later, explaining the observed time delay. Also, these simulated outflowing ions become highly field-aligned in the upward direction at high altitudes, consistent with the observed energy and pitch-angle distributions. This simulation-observation comparison study provides us the physical understanding of the suprathermal outflow H+ ions coming up from the polar cap.

Magnetospheric convection is a fundamental process in the coupling of the solar wind, magnetosphere, and ionosphere. Recent studies have shown that dayside magnetopause reconnection drives magnetospheric convection, progressing from the dayside to the nightside within approximately 10–20 min in response to southward turning of the interplanetary magnetic field. In this study, we use global magnetohydrodynamic (MHD) simulations to investigate the influence of ionospheric conductance on dayside-driven convection. We conduct three simulation runs: two with normal ionospheric conductance and one with nearly infinite conductance. The temporal and spatial pattern of magnetospheric convection largely remain consistent across all three simulation runs. Comparing the results, we observe a reduction of 20% in magnetospheric convection and a 30% increase of ionospheric Region 1 field-aligned current (FAC) and Pedersen current in the run with nearly infinite conductance, compared to the normal conductance model. The results indicate that ionospheric conductance does not affect the response time of enhanced magnetospheric convection to the solar wind. We suggest that the 10–20 min timescale for establishing magnetospheric convection corresponds to the anti-sunward drag of reconnected magnetic field lines from the sub-solar point to the flank magnetopause. In cases of larger ionospheric conductance, the ionosphere footprints of dragged field lines become more stationary, potentially resulting in larger Region 1 FAC and ionosphere Pedersen current. A larger Pedersen current is associated with stronger sunward J × B force in the ionosphere, which corresponds to a stronger anti-sunward force in the magnetosphere, thereby reducing sunward convection of closed field lines.