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As the world tries to mitigate agriculture's effect on the environment, much of the story can be found in soil, which stores and cycles nutrient elements of carbon, nitrogen, and phosphorus.

The high-Andean wetlands of the Argentinean Puna region, called "vegas" by local inhabitants, although covering less than 1% of this arid mountain region, are important ecosystems as they support biodiversity and provide local people with fresh water and food for their livestock.

Imagine you had a crystal ball that revealed when a volcano would next erupt. For the hundreds of millions of people around the world who live near active volcanoes, it would be an extremely useful device.

Abstract

We study how the normal stiffness and the permeability of a realistic rough fracture at the field scale are linked and evolve during its closure up to the percolation threshold. We base our approach on a well-established self-affine geometric model for fracture roughness, which has proven to be a relevant proxy from laboratory to multi-kilometer scales. We explore its implications for fracture apertures in reservoir-scale open channels. We build our approach on a finite element model using the MOOSE/GOLEM framework and conduct numerical flow-through experiments in a 256×256× $256\times 256\times $ 256 m3 ${\mathrm{m}}^{3}$ granite reservoir hosting a single, partially sealed fracture under variable normal loading conditions and undrained conditions. Navier-Stokes flow is solved in the embedded 3-dimensional rough fracture, and Darcy flow is solved in the surrounding poroelastic matrix. We study the evolution of the mechanical stiffness and fluid permeability of the fracture-rock system during fracture closure including mechanisms that impact the contact surface geometry like asperity yield and deposit of fracture-filling material in the open space of the rough fracture. The largely observed stiffness characteristic is shown to be related to the self-affine property of the fracture surface. A strong anisotropy of the fracture permeability is evidenced when the fluid percolation thresholds are exceeded in two orthogonal directions of the imposed pressure gradient. We propose a unifying physically based law for the evolution of stiffness and permeability in the form of an exponential increase in stiffness as permeability decreases.

Abstract

Volcanic eruptions and wildfires can impact stratospheric chemistry. We apply tracer-tracer correlations to satellite data from Atmospheric Chemistry Experiment—Fourier Transform Spectrometer and the Halogen Occultation Experiment at 68 hPa to consistently compare the chemical impact on HCl after multiple wildfires and volcanic eruptions of different magnitudes. The 2020 Australian New Year (ANY) fire displayed an order of magnitude less stratospheric aerosol extinction than the 1991 Pinatubo eruption, but showed similar large changes in mid-latitude lower stratosphere HCl. While the mid-latitude aerosol loadings from the 2015 Calbuco and 2022 Hunga volcanic eruptions were similar to the ANY fire, little impact on HCl occurred. The 2009 Australian Black Saturday fire and 2021 smoke remaining from 2020 yield small HCl changes, at the edge of the detection method. These observed contrasts across events highlight greater reactivity for smoke versus volcanic aerosols at warm temperatures.

Abstract

Atlantic Meridional Overturning Circulation (AMOC) variability originates from a large number of interacting processes with multiple time scales, with dominant processes dependent on both the latitude and timescale of interest. Here, we isolate the optimal atmospheric modes driving climate-relevant decadal AMOC variability using a novel approach combining dynamical and statistical attribution (dynamics-weighted principal component, or DPC analysis). We find that for both the subpolar (55°N) and subtropical (25°N) AMOC, the most effective independent mode of heat flux forcing closely resembles the North Atlantic Oscillation, and drives meridionally coherent AMOC anomalies through western boundary density anomalies. Conversely, established modes of wind stress variability possess limited quantitative similarity to the optimal wind stress patterns, which generate low-frequency AMOC fluctuations by rearranging the ocean buoyancy field. We demonstrate (by running a modified version of the ECCOv4r4 state estimate) that most AMOC variability on decadal time scales can be explained by the DPCs.

Abstract

Lightning is the severest threaten to safe operation of wind turbines. In this letter, the authors present the first altitude-triggered lightning experiment involving an elevated 12 m-long wind turbine blade placed on the ground. A total of 50 precursors with amplitude over 62.9 A were observed through measurements of channel-base current, fast electric field, and optical data. The air gap with around 3–5 m has been bridged between the wire's lower extremity and the metal blade tip during ascending of the rocket and it is observed to be luminous by slow framing rate camera. The precursors are classified into three groups, namely bipolar pulses, unipolar pulses, and group pulses. Excluding the precursors preceding the initial stage and M-components at the late-time of the initial stage, five stages are classified. In the first stage, the current remains limited at a relatively small value, while the electric field exhibited a slow rising behavior with positive polarity. In the second stage, the current starts to increase, and the electric field rapidly intensifies due to the accumulating charge, and the wire is assumed to experience an explosion. In the third stage, the reconnection process occurs. The current is characterized by a peak value of 1.45 kA with 10%–90% risetime of 10.4 μs. The electric fields suffer from notable decrease and are characterized by a microsecond-scale V-shape pulse. The current cutoff is quite short that almost not found. In the fourth step, the current is characterized by superimposed pulses. The final stage is the channel darkening.

Abstract

Subthermocline eddies (SEs) influencing ocean circulation are progressively known, yet their extensive impact on the western Pacific undercurrent system remains largely unexplored and, in some regions, often underestimated. Okubo-Weiss parameter analysis reveals a distinctive meridional pattern of cyclonic and anticyclonic SE distribution in the interior western Pacific basin that aligns with zonally elongated mean flows. These westward-propagating SEs play a pivotal role in regulating the formation of zonal undercurrents, particularly off-equatorial regions, through the convergence of eddy potential vorticity flux. Along the Pacific western boundary region, anticyclonic SEs typically enhance (reverse) the velocity of boundary currents flowing northward (southward), primarily through barotropic energy conversion, while cyclonic SEs do the opposite. To summarize, we provide a schematic map of the circulation system in the western Pacific and emphasize the interconnected framework of undercurrents, particularly in relation to SEs.

Abstract

We develop a 3D full waveform inversion (FWI) method based on dynamic time warping (DTW) to address the issue of cycle-skipping, which can prohibit the convergence of conventional FWI methods. DTW globally compares data samples at different time steps in 2D matrices against the time shifts of waveforms. We introduce the concept of shape descriptors into softDTW, creating a soft-shapeDTW objective function within our waveform inversion process to improve alignment accuracy. Additionally, including constraints from Sakoe-Chiba bands in the inversion further enhances efficiency and overall performance. A synthetic test has shown that the soft-shapeDTW inversion outperforms conventional waveform inversions in overcoming the cycle-skipping challenges that arise from poor initial models. This method was applied to fit observed seismograms to reveal western Yunnan's crustal structure. Seismic waveforms were recorded by 88 broadband stations from 10 local earthquakes, which were then denoised using a continuous wavelet transform method. Generalized cut and paste waveform inversions were used to determine the source parameters of these seismic events. Our inversion well-aligned various seismic phases in the selected time windows of seismograms, and the resolved velocity models well associate with local geological structure. Results suggest that the soft-shapeDTW inversion offers a robust alternative to FWI, reducing the reliance on accurate starting models.

Abstract

The 2022 eruption at Mauna Loa, Hawai'i, marked the first extrusive activity from the volcano after 38 years of quiescence. The eruption was preceded by several years of seismic unrest in the vicinity of the volcano's summit. Characterizing the structure and dynamics of seismogenic features within Mauna Loa during this pre-eruptive interval may provide insights into how pre- and co-eruptive processes manifest seismically at the volcano. In particular, the extent to which seismicity may be used to forecast the location and timing of future eruptions is unclear. To address these questions, we construct a catalog of relocated seismicity on Mauna Loa spanning 2011–2023. Our earthquake locations image complex, sub-kilometer-scale seismogenic structures in the caldera and southwest rift zone. We additionally identify a set of streaks of seismicity in the volcano's northwest flank that are radially oriented about the summit. Using a rate-and-state friction model for earthquake occurrences, we demonstrate that the seismicity rate in this region can be modeled as a function of the stressing history caused by magma accumulation beneath the summit. Finally, we observe a mid-2019 step change in the seismicity rate in the Ka'oiki region that may have altered the stress state of the northeast rift zone in the three years before the eruption. Our observations provide a framework for interpreting future seismic unrest at Mauna Loa.

Abstract

Mid-latitude auroras are conventionally generated during intense magnetic storms. However, mid-latitude auroras were observed by naked eyes at Beijing China (39°N, 116°E) unusually during a moderate storm event on 1 December 2023 with the minimum Sym-H index only ∼ ${\sim} $ −120 nT. This study combines conjugative in-site and ground-based observations to analyze the auroras and underlying physical processes. Results indicate that both electron and proton auroras appeared at low latitudes. Electron auroras predominantly arise from low-energy electron precipitation, but proton auroras may be explained by energetic tens of keV proton precipitation. Pc1/EMIC waves are observed at low latitudes in the ionosphere, potentially accounting for mid-latitude proton auroras. Downward field-aligned currents (FACs) are also detected at low latitudes, producing significant magnetic perturbations. This study reveals the underlying ionospheric responses to the mid-latitude auroras to understand potential reasons for observing aurora at such mid-latitudes during a moderate storm.

Abstract

The South China Sea is a typical marginal sea characterized by the presence of numerous seamounts. However, the effect of seamounts on the air-sea CO2 flux has not yet been well studied. In September 2021, the air-sea CO2 flux was measured directly using eddy covariance (EC), and discrete waterside sampling was conducted. The results indicate that the northern South China Sea is a source of atmospheric CO2. Furthermore, EC measurements show that the seamount emits CO2 at an average rate of 0.34 mmol m−2 hr−1, nearly double that of non-seamount areas. We suggest that the upwelling around the seamount transports deep water rich in dissolved inorganic carbon to the upper ocean, increasing the partial pressure of CO2 there. In addition, the increase in nutrients caused by the upwelling would increase the concentration of chlorophyll-a, resulting in a productive area that emits CO2.

Abstract

In areas of induced seismicity, earthquakes can be triggered by stress changes due to fluid injection and static deformation from fault slip. Here we present a method to distinguish between injection-driven and earthquake-driven triggering of induced seismicity by combining a calibrated, fully coupled, poroelastic stress model of wastewater injection with interpretation of a machine learning algorithm trained on both earthquake catalog and modeled stress features. We investigate seismicity from Paradox Valley, Colorado as an ideal test case: a single, high-pressure injector that has induced thousands of earthquakes since 1991. Using feature importance analysis, we find that injection-driven earthquakes are approximately 22± $\pm $5% of the total catalog but act as background events that can trigger subsequent aftershocks. Injection-driven events also have distinct spatiotemporal clustering properties with a larger b-value, closer proximity to the well, and earlier occurrence in the injection history. Generalization of our technique can help characterize triggering processes in other regions where induced seismicity occurs.

Abstract

We estimate the global impact of storms on the global structure and dynamics of the night side plasma sheet from observations by the NASA mission Time History of Events and Macroscale Interactions during Substorms (THEMIS). We focus on an intense storm occurring in December 2015 triggered by interplanetary coronal mass ejections (ICMEs). It starts with a storm sudden commencement (SSC) phase (SYM-H ∼ ${\sim} $ +50 nT) followed by a growth phase (SYM-H ∼ ${\sim} $ −188 nT at the minimum) and then a long recovery phase lasting several days. We investigate THEMIS observations when the spacecraft were located in the midnight sector of the plasma sheet at distances typically between 8 and 13 Earth's radii. It is found that the plasma sheet has been globally compressed up to a value of about ∼> ${\sim} > $4 nPa during the SSC and main phases, that is, 8 times larger than its value during the quiet phase before the event. This compression occurs during periods of high dynamic pressure in the ICME (20 nPa) about one order of magnitude larger than its value in the pristine solar wind. We infer a global increase of the lobe magnetic field from 30 to 100 nT, confirmed by THEMIS data just outside the plasma sheet. During the SSC and main phases, the plasma sheet is found thinner by a factor of 2 relative to its thickness at quiet times, while the Tsyganenko T96 magnetic field model shows very stretched magnetic field lines from inner magnetospheric regions toward the night side. During the recovery phase, whereas the interplanetary pressure has dropped off, the plasma sheet tends to gradually recover its quiet phase characteristics (pressure, thickness, magnetic configuration, etc.) during a long recovery phase of several days.

Abstract

In recent years, significant progress has been in ionospheric modeling research through data ingestion and data assimilation from a variety of sources, including ground-based global navigation satellite systems, space-based radio occultation and satellite altimetry (SA). Given the diverse observing geometries, vertical data coverages and intermission biases among different measurements, it is imperative to evaluate their absolute accuracies and estimate systematic biases to determine reasonable weights and error covariances when constructing ionospheric models. This study specifically investigates the disparities among the vertical total electron content (VTEC) derived from SA data of the Jason and Sentinel missions, the integrated VTEC from the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) and global ionospheric maps (GIMs). To mitigate the systematic bias resulting from differences in satellite altitudes, the vertical ranges of various VTECs are mapped to a standardized height. The results indicate that the intermission bias of SA-derived VTEC remains relatively stable, with Jason-1 serving as a benchmark for mapping other datasets. The mean bias between COSMIC and SA-derived VTEC is minimal, suggesting good agreement between these two space-based techniques. However, COSMIC and GIM VTEC exhibit remarkable seasonal discrepancies, influenced by the solar activity variations. Moreover, GIMs demonstrate noticeable hemispheric asymmetry and a degradation in accuracy ranging from 0.7 to 1.7 TECU in the ocean-dominant Southern Hemisphere. While space-based observations effectively illustrate phenomena such as the Weddell Sea anomaly and longitudinal ionospheric characteristics, GIMs tend to exhibit a more pronounced mid-latitude electron density enhancement structure.

Abstract—There was significant recent progress in the field of deep learning, which has led to compelling advances in most tasks of semantic computer vision (e.g., classification, detection, and segmentation). Point cloud registration is a problem in which two or more different point clouds are aligned by estimation of the relative geometric transformation between them. This well-known problem plays an important role in many applications such as SLAM, 3D reconstruction, mapping, positioning, and localization. The complexity of the point cloud registration increases due to the difficulty of feature extraction related to a large difference in the appearances of a single object obtained by a laser scanner from different points of view. Millions of points created every second require high-efficiency algorithms and powerful computing devices. The well-known ICP algorithm for point cloud registration and its variants have relatively high computational efficiency, but are known to be immune to local minima and, therefore, rely on the quality of the initial rough alignment. Algorithm operation with the interference caused by noisy points on dynamic objects is usually critical for obtaining a satisfactory estimate, especially when using real LiDAR data. In this study, we propose a neural network algorithm to solve the problem of point cloud registration by estimating the soft alignment of the points of the source and target point clouds. The proposed algorithm efficiently works with incongruent noisy point clouds generated by LiDAR. Results of computer simulation are presented to illustrate the efficiency of the proposed algorithm.

Abstract—

The problem of depth estimation of a scene from a given light field can be reduced to the problem of classical stereo matching with the statement that matching pixels in stereo images have the same brightness values. However, this assumption is generally incorrect, given the presence of noise in the images and the different illumination of the left and right images in the stereo pair, as well as the existence of occlusions. In this regard, the luminous flux, representing 80 images pairwise epipolar to the central one, offers a number of advantages, especially in terms of occlusion handling. In this paper, we propose the principle of viewing symmetry: if a pixel of the central image lies in the occlusion zone relative to one of the peripheral images of the light field, then this pixel does not belong to the occlusion zone for an axisymmetric image of the peripheral field. Thus, it is possible to form a robust volume of discrepancy weights relative to the occlusion. As a result, the algorithm proposed in the article significantly improves the result of the scene depth reconstruction. The effectiveness of our approach is demonstrated using the main test database of the light field and comparing it with the best reconstruction algorithms in the efficiency of border recognition and in the speed of calculation.

Abstract—The energy minimization or maximum a posteriori probability (MAP) method is the basis for solving many computer vision problems, including the segmentation problem. However, it is assumed that the number of regions during segmentation is quite small. At the same time, in the problem of superpixel segmentation or otherwise excessive segmentation, the number of such areas exceeds 1000, which makes the computational optimization problem by the MAP method practically impossible. In this paper, we propose a solution that reduces segmentation with any number of areas to the problem of marking only nine labels. In addition, convolution with the geodesic distance kernel is used to enhance the robustness of optimization. This makes it possible to obtain single-linked superpixels at the output of the algorithm, unlike many other methods that require additional adjustments. The effectiveness of the proposed method is compared and measured by the precision-recall criteria, as well as by visual illustration.

Abstract

Mathematical and simulation models of network nodes and the simplest topologies of modern data transmission networks are developed. The functional model of a modern network node is described, and its mathematical model is developed. A simulation model of a topology consisting of network nodes is developed to evaluate probabilistic and temporal indicators of the service quality of a communication network. Based on the simulation model, the probability of packet loss is plotted versus the packet arrival rate for the topology under study.

Abstract—Construction of a combined model of time series (for two models of the same type that exhibit additivity, for example, ARIMA) or a combined forecast of models (in the absence of additivity, for example, for ETS models) providing minimization of the estimated forecast variance is considered. As distinct from alternative models of time series in which the forecast variance is estimated using the Student test, the ARIMA and ETS models allow construction of a function that is related to the parameters of model. Thus, it is possible to estimate the value of the confidence interval for the forecast and construct combinations of models with a minimum estimate of the width of the interval depending on the parameters of the combination. The theoretical part of the work studies linear combinations of forecasts of two models, in which the estimate of forecast variance is minimized (regardless of the type of model). The Hessian of the function for estimating the forecast variance is obtained for construction of a linear combination of forecasts. It is analyzed under the conditions for extremum (zero first derivatives of the function for estimating the variance of the forecast for the combined models). Then, the Hessian is estimated for several groups of ETS models, and the conditions for the presence of a minimum of the estimated forecast variance at a stationary point are considered versus parameters of models.