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Author(s): M. Galletti, L. Crincoli, R. Pompili, L. Verra, F. Villa, R. Demitra, A. Biagioni, A. Zigler, and M. Ferrario

We describe the generation of plasma filaments for application in plasma-based particle accelerators. The complete characterization of a plasma filament generated by a low-energy self-guided femtosecond laser pulse is studied experimentally and theoretically in a low-pressure nitrogen gas environmen…

[Phys. Rev. E 111, 025202] Published Tue Feb 18, 2025

Author(s): Jihoon Kim, Roopendra Rajawat, Tianhong Wang, and Gennady Shvets

We demonstrate an ion acceleration scheme capable of simultaneously focusing and accelerating a multispecies ion beam with monoenergetic spectra to a few micron radius. The focal length and ion mean energy can be independently controlled: the former by using a different front-surface shape and the l…

[Phys. Rev. E 111, 025203] Published Tue Feb 18, 2025

Abstract

The gravimetry measurements from the Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) mission provide an essential way to monitor changes in ocean bottom pressure ( \(p_b\) ), which is a critical variable in understanding ocean circulation. However, the coarse spatial resolution of the GRACE(-FO) fields blurs important spatial details, such as \(p_b\) gradients. In this study, we employ a self-supervised deep learning algorithm to downscale global monthly \(p_b\) anomalies derived from GRACE(-FO) observations to an equal-angle 0.25  \( ^{\circ }\) grid in the absence of high-resolution ground truth. The optimization process is realized by constraining the outputs to follow the large-scale mass conservation contained in the gravity field estimates while learning the spatial details from two ocean reanalysis products. The downscaled product agrees with GRACE(-FO) solutions over large ocean basins at the millimeter level in terms of equivalent water height and shows signs of outperforming them when evaluating short spatial scale variability. In particular, the downscaled \(p_b\) product has more realistic signal content near the coast and exhibits better agreement with tide gauge measurements at around 80% of 465 globally distributed stations. Our method presents a novel way of combining the advantages of satellite measurements and ocean models at the product level, with potential downstream applications for studies of the large-scale ocean circulation, coastal sea level variability, and changes in global geodetic parameters.

Abstract

Data-driven technologies have shown promising potential for improving GNSS positioning, which can analyze observation data to learn the complex hidden characteristics of system models, without rigorous prior assumptions. However, in complex urban areas, the input observation data contain task-irrelevant noisy GNSS measurements arising from stochastic noise, such as signal reflections from tall buildings. Moreover, the problem of data distribution shift between the training and testing phases exists for dynamically changing environments. These problems limit the robustness and generalizability of the data-driven GNSS positioning methods in urban areas. In this paper, a novel deep reinforcement learning (DRL) method is proposed to improve the robustness and generalizability of the data-driven GNSS positioning. Specifically, to address the data distribution shift in dynamically changing environments, the robust Bellman operator (RBO) is employed into the DRL optimization to model the deviations in the data distribution and to enhance generalizability. To improve robustness against task-irrelevant noisy GNSS measurements, the long-term reward sequence prediction (LRSP) is adopted to learn robust representations by extracting task-relevant information from GNSS observations. Therefore, we develop a DRL method with robust augmented reward sequence prediction to correct the rough position solved by model-based methods. Moreover, a novel real-world GNSS positioning dataset is built, containing different scenes in urban areas. Our experiments were conducted on the public dataset Google smartphone decimeter challenge 2022 (GSDC2022) and the built dataset Guangzhou GNSS version 2 (GZGNSS-V2), which demonstrated that the proposed method can outperform model-based and state-of-the-art data-driven methods in terms of generalizability across different environments.

SummaryIn the past decade, six Mw ≥5.5 earthquakes struck the mountainous Golden Triangle region (Laos, Thailand, Myanmar) of the southeast India-Eurasia collision zone. The largest of them, the 2019 Mw 6.2 Sainyabuli earthquake in western Laos, shook river communities, dams, and a UNESCO World Heritage Site, prompting a need to understand regional earthquake potential. We used Interferometric Synthetic Aperture Radar (InSAR) data and modelling to solve for the 2019 mainshock source parameters, revealing right-lateral strike-slip along a 24 km-long NNW-trending fault which has limited topographic expression and was previously unmapped. InSAR modelling of its largest (Mw 5.5) aftershock in 2021 revealed a 7 km-long splay fault, also previously unrecognized. The 2022 Mw 5.9 Keng Tung earthquake in the northern Golden Triangle also ruptured an unknown, NW-trending right-lateral fault conjugate to longer, NE-trending faults nearby. Collectively, this shows that the region contains faults which are little evident in global digital topography and/or obscured by vegetation but long enough to generate sizeable earthquakes that should be accounted for in seismic hazard assessments. We relocated well-recorded aftershocks and other background seismicity (1978–2023) from across the Golden Triangle using the mloc software. Calibrated hypocenters span focal depths of 5–24 km and are distributed away from the main InSAR-modelled fault traces, another indication of fault structural immaturity. For the three 2019–2022 InSAR-constrained events, we also obtained moment tensor solutions from regional seismic waveform inversion. InSAR-derived peak slip depths and seismological centroid depths are mostly shallow (3–5 km), while focal depths are generally located in areas of low coseismic slip near the bottom of InSAR model faults. More broadly, we estimate a regional seismogenic thickness of ∼17 km (the 90% seismicity cut-off depth), a crucial parameter for seismic hazard calculations and building codes. Our integration of remote-sensing and seismologic analyses could be a blueprint for assessing earthquake potential of other regions with sparse instrumentation and limited topographic fault expression.

Nature Geoscience, Published online: 18 February 2025; doi:10.1038/s41561-025-01648-w

Global sea-level rise during Meltwater Pulse 1A followed sequential ice loss from the Laurentide, Eurasian and then West Antarctic ice sheets, according to a fingerprinting approach.

Abstract

Many components of the Earth system feature self-reinforcing feedback processes that can potentially scale up a small initial change to a fundamental state change of the underlying system in a sometimes abrupt or irreversible manner beyond a critical threshold. Such tipping points can be found across a wide range of spatial and temporal scales and are expressed in very different observable variables. For example, early-warning signals of approaching critical transitions may manifest in localised spatial pattern formation of vegetation within years as observed for the Amazon rainforest. In contrast, the susceptibility of ice sheets to tipping dynamics can unfold at basin to sub-continental scales, over centuries to even millennia. Accordingly, to improve the understanding of the underlying processes, to capture present-day system states and to monitor early-warning signals, tipping point science relies on diverse data products. To that end, Earth observation has proven indispensable as it provides a broad range of data products with varying spatio-temporal scales and resolutions. Here we review the observable characteristics of selected potential climate tipping systems associated with the multiple stages of a tipping process: This includes i) gaining system and process understanding, ii) detecting early-warning signals for resilience loss when approaching potential tipping points and iii) monitoring progressing tipping dynamics across scales in space and time. By assessing how well the observational requirements are met by the Essential Climate Variables (ECVs) defined by the Global Climate Observing System (GCOS), we identify gaps in the portfolio and what is needed to better characterise potential candidate tipping elements. Gaps have been identified for the Amazon forest system (vegetation water content), permafrost (ground subsidence), Atlantic Meridional Overturning Circulation, AMOC (section mass, heat and fresh water transports and freshwater input from ice sheet edges) and ice sheets (e.g. surface melt). For many of the ECVs, issues in specifications have been identified. Of main concern are spatial resolution and missing variables, calling for an update of the ECVS or a separate, dedicated catalogue of tipping variables.

Militaries that have taken power in Africa's Sahel region—notably Mali, Burkina Faso and Niger—have put pressure on western mining firms for a fairer distribution of revenue from the lucrative mining sector.

Because trees can cool cities by providing shade and evaporating water into the atmosphere, greening city streets is an often-touted strategy for climate change adaptation. But trees provide benefits only if they're healthy, and physical variations in urban environments mean that not all trees have the same chance to thrive.

Abstract

The Gravity field and steady-state Ocean Circulation Explorer (GOCE) mission, launched by the European Space Agency, provided high-quality gravitational gradient data with near-global coverage, excluding polar regions. These data have been instrumental in regional gravity field modelling through various methods. One approach involves a mathematical model based on Fredholm’s integral equation of the first kind, which relates surface gravity anomalies to satellite gradient data. Solving this equation requires discretising a surface integral and applying further regularisation techniques to stabilise the numerical solution of a resulting system of linear equations. This study examines four methods for modifying the system of linear equations derived by discretising the Fredholm integral equation. The methods include direct inversion, remove-compute-restore, truncation reduction of the integral formula, and inversion of a modified integral for estimating surface gravity anomalies from satellite gradient data over a test area in Central Europe. Since the system of linear equations is ill-conditioned, the Tikhonov regularisation is applied to stabilise its numerical solution. To assess the precision and reliability of the estimated gravity anomalies, the study introduces mathematical models for estimation of biased and de-biased noise variance–covariance matrices of estimated surface gravity anomalies. The results indicate that the signal-to-noise ratio of reduced satellite gradient data in the remove-compute-restore method is smaller compared to other methods in the study, necessitating stronger stabilisation of the model to recover surface gravity anomalies. This, in turn, leads to a more optimistic uncertainty propagation than the other considered methods.

SummaryWhile the Earth’s magnetic field has existed for 4Gyr or more, most recent estimates for the age of the inner core nucleation date no further back than 1.5Gyr. As a consequence, the relevant geometry for the Earth’s dynamo has been a full sphere for much of its life, fundamentally different from the present day dynamo operating in a spherical shell. We therefore systematically study magnetic field generation in a rapidly-rotating full sphere where convection is driven by heat sources uniformly-distributed throughout the fluid. We observe a rich diversity of behaviour in our solutions, including dipolar and multipolar dominated fields, together with vacillating and chaotically-reversing magnetic fields. At Prandtl number of unity, we construct regime diagrams as a function of three control parameters, namely the Rayleigh, Ekman and magnetic Prandtl number, which show some similarities with the corresponding diagrams for spherical shell dynamos. This study comprehensively demonstrates the feasibility of early-Earth dynamos that operate based on secular cooling of the core.

SummaryThe rapid and accurate prediction of peak ground acceleration (PGA) few seconds after earthquake start is crucial for assessing the potential damage in target areas in impact-based earthquake early warning systems. However, it is difficult to substantially improve the performance of PGA prediction methods based on empirically defined ground motion prediction equations. In this study, we proposed a hybrid deep learning network (HDL-Net) model for PGA prediction based on Japanese and Chinese datasets. The HDL-Net model is capable of extracting useful spatial and temporal features from the input physical feature parameters and three-component waveforms. The test results showed that HDL-Net outperformed the traditional empirical approaches in terms of timeliness and accuracy. To further validate the robustness of the HDL-Net model for PGA prediction, we conducted a potential damage analysis for five earthquakes in Japan. The results showed that the successful alarm (SA) rate reached 95.22%, the successful no alarm (SNA) rate was 100%, and there was no false alarm (FA). The HDL-Net model provides a potential method for earthquake early warning (EEW) and seismological PGA prediction.

Nature Geoscience, Published online: 17 February 2025; doi:10.1038/s41561-025-01650-2

Rapid deposition of early-generation oxidation products substantially reduces the formation of late-generation atmospheric compounds, according to a deposition framework based on physicochemical properties and chemical modelling.

Publication date: Available online 3 February 2025

Source: Advances in Space Research

Author(s): Lin M.M. Myint, Septi Perwitasari, Michi Nishioka, Susumu Saito, Rungnapa Kaewthongrach, Pornchai Supnithi

Publication date: 1 February 2025

Source: Advances in Space Research, Volume 75, Issue 3

Author(s): Yunong Shang, Changxuan Wen, Yang Sun, Hao Zhang, Yang Gao

Publication date: 1 February 2025

Source: Advances in Space Research, Volume 75, Issue 3

Author(s): Xun Liu, Hashem Ashrafiuon, Sergey G. Nersesov

Publication date: 1 February 2025

Source: Advances in Space Research, Volume 75, Issue 3

Author(s): Mohamed Elamine Galloua, Shuai Li, Jiahao Cui

Publication date: 1 February 2025

Source: Advances in Space Research, Volume 75, Issue 3

Author(s): Zilong Chen, Haichao Gui, Rui Zhong