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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.

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.

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.

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.

In this study, two Category 3 carbonaceous particles (RB-CV-0008 and RB-CV-0031) among more than 700 Hayabusa-returned particles were analyzed by molecular imaging using desorption electrospray ionization–high...

Vulcanian eruptions, characterized by intermediate magma compositions, pose significant hazards due to their potential for both magmatic and phreatomagmatic fragmentation. The Young Ciremai volcano located in ...

Abstract

While integer ambiguity resolution (IAR) enables GNSS to achieve real-time sub-centimeter-level positioning in open-sky environments, it can be easily hindered if the involved receivers are situated in areas with limited satellite visibility, such as in dense city environments. In such GNSS-challenged cases, commercial Low Earth Orbit (LEO) communication satellites can potentially augment GNSS by providing additional measurements. However, LEO satellites often lack code measurements, mainly transmitting satellite-specific frequency-varying carrier phase signals. This contribution aims to study the ambiguity-resolved baseline positioning performance of such phase-only signals, addressing the extent to which LEO constellations can realize near real-time positioning in standalone and GNSS-combined modes. Through a simulation platform, we analyze the distinct response of each LEO constellation (Iridium, Globalstar, Starlink, OneWeb, and Orbcomm) to IAR under various circumstances. Although achieving single-receiver high-precision positioning can be challenged by inaccuracies in the LEO satellite orbit products, the relative distance between two receivers can help overcome this limitation. As a result, centimeter-level relative positioning over short baselines can be made possible, even with a satellite elevation cut-off angle of 50 degrees, making it suitable for GNSS-challenged environments. This can be achieved with high-grade receiver clocks over very short baselines ( \(\sim \) 5 km) and access to decimeter-level orbit products.

Abstract

The prerequisite for achieving highly reliable Wide-Lane (WL) ambiguity resolution (AR) is the accurate determination of phase fractional cycle biases (FCB) on both the receiver and satellite side. It is generally assumed that the observation signal biases on the receiver side are stable, and receiver FCBs can be expressed using a single parameter over a period. However, due to the influence of satellite signal distortion and multipath errors, the receiver-dependent pseudorange biases (RDPB) may be inconsistent for different observed satellites at a certain receiver, which is called inconsistent RDPB (IRDPB) in this study. To improve the WL AR performance in GNSS network processing, we propose an optimized FCB estimation method with IRDPB corrected by the receiver individual. Utilizing data from 490 stations with four receiver types, the effect of the proposed WL FCB is verified in terms of ambiguity residual distribution and ambiguity fixed rate by comparing it with the original FCB and FCB with IRDPB corrected by receiver type. The proposed method improves the proportions of WL residuals within ± 0.1 cycles by 13.7%–20.5% for GPS, BDS-2 and BDS-3 compared to the original FCB. Compared to FCB corrected with receiver-type IRDPB, the number of stations with the proportions of residuals within ± 0.1 cycles in the percentage range (80,100] are improved by more than 130 for GPS, BDS-2 and BDS-3. Using the proposed FCB, the GNSS stations can obtain reliable real-time WL fixing solution. The result also shows that the influence of IRDPB varied with receiver types and GNSS systems. Galileo was less affected by IRDPB than GPS and BDS-2/3, and Trimble Alloy receivers suffer more significant IRDPB than the other three types of receivers.

Abstract

Magnetic reconnection converts magnetic field energy into particle energy by breaking and reconnecting magnetic field lines. Magnetic reconnection is a kinetic process that generates a wide variety of kinetic waves via wave-particle interactions. Kinetic waves have been proposed to play an important role in magnetic reconnection in collisionless plasmas by, for example, contributing to anomalous resistivity and diffusion, particle heating, and transfer of energy between different particle populations. These waves range from below the ion cyclotron frequency to above the electron plasma frequency and from ion kinetic scales down to electron Debye length scales. This review aims to describe the progress made in understanding the relationship between magnetic reconnection and kinetic waves. We focus on the waves in different parts of the reconnection region, namely, the diffusion region, separatrices, outflow regions, and jet fronts. Particular emphasis is placed on the recent observations from the Magnetospheric Multiscale (MMS) spacecraft and numerical simulations, which have substantially increased the understanding of the interplay between kinetic waves and reconnection. Some of the ongoing questions related to waves and reconnection are discussed.

Abstract

The Analyzer for Cusp Electrons (ACE) instruments on the Tandem Reconnection and Cusp Electrodynamics Reconnaissance Satellites (TRACERS) mission provide measurements of electron velocity distribution functions from two closely spaced spacecraft in a low Earth orbit that passes through the magnetospheric cusp. The precipitating and upward-going electrons provide a sensitive probe of the magnetic field line topology and electrostatic potential structure, as well as revealing dynamic processes. ACE measurements contribute to the top-level TRACERS goals of characterizing the spatial and temporal variation of magnetic reconnection at the terrestrial magnetopause and its relationship to dynamic structures in the cusp. ACE utilizes a classic hemispheric electrostatic analyzer on a spinning platform to provide full angular coverage with 10 degree by 7 degree resolution. ACE can measure electrons over an energy range of 20-13,500 electron volts, with fractional energy resolution of 19%. ACE provides 50 ms cadence measurements of the electron velocity distribution, enabling sub-kilometer spatial resolution of cusp boundaries and other structures.

Abstract

The Europa Clipper mission will explore Europa and investigate its habitability utilizing a set of five remote-sensing instruments that cover the electromagnetic spectrum from thermal infrared to ultraviolet wavelength, four in-situ fields and particles instruments, a dual-frequency radar, and a gravity and radio science investigation. Key mission objectives include to produce high-resolution images of Europa’s surface, determine its composition, look for signs of recent or ongoing activity, measure the thickness of the icy shell, search for subsurface lakes, and determine the depth and salinity of Europa’s ocean. The Europa Clipper Mission Plan integrates the above investigations in a way that allows for the simultaneous acquisition of complimentary datasets (i.e., datasets at the regional scale, distributed globally across Europa) utilizing a complex network of flybys while in Jupiter orbit. About 50 flybys of Europa—with closest-approach altitudes varying from several thousand kilometers to as low as 25 kilometers—will be executed over an approximately 4.3-year prime mission. We present an overview of the mission design, which is driven by the complex scientific goals of the mission but also influenced by launch vehicle capabilities, the intense Jovian radiation environment, varying thermal environment, and dependency on precise planet and moon flybys to manage the orbit. We describe the interplanetary and Jovian orbit design, Mission Plan, and Navigation Plan, and forecast performance against mission requirements to date.

Abstract

The stochastic representation of an uncertain shape model allows the dynamic evaluation of its induced gravity signal. This can be also applied for representing a time variable gravity field to model mass changes. The algorithm for estimating variations in gravitational potential is extended for the case of second-order derivatives. Two different harmonic synthesis formulas are used to derive the sought variations: one expressed in spherical coordinates using the traditional associated Legendre functions (ALF) and their derivatives up to the second order, while the other expressed in Cartesian coordinates by including the derived Legendre functions (DLF). The obtained variations are compared in terms of convergence with gravity signal differences referring to the specific shape changes using the line integral analytical approach for three asteroid shape models. Both approaches provide results that differ from the analytical method at a 1E−1 level, while the differences between them are at the 1E−15 level. The obtained results are highly influenced by the geometry of the examined shape model, with the ALF approach providing variations with closer agreement with the analytical method only for the almost spherical shape. Both harmonic synthesis expressions can be used to derive accurate results, as they differ at a very low level, and one can choose based on the convenience of their algorithmic characteristics.

Abstract

Effective angular momentum (EAM) forecasts are widely used as an important input for predicting both polar motion and dUT1. So far, model predictions for atmosphere, ocean, and terrestrial hydrosphere utilized in Earth rotation research reach only 6-days into the future. GFZ’s oceanic and land-surface model forecasts are forced with operational 6-day high-resolution deterministic numerical weather predictions provided by the European Centre for Medium-range Weather Forecasts. Those atmospheric forecasts extend also further into the future with a reduced sampling rate of just 6 h but the prediction skill decreases rapidly after roughly one week. To decide about publishing 10-day instead of 6-day model-based EAM forecasts, we generated a test set of 454 individual 10-day forecasts and used it with GFZ’s EAM Predictor method to calculate Earth rotation predictions. Using 10-day instead of 6-day EAM forecasts leads to slight improvements in y-pole and dUT1 predictions for 10 to 30 days ahead. By introducing additional neural network models trained on the errors of the EAM forecasts when compared to their subsequently available analysis runs, Earth rotation prediction can be enhanced even further. A reduction of the mean absolute errors for polar motion and length-of-day prediction at a forecast horizon of 10 days of 26.8% in x-pole, 15.5% in y-pole, 27.6% in dUT1, and 47.1% in \(\Delta \) LOD is achieved. This test application successfully demonstrates the potential of the extended EAM forecasts for Earth rotation prediction although the success rate has to be further improved to arrive at robust routine predictions. GFZ publishes from October 2024 onwards raw uncorrected 10-day instead of 6-day EAM forecasts at www.gfz-potsdam.de/en/esmdata for the individual contributions of atmosphere, ocean, and terrestrial hydrosphere. Users interested in the summarized effect of all subsystems are advised to use the 90-day combined EAM forecast product that also makes use of the presented corrections to the EAM forecasts.

Abstract

GPS flex power can improve anti-jamming capability by enhancing the transmitting power of individual signals. However, during the active periods of GPS flex power in 2020, it was found that the accuracy of kinematic orbit for GRACE-FO satellites is decreased. In this paper, the impact of flex power on kinematic orbit determination of GRACE-FO is investigated. With the analysis of 30-day epoch-differenced geometry-free combinations of phase, i.e., \(\:\varDelta\:{{\Phi\:}}_{\text{G}\text{F}}\) and signal-to-noise ratio (SNR) for GRACE-FO satellites, a new strategy which considers the impact of flex power on the continuity of ambiguity is put forward to improve the kinematic orbit of GRACE-FO. After considering flex power, the 3D root-mean-square (RMS) of GRACE-C and GRACE-D are reduced to 4.10 and 4.42 cm, with improvements of 36% and 21%, respectively. The improvements of SLR validation are 34% and 14% for GRACE-C and GRACE-D. The above results prove the effectiveness of the proposed strategy.

Abstract

Signal anti-jamming has always been a difficult problem in GNSS (global navigation satellite system) signal processing. There are many GNSS anti-jamming techniques in the existing research, which can achieve good results if the interferences are sparsely distinguishable in some signal feature domains. Specifically, the single antenna based anti-jamming techniques cannot deal with wideband Gaussian noise interference because it is not sparse in time or frequency domain, while the only effective method currently is using multiple antennas to apply the space array processing (SAP) technique since the wideband Gaussian noise interference is sparse in the spatial domain. However, when the incoming directions of the different interferences are not less than that of antennas, the interferences are not sparse to the array anymore, and the SAP anti-jamming performance would decrease drastically. In this paper, a LSTM (long short-term memory) deep neural network (DNN) based algorithm is proposed to enhance the array anti-jamming performance in this situation. The proposed network estimates the interferences as an integrity by exploring the non-linear relationship of the array data received by antennas. Especially, a new loss function is designed exclusively for GNSS anti-jamming problem. The proposed DNN method is verified in the simulation that two wideband Gaussian interferences with JSR (jamming to signal ratio) 50 dB can be eliminated by using two antennas’ data, and the interference cancellation ratio improvement is about 24 dB compared to some other widely used classical SAP algorithms.

Nature Geoscience, Published online: 12 February 2025; doi:10.1038/s41561-025-01646-y

Natural gradients across surface ocean regions show that changes in carbonate chemistry projected for ocean alkalinity enhancement could promote the proliferation of calcifying phytoplankton. This shift would increase an alkalinity sink, thus reducing the efficiency of ocean alkalinity enhancement as a CO2 removal method.

Nature Geoscience, Published online: 12 February 2025; doi:10.1038/s41561-025-01644-0

Intensive ocean alkalinity enhancement will cause a proliferation of calcifying organisms, which reduces its effectiveness as a carbon sequestration approach, according to an analysis of coccolithophore sensitivity to natural carbonate chemistry variability.