Feed aggregator

Direct pathway of incorporating dietary nitrogen in shell-bound matrix of the planktic foraminifera <em>Trilobatus sacculifer</em>

Earth and Planetary Science Letters - Thu, 02/20/2025 - 19:10

Publication date: 15 March 2025

Source: Earth and Planetary Science Letters, Volume 654

Author(s): Wei-Ning Fang, Oscar Branson, Er-Wen Yang, Wen-Hui Chen, Ren-Yi Cai-Li, Howard J. Spero, Jennifer Fehrenbacher, Lael Vetter, Charlotte LeKieffre, Haojia Ren

Coeval formation of continental crust and cratonic mantle facilitated by surface material recycling in the Paleoarchean: Constraints from molybdenum isotopes

Earth and Planetary Science Letters - Thu, 02/20/2025 - 19:10

Publication date: 15 March 2025

Source: Earth and Planetary Science Letters, Volume 654

Author(s): Sukalpa Chatterjee, Arathy Ravindran, Qasid Ahmad, Om Prakash Pandey, Martin Wille, Klaus Mezger

Phosphorus-to-calcium ratios in benthic foraminiferal shells as a proxy for coastal seawater phosphate concentrations

Earth and Planetary Science Letters - Thu, 02/20/2025 - 19:10

Publication date: 15 March 2025

Source: Earth and Planetary Science Letters, Volume 654

Author(s): Han Zhang, Bochao Xu, Zhiqing Lai, Adina Paytan, William C. Burnett, Xiaoyi Guo, Lihui Ren, Yuan Lu, Jianing Zhang, Huamao Yuan, Qingzhen Yao, Zhigang Yu

Equatorial Ionization anomaly disturbances (EIA) triggered by the May 2024 solar Coronal Mass Ejection (CME): The strongest geomagnetic superstorm in the last two decades

Publication date: Available online 5 February 2025

Source: Advances in Space Research

Author(s): P.R. Fagundes, V.G. Pillat, J.B. Habarulema, M.T.A.H. Muella, K. Venkatesh, A.J. de Abreu, C.M. Anoruo, F. Vieira, K.H. Welyargis, E. Agyei-Yeboah, A. Tardelli, G.S. Felix, G.A.S. Picanço

Multichannel PredRNN: a storm-time TEC map forecasting model using both temporal and spatial memories

GPS Solutions - Thu, 02/20/2025 - 00:00
Abstract

The predictive learning of total electron content (TEC) spatiotemporal sequences aims to generate future TEC maps by learning from historical data, where both the spatial appearances and temporal variations are crucial for accurate predictions. However, the state-of-the-art TEC map prediction models typically employ sequential stacking of ConvLSTM, ConvGRU, and their variants. These models focus more on modeling temporal variations, and the spatial features extracted from the historical sequence are highly abstracted, resulting in the fine-grained spatial appearances not being adequately memorized or transmitted, leading to fuzzy prediction results during storm time. In this paper, we used PredRNN to propose a storm-time ionospheric TEC spatiotemporal prediction model with multichannel features, named Multichannel PredRNN, which can simultaneously remember the temporal patterns and spatial appearances in input sequence. The temporal memory as well as the spatial memory are updated repeatedly over time, ensuring that both temporal memory and spatiotemporal memory are fully utilized in prediction. According to Dst index, 60 magnetic storm events from 2011 to 2019 were selected as the dataset. We first discussed the impact of feature combinations on predictive performance. The results show that using multichannel feature (TEC + Dst&F10.7), the Multichannel PredRNN and the comparison models ConvGRU and ConvLSTM have the best prediction performance. Then we used the optimal feature combination for prediction. We compared Multichannel PredRNN with IRI-2016, COPG, ConvLSTM and ConvGRU under various conditions, including the entire test magnetic events, periods of quiet and storm, different phases of geomagnetic storms, and the most severe geomagnetic storms. Finally, we compared the performance of different output steps. The experimental results indicate that in all cases, Multichannel PredRNN with dual memory state and zigzag flow is superior to four compared models.

Downscaling GRACE-derived ocean bottom pressure anomalies using self-supervised data fusion

Journal of Geodesy - Tue, 02/18/2025 - 00:00
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.

Deep reinforcement learning with robust augmented reward sequence prediction for improving GNSS positioning

GPS Solutions - Tue, 02/18/2025 - 00:00
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.

Meltwater Pulse 1A sea-level-rise patterns explained by global cascade of ice loss

Nature Geoscience - Tue, 02/18/2025 - 00:00

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.

Monitoring the Multiple Stages of Climate Tipping Systems from Space: Do the GCOS Essential Climate Variables Meet the Needs?

Surveys in Geophysics - Tue, 02/18/2025 - 00:00
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.

Categories:

Uncertainty propagation through integral inversion of satellite gradient data in regional gravity field recovery

Journal of Geodesy - Mon, 02/17/2025 - 00:00
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.

Change of Editor-in-Chief

Surveys in Geophysics - Mon, 02/17/2025 - 00:00
Categories:

Retirement of Editor-in-Chief

Surveys in Geophysics - Mon, 02/17/2025 - 00:00
Categories:

Formation of late-generation atmospheric compounds inhibited by rapid deposition

Nature Geoscience - Mon, 02/17/2025 - 00:00

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.

Analysis of Ionospheric and Geomagnetic Fields Changes in Thailand During the May 2024 Geomagnetic Storm

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

Active solar eclipse avoidance on the distant retrograde orbit of the Earth-Moon system

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

A non-Lyapunov approach to control design with application to spacecraft docking

Publication date: 1 February 2025

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

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

Earth observation satellite imaging task scheduling with metaheuristics: Multi-level clustering and priority-driven pre-scheduling

Publication date: 1 February 2025

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

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

Neural network-based navigation filter for monocular pose and motion tracking of noncooperative spacecraft

Publication date: 1 February 2025

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

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

Theme by Danetsoft and Danang Probo Sayekti inspired by Maksimer