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Structural shadowing effects of satellite components on ionospheric electric field measurements

Publication date: 15 January 2026

Source: Advances in Space Research, Volume 77, Issue 2

Author(s): Soraya Makhlouf, Mourad Djebli

Spacecraft anti-unwinding attitude tracking with guaranteed performance: A DREM-based adaptive control approach

Publication date: 15 January 2026

Source: Advances in Space Research, Volume 77, Issue 2

Author(s): Bin Chen, Xiaodong Shao, Haoyang Yang, Dongyu Li, Qinglei Hu

An eight-year global look at correlations between total electron content, earthquakes and solar wind

Publication date: 15 January 2026

Source: Advances in Space Research, Volume 77, Issue 2

Author(s): Wojciech Jarmołowski, Paweł Wielgosz, Anna Krypiak-Gregorczyk, Beata Milanowska

Robust trajectory optimization for pursuit-evasion game with navigation and control errors

Publication date: 15 January 2026

Source: Advances in Space Research, Volume 77, Issue 2

Author(s): Yiding Li, Gang Zhang

Morphological characterization of impact craters in the south polar region of moon using Chandrayaan-2 Dual Frequency Synthetic Aperture Radar data

Publication date: 15 January 2026

Source: Advances in Space Research, Volume 77, Issue 2

Author(s): Nitish Kumar, V.S. Rathore, Ajeet Kumar, Akhouri Pramod Krishna, Raja Biswas, Anup Kumar Das

Satellite ephemeris autonomous monitoring using BDS-3 inter-satellite link measurements

Publication date: 15 January 2026

Source: Advances in Space Research, Volume 77, Issue 2

Author(s): Lihao Yin, Mingyuan Zhang, Qianyi Ren, Wenbin Gong, Richang Dong

Gully erosion prediction using weight of evidence and advanced machine learning models

Publication date: 15 January 2026

Source: Advances in Space Research, Volume 77, Issue 2

Author(s): Xiaohui Chen, Alireza Arabameri, M. Santosh, Hasan Raja Naqvi, Mohd Ramiz

CAU-Net: An attention-based feature enhancement model for ground-based cloud image segmentation applicable to <em>peri</em>-solar regions

Publication date: 15 January 2026

Source: Advances in Space Research, Volume 77, Issue 2

Author(s): Junye Zhu, Yutong Liu, Kefan Xu, Yangshu Lin, Keqi Wang, Zhiming Lin, Qiwen Jin, Chao Yang, Lijie Wang, Chenghang Zheng, Yongxin Zhang, Xuecheng Wu

A sensitivity enhancement method for time–frequency system integrity monitoring based on incremental update factor graph

Publication date: 15 January 2026

Source: Advances in Space Research, Volume 77, Issue 2

Author(s): Yu Guo, Ming Ma, Jing Peng, Hang Gong, Xin Yang, Gang Ou

Monitoring of electron temperature and trace erosion product concentration in Hall thruster plume using optical emission spectroscopy method combined with electrostatic probe

Publication date: 15 January 2026

Source: Advances in Space Research, Volume 77, Issue 2

Author(s): Wei Xi, Xi-Ming Zhu, Lu Wang

Geographic-based failure-tolerant routing for LEO mega-constellations

Publication date: 15 January 2026

Source: Advances in Space Research, Volume 77, Issue 2

Author(s): Jiaqi Li, Lei Yang, Lizeng Gong, Zhenglong Yin, Quan Chen

Improved EKF for BeiDou-3 autonomous orbit determination with constellation rotation error elimination method

Publication date: 15 January 2026

Source: Advances in Space Research, Volume 77, Issue 2

Author(s): Songhua Hu, Jingshi Tang

A space target positioning method under distributed video satellite with LOS angle and orbit position error

Publication date: 15 January 2026

Source: Advances in Space Research, Volume 77, Issue 2

Author(s): Xiangru Bai, Haibo Song, Caizhi Fan

Low-thrust transfers to halo orbits in different systems: Hybrid optimization and free-coast design

Publication date: 15 January 2026

Source: Advances in Space Research, Volume 77, Issue 2

Author(s): Zhaoyu Li, Tianyou Li, Hao Zeng, Rui Xu

The characteristics of the distribution of meteor beginning heights in Quadrantids, Perseids and Geminids

Publication date: 1 January 2026

Source: Advances in Space Research, Volume 77, Issue 1

Author(s): Chih-Ming Lin, I-Ching Yang

Global accuracy assessment of ionospheric F2 peak characteristics based on coincident-colocated COSMIC-2 RO and Digisonde measurements: a three-year period analysis (2020–2022)

Publication date: 1 January 2026

Source: Advances in Space Research, Volume 77, Issue 1

Author(s): K.S. Paul, H. Haralambous, M. Moses, S.K. Panda

A landslide inventory that extends over a century in Alaska demonstrates that climate change is having a major impact

EOS - Fri, 01/02/2026 - 16:28

The Landslide Blog is written by Dave Petley, who is widely recognized as a world leader in the study and management of landslides.

Of course, allow me to start by wishing all my readers a Happy 2026. I suspect that we are in for quite a landslide journey again this year.

In late November, a very interesting open access paper (Darrow and Jacobs 2025) was published on the journal Landslides. This piece of work sought to understand the patterns of landslides in Alaska over a century through the creation of a database compiled from “a combination of 24 digital newspapers and online media sources, including historic digitised Alaskan newspapers”. Such a study is an epic amount of work, but yields fantastic data. This study is no exception.

What is of particular interest here is that Alaska suffers from a range of landslide hazards, and suffers significant losses from them, and it is an environment in which climate change is clearly occurring, with warming at a rate that is higher than the global average. Previous studies have shown that this is having a measurable impact on landslides in the mountains of Alaska.

In total, Darrow and Jacobs (2025) have identified 281 landslides since 1883 in Alaska, with the occurrence showing a strong seasonal pattern associated primarily with seasonal patterns of rainfall. The headline from the paper is summarised in this graphic from the paper:-

The recorded incidence of landslides in Alaska by decade, from Darrow and Jacobs (2025).

The data shows a dramatic increase in landslides in recent decades, and in particular in the last two decades or so. Of course, care is needed to ensure that this is not an artefact of the reporting of landslides, but Darrow and Jacobs (2025) explored this issue in detail, concluding that the signal is real. Fortunately, the number of fatalities caused by landslides in Alaska is small, and there is no significant trend in terms of fatal landslides.

So what lies behind this change? Darrow and Jacobs (2025) show that the increase in occurrence of landslides in Alaska is associated with a marked increase in in average annual air temperature that ranges between 1.2 C and 3.4 C, and an associated increase in precipitation that ranges from 3% to 27%, over the 50 years.

Of course, warming is not going to stop in Alaska in the next few decades, so the likely direction of travel in terms of landslides there is clear. There is recognition in Alaska that greater attention will be needed on landslides.

But more widely, this is further quantitative evidence that the climate is having a big impact on landslide hazard. It is remarkable how the evidence just keeps accumulating.

Reference

Darrow, M.M. and Jacobs, A. 2025. Read all about it! A review of more than a century of Alaskan landslides as recorded in periodicalsLandslides. https://doi.org/10.1007/s10346-025-02663-z.

Return to The Landslide Blog homepage Text © 2026. The authors. CC BY-NC-ND 3.0
Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.

Marine Heat Waves Can Exacerbate Heat and Humidity over Land

EOS - Fri, 01/02/2026 - 14:52
Source: AGU Advances

In 2023, Earth experienced its warmest year since 1850, with heat waves stretching across oceans and land alike. East Asia, for example, experienced scorching temperatures and high humidity throughout the summer months. Humid-heat extremes like those seen that year can trigger heat-related illnesses and mortality at higher-than-average rates.

As on land, the ocean around East Asia also experienced unprecedented warming in 2023. Sea surface temperatures (SST) in the Kuroshio-Oyashio Extension region reached record highs, persisting through much of the year. Researchers know that marine heat waves can influence land heat waves, but the details of these connections remain unclear.

Okajima et al. modeled regional land-sea interactions to better understand the effects of the unprecedented 2023 marine heat wave on conditions on land in East Asia. The team focused on the peak hot and humid months of July, August, and September, using hourly data on atmospheric conditions, including temperature, humidity, wind velocity, and atmospheric pressure, as well as SST data from satellites and in situ sensors.

The modeling suggested that the 2023 marine heat wave greatly exacerbated the East Asian heat wave, particularly in Japan, by affecting atmospheric circulation and altering the usual radiative effects of clouds and water vapor. The team said the influence of the marine heat wave explains roughly 20% to 50% of the increase in the intensity and duration of hot and humid conditions observed on land in East Asia in summer 2023.

The scientists note that this research provides valuable insights that could help improve long-range weather predictions. Such predictions may help communities prepare for health risks, particularly in Asia, which the World Meteorological Organization reported earlier this year is warming twice as fast as the global average. (AGU Advances, https://doi.org/10.1029/2025AV001673, 2025)

—Sarah Derouin (@sarahderouin.com), Science Writer

Citation: Derouin, S. (2025), Marine heat waves can exacerbate heat and humidity over land, Eos, 107, https://doi.org/10.1029/2026EO260009. Published on 2 January 2026. Text © 2026. AGU. CC BY-NC-ND 3.0
Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.

Subsurface structure across the Tacoma Basin, Washington State, using trans-dimensional Bayesian inversion of fundamental mode spatial autocorrelation data

Geophysical Journal International - Fri, 01/02/2026 - 00:00
SummarySpatial autocorrelation (SPAC), the azimuthal average of the normalized cross-correlation between equidistant station pairs deployed in a 2-D array, is widely used to image the subsurface structure. However, the rigorous estimate of subsurface structure and its uncertainties as a function of depth using SPAC data is challenging due to the nonlinear relation between the SPAC data and Earth structure as well as the trade-off between depth and velocity. Additionally, data noise is strongly correlated due to data processing (e.g., filtering, stacking from multiple time segments and azimuthal averaging). Most studies do not account for the correlated noise and fix the ratio of compressional-wave velocity (${V}_P$) to shear-wave velocity (${V}_s$) (i.e., ${V}_P$/${V}_s$ ratio) and the number of layers, both of which are typically unknown. To address these challenges, we develop a hierarchical trans-dimensional Bayesian inversion of fundamental mode of SPAC data that properly accounts for the correlated data noise, samples the ${V}_P$/${V}_s$ ratio, and relaxes the number of layers (i.e., model parameterization) to be unknown in the inversion. We further examine the limitation of using only fundamental modes in the inversion. Our synthetic experiments show that the inversion recovers an incorrect model unless we sample the correlated noise and ${V}_P$/${V}_s$ ratio in the inversion. The inversion is then applied to SPAC data acquired at 19 sites across the Tacoma basin in Washington State to characterize the ${V}_s$ and the time-averaged ${V}_s$ over 30-m depth ($V{s}_{30}$). Our results show that the $V{s}_{30}\ $varies from ∼200 m/s to 800 m/s. The $V{s}_{30}$ within the basin is higher in the middle and lower on the east and west sides. We find that these $V{s}_{30}$ values vary with geologic unit. The uncertainties for $V{s}_{30}$ are within 20 m/s in average except for the most eastern site TB28. Additionally, the uncertainties are greater for deeper depths beneath most of the sites as the sensitivity decreases as a function of depth. The ${V}_s$ structure as a function of depth is also complex beneath some sites, possibly because the SPAC curves are affected by higher order Rayleigh modes that are not considered in the inversion. To better constrain the deeper ${V}_s\ $structure, $V{s}_{30}$, and/or other average measures of ${V}_s$ over depth, additional constraints from complementary data, such as ellipticity or geologic data are needed. Moreover, our synthetic experiments show that higher order modes can have significant effect in the inversion results, particularly when there is a low-velocity layer.

Regional Geomagnetic Field Modeling Method Based on a Two-Stage Adaptive Weight Physics-Informed Neural Network

Geophysical Journal International - Fri, 01/02/2026 - 00:00
SummaryRegional geomagnetic field models are used to delineate intricate details of the Earth’s magnetic field and have significant application value in precision navigation and geomagnetic exploration. However, traditional modeling methods often encounter challenges when applied to sparse data, leading to issues like low model resolution and accuracy, as well as limited generalizability. The recently developed physics-informed neural networks (PINNs), a powerful modeling tool, presents a viable alternative for regional geomagnetic field modeling. This study employed the PINNs method to construct a geomagnetic field model for satellite altitudes over the Chinese region, based on the Swarm satellite dataset provided by the European Space Agency. An adaptive weight training method was used for the two-stage training process, involving an initial pre-training and subsequent fine-tuning of the model. Experimental verification shows that the proposed algorithm enhanced the model’s fidelity to physical laws, improved its resolution and prediction accuracy (reducing the root mean square errors for geomagnetic components to as low as 4 nT), and enhanced its generalizability, with the total field intensity F and the prediction accuracy of both the X- and Y-components demonstrating superiority over that of other traditional methods. Collectively, these advancements enable efficient regional geomagnetic field modeling while providing a foundation for more reliable and precise predictions.

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