Earth and Space Science最新文献

Earth's radiation belts are the regions where highly energetic charged particles are trapped by Earth's magnetic field, posing significant risks to the satellites and other space-based technologies. Understanding the dynamics of the radiation belts is critical not only for advancing fundamental plasma physics but also for predicting and mitigating space weather impacts. To assess recent achievements, identify scientific priorities, and foster collaboration, a round-table discussion was hosted by the “System Understanding of Radiation Belt Particle Dynamics through Multi-Spacecraft and Ground-Based Observations and Modeling” focus group at the 2024 Geospace Environmental Modeling Workshop. Bringing together around 60 participants, including many students and early-career scientists (<10 years post Ph.D.), this session explored recent advances, critical open questions, and future directions in radiation belt science and exploration. Participants highlighted significant progress in observations, modeling, and understanding of radiation belt dynamics over the past decade. However, critical challenges remain, including the accurate quantification of source and loss processes, the role of small- and meso-scale processes in radiation belt dynamics, system coupling within the magnetosphere, and the applicability of current knowledge to more active solar and geomagnetic conditions. The critical need for improved observational and modeling capabilities, open science practices, and stronger community collaboration are identified as priorities to drive future advances in radiation belt sciences.

Launched by the European Space Agency in August 2018, the Aeolus satellite utilizes the Atmospheric Laser Doppler Instrument (ALADIN) to achieve the first global direct measurements of wind profiles in the troposphere and lower stratosphere. The observational data provide important support for global weather forecasting, climate monitoring, and atmospheric dynamics research. This study conducts a systematic evaluation of the Aeolus Level-2B (L2B) wind product over Antarctica by comparing it with radiosonde measurements from polar stations and the fifth-generation ECMWF reanalysis data set (ERA5) during the period from 2019 to 2022. Results show that under Rayleigh-clear conditions, Aeolus winds exhibit a correlation of 0.95 with radiosondes, with a bias of −0.03 m/s, a standard deviation (STD) of 4.29 m/s, and a scaled median absolute deviation (SMAD) of 4.78 m/s. Under Mie-cloudy conditions, the correlation is also 0.95, with a bias of +0.45 m/s, STD of 3.70 m/s, and SMAD of 3.98 m/s. Seasonal analysis indicates larger errors during spring and autumn, while the best agreement is found in summer. Overall, Aeolus wind observations over Antarctica show good consistency with radiosondes, meet ESA mission performance requirements, and provide reliable support for polar weather prediction and climate research.

The annular solar eclipse of 21 June 2020, provided a valuable opportunity to examine the ionospheric response to celestial events. This study analyzes variations in Vertical Total electron content (VTEC) over the equatorial region using data from the UQRG Global Ionospheric Map (GIM), ground-based GPS-TEC measurements, and equatorial electrojet (EEJ) strength from magnetometers near the eclipse path. Significant VTEC reductions were observed during the eclipse. The early decline began in East Africa and South Asia during the morning hours, while in the Western Pacific region, the reduction occurred in the late afternoon, coinciding with the onset of the eclipse. Around local noon, a delayed decrease was detected at stations located in Southeast and East Asia. 22%–53% VTEC reduction was recorded during the eclipse's main phase, with effects persisting from 35 min to over 8 hr post-eclipse. Post-eclipse variations in dusk sector suggest local electrodynamical effects. The study found no significant impact on EEJ strength between $��130�°�$E to $��144�°�$E, and no substantial counter equatorial electrojet was detected. Conducted during the monsoon season and the longest day of the year, observed VTEC reductions likely result from eclipse-induced pressure changes and cooling effects. GIM visualizations showed that dVTEC% decreased by up to $��\sim �$40% globally. The findings indicate that VTEC decreases are not strongly correlated with obscuration percentage, highlighting complexity of ionospheric responses. This study enhances understanding of eclipse-driven ionospheric variability, emphasizing the role of photoionization, recombination, geographic location, and local time, with implications for space weather forecasting and ionospheric modeling.

Magmatic intrusions in rifted continental margins play a critical role in shaping structural evolution, yet their geometry and emplacement mechanisms remain poorly constrained in salt-influenced terrains. The Moroccan Central High Atlas, a segment of the Atlas fold-and-thrust belt, hosts extensive Middle Jurassic to Early Cretaceous intrusions that interact intricately with pre-existing rift structures and evaporite-rich stratigraphy. Yet, their deep subsurface architecture remains poorly resolved due to limited high-resolution imaging. Here, we integrate legacy aeromagnetic and gravity data with modern 2D forward modelling and 3D inversion to resolve the spatial distribution, depth extent, and structural controls of magmatic bodies beneath the Central High Atlas. Our results reveal steeply dipping, dyke-like intrusions aligned with ENE–WSW-trending inherited faults, indicating tectonic inheritance strongly influenced magmatic plumbing. We demonstrate that ascending magmas exploited pre-existing tectonic fabrics, driving salt mobilization and contributing to the growth of diapiric structures. These findings provide the first crustal-scale geophysical image of subsurface intrusions in this region, establishing a genetic link between salt tectonics, magma emplacement, and structural inheritance. Importantly, this is the first 3D crustal-scale geophysical reconstruction of Jurassic magmatic plumbing in the Atlas, revealing their role in diapirism and regional uplift.

Ocean surface wave climates are shaped by both atmospheric forcing and underlying ocean conditions. Variability in open-ocean wave heights subsequently reflects complex interactions occurring across a broad range of spatial and temporal scales. Many of the processes driving this variability take place at small spatial scales that have been previously poorly resolved by sparse altimetry observations and coarse global wave models. The Surface Water and Ocean Topography (SWOT) mission offers a new opportunity to observe variability at these scales with unprecedented two-dimensional measurements of significant wave height (SWH) from the Ka-band radar interferometer (KaRIn). In this study, we evaluate the accuracy of SWOT's KaRIn SWH estimates at an open-ocean calibration site off Central California by comparing them to in situ wave measurements from a closely spaced array of buoys. SWOT KaRIn SWH measurements are validated at the calibration site with high fidelity and perform consistently in additional comparison to a network of coastal wave buoys. The centered root-mean-square error ranges from 0.10 to 0.17 m across the various data sets and product versions, with correlation coefficients exceeding 0.98. Additionally we show that SWOT is capable of accurately resolving gradients in wave conditions over short spatial scales (10–90 km), and the high resolution two-dimensional KaRIn observations better represents spatial variability in SWH than either traditional altimetry or coarse-grid operational numerical wave models run without currents. Overall, these findings validate SWOT's wide-swath observations as a powerful tool for observing and understanding ocean surface wave conditions and their connection to broader ocean-atmosphere dynamics.

X-band Phased array radars are characterized by high spatial and temporal resolution, but suffer from a range of data quality problems, such as echo voids after the filtering of ground clutter, abnormal radials, radial obstructions and irregular missing radar echoes. This paper proposes a radar echo image restoration model (GCD) based on color correction and detail enhancement of adversarial generative networks with dual-stream encoder-decoder. To overcome the challenge of restoring strong echo regions, a multi-scale Feature Alignment (FA)-based strong echo color correction module is designed to achieve FA between original features and radial obstruction features. Additionally, a Local Detail Enhancement Module is designed to enhance the high-frequency texture information in the strong echo regions. The GCD not only applies to all types of radar PPI images but also significantly reduces the time required for initial data processing compared to traditional methods (Sliding Window Filling method). Specifically, the speedup ratio for single-image testing is 361.28. Addressing the issue of radial obstructions, GCD achieves an improvement of 10.15 in PSNR and a notable decrease of 18.921 dBZ in $��\Delta �$|dBZ|, thereby resolving the problem of false enhancement of meteorological echo edges that occurs with the Sliding Window Filling method. When compared to the base model, GCD further enhances the restoration of strong echoes, with a decrease in $��\Delta �$|dBZ| by 0.654. The method proposed fills the gap in the field of radar data quality control in image processing and promotes the development of artificial intelligence in this area.

El Niño and the Southern Oscillation (ENSO) is the strongest inter-annual signal in the global climate system with worldwide climatic, ecological, and societal impacts. Over the past decades, the research on ENSO prediction and predictability has attracted broad attention. Typical prediction efforts based on physically coupled models (e.g., SINTEX-F, CanCM4) demonstrate skill at short lead times (approximately 6–12 months) but tend to lose predictability rapidly over longer horizons. Benefiting from their ability to capture nonlinear dependencies and improve long-term accuracy, deep learning methods such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks have been widely applied in the prediction of ENSO-related indices, such as the Niño 3.4 index. In this study, we propose a newly designed neural network, named ACTNet, by incorporating a self-attention mechanism into a CNN + LSTM architecture. ACTNet is designed to process the past 12 months of global sea surface temperature (SST), heat content, zonal wind (UA), and meridional wind (VA) as inputs, where CNN layers extract spatial patterns, LSTM layers capture temporal dependencies, and a self-attention mechanism highlights critical spatiotemporal relationships for accurate ENSO prediction. It can predict the Niño 3.4 index at a monthly resolution up to 24 months in advance reasonably well, achieving correlation coefficients exceeding 0.5. Compared to conventional CNN and CNN + LSTM models, ACTNet demonstrates improved spatiotemporal feature extraction and long-lead prediction skill. Another, independent aspect is ENSO-type prediction based on historical observed SST anomalies. Since ENSO events manifest in different types—such as Eastern Pacific and Central Pacific El Niño, as well as their La Niña counterparts—distinguishing these types is crucial for understanding regional climate impacts. To this end, we further employed an LSTM model to classify events into six defined ENSO types based on Niño 3 and Niño 4 indices, achieving a classification accuracy of 70.5% at a 12-month lead time.

Drought poses a major threat to agriculture and food security in the Horn of Africa (HOA), where monitoring efforts are hindered by sparse in situ observations and a lack of ground truth data. In this paper, a new self-supervised drought classification model, Seasonal Anomaly Embedding with Vision Transformers (SAED-ViT) is proposed using satellite-derived seasonal anomalies of NDVI, Land Surface Temperature (LST), and precipitation. The method employs the masked autoencoders with Vision Transformers (MAE-ViT) to learn robust spatiotemporal representations from 25 years of satellite Earth observation data (2000–2024). The learned latent features are clustered using unsupervised K-Means to identify semantically meaningful drought regimes, which are then mapped to standardized severity classes without requiring predefined thresholds or labeled data. The results exhibit high spatial accuracy and temporal coherence across a broad range of agro-climatic regions with capturing the large-scale droughts in 2011, 2017, and 2022. Quantitatively compared and verified against Standardized Precipitation Evapotranspiration Index (SPEI), the agreement is strong (r = −0.91, P-value < 0.01) and better when compared to the conventional indexes like NDVI, TCI, and VHI. SAED-ViT achieved robust and label-free drought-severity classification across multiple satellite data sources.

Precipitation downscaling is essential for generating high-resolution data from coarse-resolution global climate models and assessing the environmental impacts of climate change at regional and local scales. Convolutional Neural Networks (CNNs) are an emerging critical deep-learning technique that promises significant improvements over other downscaling methods. This study employs three attention-enhanced CNNs including Attention-based Laplacian Pyramid Network (AttLap), Attention and Convolutional Mix Network (ACMix) and Multi-scale Attention Network (MAN), and further evaluates their performance in regional precipitation downscaling. Focusing on the Middle Reaches of the Yellow River in China (MRYR), we utilize ERA5 atmospheric variables and Global Precipitation Measurement (GPM) data for model training and testing. Our results indicate that all the attention-enhanced CNNs improve spatio-temporal precipitation simulations across daily, monthly, and annual timescales compared to the conventional CNN model. Notably, the AttLap model shows the greatest improvements compared to the conventional CNN, reducing root-mean-square error of daily precipitation by 10.1% and increasing the correlation coefficient by 16.7% for the regional mean. Moreover, the attention mechanism improves the model's ability to simulate extreme precipitation, showing that the 95th and 99th percentiles of predicted precipitation are much closer to that of GPM data. Meanwhile, the probability density function for daily precipitation in the attention-enhanced CNNs exhibits better agreement with GPM data, particularly for heavy precipitation, further confirming the advantage of the attention mechanism in simulating extreme precipitation. These findings indicate that the attention-enhanced CNNs significantly improve the ability to capture the spatio-temporal precipitation features, thereby enhancing the downscaling accuracy. The study highlights the potential of attention-enhanced models for regional precipitation downscaling, providing valuable tools for climate projection and water resource management in complex terrain regions.

The Korea Pathfinder Lunar Orbiter (KPLO) spacecraft utilizes the KPLO Magnetometer (KMAG) payload, a three-fluxgate magnetometer array mounted on a 1.2 m boom, to measure crustal and induced lunar magnetic fields. The short boom length exposes the magnetometers to intricate, multi-source stray magnetic fields. These interference signals include a low-frequency, 20 nT peak-to-peak signal from the solar panels and batteries as the spacecraft transitions between sunlight and darkness during certain orbital phases. These stray magnetic fields impede the analysis of lunar magnetic anomalies with magnitudes up to 3 nT at a 100 km altitude. Additionally, downlink issues during the mission's initial stages occasionally resulted in data gaps of up to 12 min (approximately 13% of the orbit) in several orbits. To overcome these data quality challenges, we present a comprehensive three-component method: (a) the Recurrent Forecasting Multichannel Singular Spectrum Analysis (M-SSA) algorithm interpolates data gaps, (b) Wavelet-Adaptive Interference Cancellation for Underdetermined Platforms (WAIC-UP) removes stray magnetic fields from the continuous magnetometer measurements, and (c) the Removal Algorithm for Magnetometer Environmental Noise (RAMEN) gradiometry algorithm corrects low-frequency trends not observed by WAIC-UP. We demonstrate the efficacy of our approach by comparing the results with contemporaneous magnetic field measurements from the lunar-orbiting ARTEMIS-P1 spacecraft and lunar crustal magnetic field maps from the Lunar Prospector and Kaguya missions. This integrated application of M-SSA, WAIC-UP, and RAMEN enables KMAG to reliably investigate lunar magnetic fields despite non-dipolar spacecraft interference and intermittent data gaps.