Earth and Space Science最新文献

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.

Internal tides are sub-surface inertia-gravity waves that generate significant sea surface height signals detectable with satellite altimetry. The Surface Water and Ocean Topography (SWOT) mission provides an exciting opportunity to characterize these signals with unprecedented spatial detail. Separating tidal and non-tidal oceanic signals is necessary for achieving the SWOT mission's objective of advancing our understanding of mesoscale and submesoscale processes. In this study, we evaluate the performance of a data-assimilative HYbrid Coordinate Ocean Model (HYCOM) forecast system in resolving both phase-locked and non-phase-locked internal tides during the SWOT Cal/Val period. We compare HYCOM's effectiveness to the High-Resolution Empirical Tide model (HRET22), which is currently used for internal tide corrections but only accounts for the phase-locked component. HYCOM achieves an average of 5% greater reduction in phase-locked internal tide variance and a 24.6% greater total variance reduction compared to HRET22 by also accounting for non-phase-locked internal tides. At the $���M_2��$ frequency, HYCOM reduces up to 73% of total internal tide variance in SWOT observations, including substantial contributions from the non-phase-locked component. Despite these advances, persistent residuals remain in energetic, topographically complex areas, pointing to the continued need for improved modeling and data assimilation. These findings demonstrate the crucial role of forecast models in advancing internal tide mapping and significantly improving altimetric data correction in the era of SWOT oceanography.

The goal of this paper is to estimate the soft X-ray signature associated with dense and fast magnetosheath jets streaming toward the dayside magnetosphere. We developed a non-self-consistent kinematic approach for simulating numerically the transport of high-speed plasma jets in the magnetosheath. Our methodology is based on global magnetohydrodynamic simulations which provide the background state of the terrestrial magnetosphere and theoretical insight on the propagation of high-speed plasma jets. We compute line-of-sight and time-integrated intensity maps of the soft X-rays generated by the charge exchange process taking place when the high-speed jet interacts with the background exosphere. The X-rays are detected by a virtual telescope launched into the simulation domain. Our results show that the soft X-ray signature of a dense and fast plasma jet is visible in the magnetosheath. We can correctly characterize the detected jets with different setups for the virtual telescope. The impact of density on the jet's X-ray signature is stronger than the impact of bulk velocity, the denser jets being more likely to be detected by an X-ray telescope than the faster ones. We discuss an image processing technique based on frame differencing which may allow an improvement of the X-ray visibility of high-speed jets. We also show that the detection of jets is enhanced considerably when the soft X-ray telescope is placed in the equatorial plane, pointing toward the magnetotail.

On 10 June and 27 September 2024, two workshops were held at GFZ Potsdam under the umbrella of the Geo. X Research Network of Geosciences to discuss the unresolved question of the overestimation and lack of scattering of modeled ring current electrons during geomagnetic storms. At the workshops, we discussed the potential contributions to the lack of scattering of electron cyclotron harmonic (ECH) waves, chorus waves, time-domain-structures (TDS), the non-linear effects of wave-particle interactions, and induced electric fields. A case study shows that the scattering by ECH waves is insufficient to account fully for the missing electron loss. More work must be done to understand the potential effects of inaccuracies in the assumed chorus wave models, TDS, and the non-linear effects of wave-particle interactions. Including induced electric fields in ring current simulations is an important step to describe the electron drifts more accurately. Explaining the missing loss process is crucial for space weather applications of surface charging effects, which rely on accurate predictions of ring current electron fluxes.

This study evaluates the capability of a robotic arm equipped with a force-torque sensor and a specially designed scoop to perform geomechanical characterization of high-fidelity lunar highlands regolith simulant. Experiments focused on pressure-sinkage, shear strength, and angle of repose to assess the performance and applicability of the system to lunar exploration and infrastructure development. Results demonstrated that pressure-sinkage measurements using the scoop reliably characterize the in situ relative density of regolith simulants, showing clear trends as a function of material density. This capability highlights the potential for real-time assessments of local regolith properties during lunar missions. Shear strength experiments identified a need for advanced robotic arm motion controls for shear testing; alternative methods and advanced modeling techniques for determining shear strength using the scoop are under active investigation. Angle of repose tests confirmed the ability of the robotic arm, scoop, and imaging hardware to measure this property accurately, showcasing the versatility of this approach for regolith characterization. The findings underscore the promise of robotic arms for performing critical geomechanical measurements on planetary surfaces given properly designed end effectors that would enable data collection essential for optimizing rover traverse paths, selecting infrastructure sites, investigating geologic history, and supporting both scientific exploration and settlement planning. These results support the inclusion of geomechanical measurement payloads in future missions, directly advancing NASA's Artemis lunar exploration program objectives.

Since the 2010 launch of Cryosat-2, a new generation of altimeters, referred to as SAR altimetry, has emerged and partially replaced the previous conventional altimeters known as Low Resolution Mode (LRM) altimetry. A surface slope correction has been previously developed for LRM altimetry. However, the differences in the way the two altimeters work, and in particular their radar footprint, make LRM altimeter slope correction inapplicable to SAR altimetry. Thus, in this paper, a slope correction model is provided for SAR altimetry, derived from the LRM-based approach. The shape of the SAR footprint induces that height correction depends on each satellite mission. Consequently, a generic method allowing to generate global maps of height correction for distinct missions is provided. The maps are computed for the Sentinel-6A mission and the importance of correcting this effect for SAR altimetry is highlighted by studying the sea surface height anomaly biases between Sentinel-6A SAR and LRM measurements. Finally, it is shown that applying the slope correction to Sentinel-6A SAR mode sea surface height anomaly measurements enhances their consistency with the latest Mean Sea Surface (MSS) model, reducing the root mean square error between the sea surface height anomaly and the MSS model by up to 1 cm.

Global Navigation Satellite System-Acoustic (GNSS-A) seafloor geodetic observation is an effective technique not only for measuring offshore crustal deformation but also for detecting underwater sound speed structures. Collecting acoustic range data along survey tracks with broad spatial coverage is typically essential to detect horizontal gradients of the sound speed structure. Although acoustic ranging data sets with broad spatial coverage have been acquired using research vessels, the acquisition of such data sets has often been difficult using an unmanned sea surface platform, such as a Wave Glider. To ensure positioning accuracy for insufficient acoustic ranging data sets, constraining the horizontal gradients of the sound speed structure using independent information is essential. In this study, we examined the applicability of ocean models to improve GNSS-A positioning accuracy for insufficient acoustic ranging data sets. We calculated the sound speed parameters expressing the horizontal gradients of the sound speed structure using the ocean models JCOPE2M and MOVE/MRI.COM, and compared them with those estimated by GNSS-A using actual acoustic ranging data sets with broad spatial coverage. The results illustrated that these ocean models have the potential to improve the positioning accuracy when large-scale horizontal inhomogeneity exists in the sound speed structure (e.g., an oceanic current). However, the GNSS-A analysis results using actual data indicate a significant influence of small-scale horizontal inhomogeneities, suggesting that higher-resolution ocean models are required to further improve positioning accuracy.