Earth and Space Science最新文献

{"title":"Regional Uncertainty Analysis in the Air–Sea CO2 Flux","authors":"L. Gloege, M. D. Eisaman","doi":"10.1029/2024EA004032","DOIUrl":"https://doi.org/10.1029/2024EA004032","url":null,"abstract":"<p>Accurate quantification of the ocean carbon sink and its associated uncertainty is critical for guiding international policy efforts and the accurate monitoring, reporting, and verification of marine carbon dioxide removal interventions. Here we use error propagation to break down the uncertainty in air–sea <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mtext>CO</mtext>\u0000 <mn>2</mn>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${text{CO}}_{2}$</annotation>\u0000 </semantics></math> flux into three primary sources: the gas transfer velocity <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mfenced>\u0000 <msub>\u0000 <mi>k</mi>\u0000 <mi>w</mi>\u0000 </msub>\u0000 </mfenced>\u0000 </mrow>\u0000 <annotation> $left({k}_{w}right)$</annotation>\u0000 </semantics></math>, the solubility <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mfenced>\u0000 <msub>\u0000 <mi>K</mi>\u0000 <mn>0</mn>\u0000 </msub>\u0000 </mfenced>\u0000 </mrow>\u0000 <annotation> $left({K}_{0}right)$</annotation>\u0000 </semantics></math>, and the difference in partial pressure of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mtext>CO</mtext>\u0000 <mn>2</mn>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${text{CO}}_{2}$</annotation>\u0000 </semantics></math> <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mfenced>\u0000 <mrow>\u0000 <mi>Δ</mi>\u0000 <msub>\u0000 <mtext>pCO</mtext>\u0000 <mn>2</mn>\u0000 </msub>\u0000 </mrow>\u0000 </mfenced>\u0000 </mrow>\u0000 <annotation> $left({Delta }{text{pCO}}_{2}right)$</annotation>\u0000 </semantics></math> between the ocean and atmosphere. These are further decomposed into uncertainties from the underlying variables (e.g., temperature and salinity used to calculate <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mi>K</mi>\u0000 <mn>0</mn>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${K}_{0}$</annotation>\u0000 </semantics></math>). We find gas transfer velocity is the dominant term driving uncertainty in the air–sea <span></span>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004032","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144292296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DepthFormer: Depth-Enhanced Transformer Network for Semantic Segmentation of the Martian Surface From Rover Images","authors":"Yuan Ma,&nbsp;Zhaojin Li,&nbsp;Bo Wu,&nbsp;Ran Duan","doi":"10.1029/2024EA003812","DOIUrl":"https://doi.org/10.1029/2024EA003812","url":null,"abstract":"<p>The Martian surface, with its diverse landforms that reflect the planet's evolution, has attracted increasing scientific interest. While extensive data is needed for interpretation, identifying landform types is crucial. This semantic information reveals underlying features and patterns, offering valuable scientific insights. Advanced deep learning techniques, particularly Transformers, can enhance semantic segmentation and image interpretation, deepening our understanding of Martian surface features. However, current publicly available neural networks are trained in the context of Earth, rendering the direct use of the Martian surface impossible. Besides, the Martian surface features poorly texture and homogenous scenarios, leading to difficulty in segmenting the images into favorable semantic classes. In this paper, an innovative depth-enhanced Transformer network—DepthFormer is developed for the semantic segmentation of Martian surface images. The stereo images acquired by the Zhurong rover along its traverse are used for training and testing the DepthFormer network. Different from regular deep-learning networks only dealing with three bands (red, green and blue) of images, the DepthFormer incorporates the depth information available from the stereo images as the fourth band in the network to enable more accurate segmentation of various surface features. Experimental evaluations and comparisons using synthesized and actual Mars image data sets reveal that the DepthFormer achieves an average accuracy of 98%, superior to that of conventional segmentation methods. The proposed method is the first deep-learning model incorporating depth information for accurate semantic segmentation of the Martian surface, which is of significance for future Mars exploration missions and scientific studies.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003812","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144273233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Bootstrapping Convolutional Neural Network Technique for Optimizing Automated Detection of Equatorial Plasma Bubbles by Optical All-Sky Imagers","authors":"Daniel Okoh,&nbsp;Claudio Cesaroni,&nbsp;Babatunde Rabiu,&nbsp;Kazuo Shiokawa,&nbsp;Yuichi Otsuka,&nbsp;Samuel Ogunjo,&nbsp;Aderonke Akerele,&nbsp;John Bosco Habarulema,&nbsp;Bruno Nava,&nbsp;Yenca Migoya-Orué,&nbsp;Punyawi Jamjareegulgarn,&nbsp;Adeniran Seun,&nbsp;Ogechi Adama,&nbsp;George Ochieng,&nbsp;James Ameh,&nbsp;Adero Awuor,&nbsp;Paul Baki","doi":"10.1029/2024EA004117","DOIUrl":"https://doi.org/10.1029/2024EA004117","url":null,"abstract":"<p>Equatorial plasma bubbles (EPBs) disrupt satellite-based communication and navigation systems, particularly in equatorial regions. Reliable detection and classification of EPBs from all-sky imager (ASI) images are essential for accurate space weather monitoring and forecasting. This study presents a novel bootstrapping convolutional neural network (CNN) approach to optimize automated EPB detection on ASI images for operational space weather monitoring applications, and overcoming challenges related to image variability and imbalanced data sets. Data used for CNN training were obtained from the optical mesosphere thermosphere imagers ASI installed at the Space Environment Research Laboratory, National Space Research and Development Agency, Abuja during the period from 2015 to 2020. Our method involved training three sub-models, and aggregating their predictions. The CNN trainings were conducted on three sub-datasets of 3,000 images each, categorized as “EPB,” “Noisy/Cloudy” or “No EPB.” Three corresponding sub-models were developed from the CNN trainings. The three sub-model classifications independently gave prediction accuracies of 98.67%, 98.33%, and 95.83% on a reserved test data set of 600 images. Ensemble models further improved the model prediction accuracies to 99.17% and 99.33% for methods based on the mean of sub-model probabilities and the mode of sub-model classifications respectively. Our results indicate that the bootstrapping CNN technique enhanced the EPB detection accuracy, providing a powerful tool for real-time space weather monitoring applications, and implications for improving operational reliability of satellite-based navigation and communication in the equatorial region.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004117","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144256420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Simple Snowfall Retrieval Algorithm for the GPM Dual-Frequency Precipitation Radar: Development and Validation With OLYMPEX Campaign Observation","authors":"S. Akiyama,&nbsp;S. Shige,&nbsp;K. Aonashi,&nbsp;T. Iguchi","doi":"10.1029/2024EA003962","DOIUrl":"https://doi.org/10.1029/2024EA003962","url":null,"abstract":"<p>The current operational algorithm for the Ku- and Ka-band Dual-frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) satellite, which does not effectively utilize Ka-band radar, underestimates snowfall amount. We developed a dual-frequency method (DF-method) that can be incorporated into the framework of the DPR operational algorithm. Estimates from the DF-method, as well as those from the operational algorithm, were validated against data nearly simultaneously measured by in situ airborne instruments and those from a ground-based radar during the Olympic Mountains Experiment (OLYMPEX). The results showed the DF-method produced high correlation, but some bias dependent on an assumed particle model. Both the operational algorithm and the DF-method using the scattering properties of the spheroid model equivalent to the best aggregate model yielded unsatisfactory results, indicating that it is important to use realistic snow scattering properties in the DF-method, rather than relying on the Mie or T-matrix scattering.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003962","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144256418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unsupervised Anomaly Detection for Volcanic Deformation in InSAR Imagery","authors":"Robert Popescu,&nbsp;Nantheera Anantrasirichai,&nbsp;Juliet Biggs","doi":"10.1029/2024EA003892","DOIUrl":"https://doi.org/10.1029/2024EA003892","url":null,"abstract":"<p>Satellite-based Interferometric Synthetic Aperture Radar (InSAR) images have the potential to detect volcanic deformation prior to eruptions, but while a vast number of images are routinely acquired, only a small percentage contain volcanic deformation events. Manual inspection could miss these anomalies, and an automatic system modeled with supervised learning requires suitably labeled data sets. To tackle these issues, this paper explores the use of unsupervised deep learning on InSAR images for the purpose of identifying volcanic deformation as anomalies. We test three different state-of-the-art architectures, one convolutional neural network Patch Distribution Modeling (PaDiM) and two generative models (GANomaly and Denoising diffusion probabilistic models (DDPM)). We propose a preprocessing approach to deal with noisy and incomplete data points. We further improve the performance of PaDiM by using a weighted distance, assigning greater importance to features from deeper layers. The final framework was tested with five different volcanoes, which have different characteristics and its performance was compared against an existing supervised learning method for volcanic deformation detection. The experiments show that our final anomaly detection outperforms the supervised learning method, particularly where the characteristics of deformation are unknown. Our framework can thus be used to identify deformation at volcanoes without needing prior knowledge about the deformation patterns present there.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003892","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144256419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The \u0000 \u0000 \u0000 b\u0000 \u0000 $b$\u0000 -Value Tomography of the Calabrian Arc","authors":"C. Godano,&nbsp;G. Petrillo,&nbsp;A. Tramelli,&nbsp;V. Convertito","doi":"10.1029/2024EA004065","DOIUrl":"https://doi.org/10.1029/2024EA004065","url":null,"abstract":"<p>In the Calabrian Arc subduction zone, the notable lack of seismicity at depths near 100 km strongly suggests the presence of slab detachment. Contrary to typical patterns, where <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>b</mi>\u0000 </mrow>\u0000 <annotation> $b$</annotation>\u0000 </semantics></math>-values decrease with depth, our b-value mapping reveals unexpectedly high <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>b</mi>\u0000 </mrow>\u0000 <annotation> $b$</annotation>\u0000 </semantics></math>-values at these depths. Within the 100–150 km depth interval, the gradient of the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>b</mi>\u0000 </mrow>\u0000 <annotation> $b$</annotation>\u0000 </semantics></math>-value reaches its peak, indicating a significant reduction in stress. We propose four potential interpretations for these observations: (a) fluid-induced weakening due to dehydration processes, (b) heterogeneity at the slab tip reducing rupture propagation, (c) creeping zone behavior at the detachment tip, and (d) post-detachment damage to the rocks, leaving them unable to support stress. These hypotheses remain beyond experimental verification at present. This study underscores the complex interplay of geological processes at depth and their implications for seismic hazard assessment in subduction zones.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of 3He Neutron Sensors for Planetary Penetrator Experiments","authors":"David J. Lawrence,&nbsp;Bruce L. Barraclough,&nbsp;Richard C. Elphic,&nbsp;Paul G. Lucey","doi":"10.1029/2024EA004091","DOIUrl":"https://doi.org/10.1029/2024EA004091","url":null,"abstract":"<p>This paper provides a report on a test that was carried out over 20 years ago to demonstrate that two ³He gas proportional neutron sensors could survive a high-impact penetrator test. This test was carried out as part of a risk reduction effort for a proposed mission that would send multiple penetrators to landing locations within lunar permanently shaded regions (PSRs). After landing, the neutron sensors would carry out in situ measurements within the PSRs to quantify the hydrogen abundances within these regions. Two penetrator shots were successfully carried out with the neutron sensors enclosed in the penetrators. The deceleration value for the shots exceeded 1,400 G's over less than 20 milliseconds. Pre- and post-penetration measurements of the ³He sensors show that the sensors themselves suffered no degradation in performance; one non-spaceflight quality high-voltage connector did indicate performance degradation. These results provide confidence that these types of ³He neutron sensors could be successfully used in a future penetrator mission to a planetary body.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PM2.5 Forecasting at U.S. Embassies and Consulates Worldwide Using NASA Model Powered by Machine Learning","authors":"Junhyeon Seo,&nbsp;Alqamah Sayeed,&nbsp;Seohui Park,&nbsp;John Kerekes,&nbsp;Stephanie M. Christel,&nbsp;Mary T. Tran,&nbsp;Pawan Gupta","doi":"10.1029/2025EA004210","DOIUrl":"https://doi.org/10.1029/2025EA004210","url":null,"abstract":"<p>Air quality forecasting is crucial for public health, especially in rural, suburban, and developing areas lacking reliable monitoring data. Hybrid monitoring (surface, satellite, and models) offers a scalable, cost-effective solution for tracking pollution and trends. This work presents a machine learning model that integrates ground measurements with global model outputs assimilating satellite observations to forecast air quality. Ground measurements of fine particulate matter (PM2.5) from over 60 U.S. embassies and consulates were used to calibrate global model outputs for local air quality forecasting. Multi-channel input data was prepared using the Goddard Earth Observing System forward processing for meteorology and aerosol forecasts over 72 hr. An advanced convolutional neural network addressed high-dimensional data and nonlinearities between inputs and outputs. A global model was developed and fine-tuned with continent-specific local models. The global model achieved Root Mean Squared Error (RMSE) and slope of 5.64 μg/m³ and 0.96, respectively. Local models showed improved performance with RMSE of 3.21 μg/m³ and slope of 0.98, outperforming the global model in Air Quality Index predictions by 6.57% in accuracy and greater stability during variability. The forecasts are publicly accessible via an application programming interface, providing global air quality predictions for 269 U.S. embassy and consulate sites to support public health and operational planning.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025EA004210","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144232357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Algorithm for Ice-Water Discrimination in Large Lakes Using ICESat-2 Altimetry and Data Driven Machine Learning","authors":"Isabella Peter,&nbsp;Eric J. Anderson,&nbsp;Matthew R. Siegfried,&nbsp;Nathan T. Kurtz","doi":"10.1029/2024EA004155","DOIUrl":"https://doi.org/10.1029/2024EA004155","url":null,"abstract":"<p>Large freshwater lakes are critical for human life, ecosystem functioning, and the global carbon cycle. However, consistent high-resolution methods to characterize ice over large lakes remain limited. Here we develop an algorithm to progress ice observations over inland bodies of water by improving surface classifications using data derived from ICESat-2, Landsat 8/9 and other operational products. This algorithm implements a hierarchical approach composed of remote sensing products and data driven machine learning. In this study we show that although the current classification method used in ICESat-2 Inland Surface Water Height (ATL13) is prone to overgeneralization and misclassification, our proposed algorithm, which integrates novel classification methods and data-driven machine learning, enhances surface classification accuracy. We tested this algorithm on a wide breadth of data, spanning four ice seasons in the Laurentian Great Lakes. In our algorithm, we developed two prediction methods that outperformed the current classification method in place for ATL13 by 26.46% and 20.37% and is scalable to other inland surface waters because of the global coverage of the necessary parameters for surface classification. Improved surface classification allows for inland surface bodies of water to be observed with greater detail, particularly using ICESat-2 data, and enables the production of improved data sets of ice concentration and thickness. Improved ice information on Earth's largest lakes will have cascading effects on not only public safety and operational efficiency, but also the monitoring of anthropogenic changes in these bodies of water.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004155","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144232358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lightning Prediction in the Tehran Region Using the WRF Model With Multiple Physical Parameterizations and an Ensemble Approach","authors":"Sakineh Khansalari,&nbsp;Maryam Gharaylou","doi":"10.1029/2024EA004097","DOIUrl":"https://doi.org/10.1029/2024EA004097","url":null,"abstract":"<p>This study aims to predict the lightning (thunderstorm) potential in the Tehran region using data from meteorological synoptic stations and the Earth Networks Total Lightning Network (ENTLN). We employed the Weather Research and Forecasting (WRF) model to simulate lightning, focusing on the innermost domain, which spans between 34.5 and 36.5°N, and between 49.5 and 53.25°E. The initial and boundary conditions for the WRF model were derived from the Global Forecast System data set, with a spatial resolution of 0.5°. We analyzed 10 significant lightning events from 2015 to 2022, primarily focusing on the spring season. Lightning simulations were conducted using the WRF model with seven different physical schemes and the Lightning Potential Index. The results indicate that the WRF model, particularly when utilizing the Morrison, WDM6, and NSSL-2 schemes, effectively simulates lightning regions. However, some underestimation was observed, notably in the southwestern portion of the study area. Comparisons with ENTLN data showed that configurations 1 and 2, using WSM6 and Goddard schemes, achieved the highest Probability of Detection, Critical Success Index, and higher Success Rates for actual lightning events. The uncertainty in lightning simulation and the model's sensitivity to physical parameterization highlight the importance of using an ensemble approach in the WRF model. By averaging outputs from different configurations in the ensemble, a more optimal result, closer to observed data, can be achieved. Based on these findings, we recommend the ensemble method as the most reliable approach for more accurate lightning simulations in future studies.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144220166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}