Earth and Space Science最新文献

{"title":"Retrospective Mapping of Global Snow and Ice Cover Beyond the Satellite Observational Era","authors":"Kingsley K. Kumah,&nbsp;Omid Zandi,&nbsp;Ali Behrangi","doi":"10.1029/2024EA004171","DOIUrl":"https://doi.org/10.1029/2024EA004171","url":null,"abstract":"<p>Monitoring Earth's snow and ice cover is essential for diverse applications, including climate studies, hydrological forecasting, and precipitation mapping. This study develops and evaluates methodologies to extend the Global Multisensor Automated Snow and Ice Mapping System (GMASI) records prior to its July 1987 inception, reconstructing high-resolution global snow and ice cover data. Using ERA5 reanalysis variables, three machine learning (ML) approaches—ML-E (ML with ERA5 predictors only), ML-EC (ML with ERA5 and Climatology-based predictors), and ML-ECC (ML with ERA5 predictors, Climatology-based predictors, and additional Consistency Checks)—were tested alongside climatological and fractional cover-based methods. Validation against GMASI (1988–1991) shows that ML-EC and ML-ECC achieve superior alignment, with the latter offering marginal accuracy gains. Both methods demonstrated stable daily estimates, with mean percentage biases for snow and ice cover remaining below 3% during validation. Their high accuracy is further reflected in probabilities of detection (POD) exceeding 97% across key surface types. Across all methods, there was a general tendency to underestimate snow-free areas and overestimate snow-covered regions in the Northern Hemisphere, while classification challenges in the Southern Hemisphere were more pronounced over snow-free land and Antarctic sea ice. The ML-EC approach was subsequently applied to extend the GMASI record back to 1980, capturing seasonal and interannual variability consistent with GMASI-era trends. These results underscore the potential of ML techniques to extend snow and ice cover records as far back as the beginning of the reanalysis era (1940–present), providing invaluable insights for climate analysis and operational applications.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 5","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004171","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135859","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":"Global Distribution of Low Frequency Family Marsquakes From Deep-Learning-Based Polarization Estimation","authors":"Quanhong Li,&nbsp;Zhuowei Xiao,&nbsp;Jinlai Hao,&nbsp;Juan Li","doi":"10.1029/2025EA004303","DOIUrl":"https://doi.org/10.1029/2025EA004303","url":null,"abstract":"<p>The seismometer has recorded thousands of marsquakes. Accurately locating these events is crucial for understanding Mars' internal structure and geological evolution. With only a single station, determining the location, especially the accurate back-azimuth, is more challenging than on Earth. Deep learning, being data-driven, can learn patterns of complex noise that are difficult for traditional methods to model, making it promising for improving back-azimuth estimation of marsquakes. However, challenges arise when applying deep learning to estimate marsquake polarization due to the limited quantity and low signal-to-noise ratios (SNR) of the data. In this study, we trained deep learning models for learning the noise patterns preceding marsquakes to address these challenges. By combining the proposed Sliding Window Inference and Featured-Training (SWIFT) to handle the high uncertainty in P phase picking, we are able to estimate polarizations of low frequency family marsquakes with improved accuracy. As a result, we have further improved the localization of marsquakes by relocating 56 events, including seven Quality C events with epicentral distances over 90°. For two Martian impact events with ground-truth locations, S1000a and S1094b, our deviations are only ∼5.65° and ∼2.72°. Our results reveal a new identified clustered seismicity zone around compressional structures in Hesperia Planum, including seven marsquakes with magnitudes from 2.7 to 3.6. Marsquakes are also widely distributed along the northern lowlands, the dichotomy boundary, and the higher-latitude southern highlands, suggesting a globally distributed pattern. Our renewed marsquake locations provide new insights into the tectonic interpretation of marsquakes.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 5","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025EA004303","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135860","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":"Collaborative and Reproducible Planetary Science Through the Europlanet GMAP JupyterHub Processing Environment","authors":"G. Nodjoumi,&nbsp;C. H. Brandt,&nbsp;J. E. Suárez-Valencia,&nbsp;E. Luzzi,&nbsp;M. Valiante,&nbsp;A. P. Rossi","doi":"10.1029/2025EA004251","DOIUrl":"https://doi.org/10.1029/2025EA004251","url":null,"abstract":"<p>JupyterHub is an open-source system enabling multiple users to access individual computational environments. This facilitates collaborative development and execution of Jupyter notebooks, Python scripts, and other tools among researchers and educators through a unified interface. Through the integration of container technologies, including Docker, JupyterHub achieves seamless scalability for numerous users while maintaining efficient computational resource management. This flexible approach is especially useful in specialized areas like planetary data science, which requires robust and reproducible workflows to manage large volumes of mission data. The Europlanet Geologic MApping of Planetary surfaces (GMAP) project employs a Docker-based JupyterHub deployment to centralize essential data processing tools, such as the Integrated Software for Imagers and Spectrometers (ISIS) and the NASA Ames Stereo Pipeline (ASP). These open-source tools facilitate tasks ranging from image calibration and map projection to stereogrammetry and 3D modeling. The deployment of these elements within Docker containers facilitates simplified installation and consistent performance across disparate hardware configurations. The use of pre-configured image formats within ISIS, ASP, and other GIS and Python libraries allows planetary scientists to efficiently process raw data into analytical products, including Digital Terrain Models. Additionally, JupyterHub's architecture enables secure collaboration via authentication methods (e.g., OAuth, GitHub), with concurrent provision for private and shared data directories. This integrated framework promotes reproducible research by streamlining the sharing of scripts, notebooks, and workflows. The GMAP JupyterHub platform significantly accelerates scientific discovery through the reduction of technical barriers, the promotion of standardization, and the provision of global access to planetary data science resources.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 5","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025EA004251","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144117851","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":"Improving OCO-2 \u0000 \u0000 \u0000 \u0000 X\u0000 \u0000 \u0000 C\u0000 O\u0000 \u0000 2\u0000 \u0000 \u0000 \u0000 ${X}_{{mathbf{C}mathbf{O}}_{mathbf{2}}}$\u0000 Retrievals Through the Scaling of Singular Value Decomposition-Based Temperature and Water Vapor Profiles","authors":"R. R. Nelson,&nbsp;S. S. Kulawik,&nbsp;C. W. O’Dell,&nbsp;J. McDuffie,&nbsp;A. Eldering","doi":"10.1029/2024EA003975","DOIUrl":"https://doi.org/10.1029/2024EA003975","url":null,"abstract":"&lt;p&gt;NASA's Orbiting Carbon Observatory-2 (OCO-2) has the goal of accurately estimating column-averaged dry-air mole fractions of carbon dioxide (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;X&lt;/mi&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;C&lt;/mi&gt;\u0000 &lt;mi&gt;O&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; ${X}_{{mathrm{C}mathrm{O}}_{mathrm{2}}}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;). In order to fit the measured radiances, many parameters besides &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mtext&gt;CO&lt;/mtext&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; ${text{CO}}_{mathrm{2}}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; are included in the optimal estimation state vector, including atmospheric water vapor and temperature. The current operational &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;X&lt;/mi&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;C&lt;/mi&gt;\u0000 &lt;mi&gt;O&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; ${X}_{{mathrm{C}mathrm{O}}_{mathrm{2}}}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; retrieval algorithm (v11) solves for a multiplicative scaling factor on an a priori water vapor profile and an additive offset on an a priori temperature profile. However, simulations have indicated that water vapor and temperature each have 1.5–3 degrees of freedom in the vertical column. This means that the retrieval is limited in its ability to fit the true profiles of temperature and water vapor. Here, we use singular value decomposition to determine the three most explanatory profile “shapes” of water vapor and temperature error, then retrieve a single scaling factor applied to each shape. We assess retrieval errors by comparing to the Total Carbon Column Observing Network (TCCON) and multiple atmospheric &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mtext&gt;CO&lt;/mtext&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; ${text{CO}}_{mathrm{2}}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; inverse models. We find that after applying quality filtering using Data Ordering Genetic Optimization and a custom bias correction, the scatter o","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 5","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003975","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144117852","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":"Identification of the Atmospheric Vortex Shedding Behind the Jeju Island Based on MODIS and ERA5 Data","authors":"Chaoyue Wu,&nbsp;Daoyi Chen,&nbsp;Shengli Chen,&nbsp;Yuqi Zhang,&nbsp;Qiao Su,&nbsp;Yihan Zhang","doi":"10.1029/2024EA004022","DOIUrl":"https://doi.org/10.1029/2024EA004022","url":null,"abstract":"<p>A fascinating phenomenon often occurs as pairs of vortices in two rows on the leeward of some islands. The satellite remote sensing has been a major facilitator to observe this phenomenon. In this study, we conduct a statistical analysis on the occurrence of vortex street by processing 20-year cloud images from the MODIS remote sensing data around Jeju Island in the northwest of Pacific Ocean. The criteria for forming an atmospheric vortex shedding (VS) are also discussed with ERA5 hourly data. The results indicate that the steady upstream flow reaching a certain speed under a strong temperature inversion is beneficial to the formation of VS. Based on the speed of upstream flow and the intensity of temperature inversion which are selected by machine learning methods, fitting lines are constructed to predict the occurrence of vortices, with an accuracy of more than 98%. This fitting relation can probably be used to parameterize the small-scale VS behind mountains in the meso-scale atmosphere simulation system to improve its accuracy.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 5","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108928","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":"Identification of Natural Hydrogen Seeps: Leveraging AI for Automated Classification of Sub-Circular Depressions","authors":"N. Ginzburg,&nbsp;J. Daynac,&nbsp;S. Hesni,&nbsp;U. Geymond,&nbsp;V. Roche","doi":"10.1029/2025EA004227","DOIUrl":"https://doi.org/10.1029/2025EA004227","url":null,"abstract":"<p>Hydrogen has long been used as an energy vector, but the recent discovery of natural hydrogen (H₂) opens the door for its use as a direct energy source. Identifying H₂ seepages is therefore crucial to advance exploration. Although the scientific community does not yet fully understand the parameters controlling H₂ leaks from underground, sub-circular depressions (SCDs) appear to be key indicators associated with these emissions. However, distinguishing SCDs from similar landforms remains a challenge. This study leverages open-source multispectral and high-resolution imagery to train a deep learning model (YOLOv8) for classifying rounded landforms and detecting H₂-related structures (i.e., SCDs). The model achieved 90% accuracy with Google Maps© imagery, outperforming Sentinel-2 multispectral data. Applied to a pre-existing data set from Brazil, the model allowed a large-scale screening, discarding 52% of the structures as non-H₂ emitting ones and pinpointing high-potential areas for field validation. Future enhancements, including, for example, higher-resolution input data and morphometric analysis, would aim to reduce false positives and boost predictive accuracy. This approach significantly improves H₂ exploration efficiency, with global applicability including some region-specific adjustments during post-processing analyses.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 5","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025EA004227","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144100494","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":"Adaptive Super-Resolution for Ocean Bathymetric Maps Using a Deep Neural Network and Data Augmentation","authors":"Koshiro Murakami,&nbsp;Daisuke Matsuoka,&nbsp;Naoki Takatsuki,&nbsp;Mitsuko Hidaka,&nbsp;Junji Kaneko,&nbsp;Yukari Kido,&nbsp;Eiichi Kikawa","doi":"10.1029/2024EA003610","DOIUrl":"https://doi.org/10.1029/2024EA003610","url":null,"abstract":"<p>Machine learning-based image super-resolution is a robust approach for obtaining detailed bathymetric maps. However, in machine learning using supervised data, dissimilarities in the features of training and target data sets degrades super-resolution performance. In this study, we propose a two-step method to generate training data with features similar to those of the target data using image transformation and composition. The super-resolution model trained using the proposed method on the Central Okinawa Trough data was applied to the bathymetry data around Okinotorishima Islands. The method improved the root mean squared error by up to 14.3% without compromising spatial consistency compared with that observed using conventional approaches, thus demonstrating the potential of combining artificial data generation with machine learning for super-resolution bathymetry mapping of the entire ocean floor. The proposed method, independent of the characteristics of training data, is suggested as a potential alternative to acoustic measurements for expanding areas of detailed bathymetric maps.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 5","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003610","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085204","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":"Gap Flows in Nares Strait: Multi-Scale Numerical Model Simulations in Comparison to Aircraft Measurements","authors":"Svenja H. E. Kohnemann,&nbsp;Günther Heinemann","doi":"10.1029/2024EA003912","DOIUrl":"https://doi.org/10.1029/2024EA003912","url":null,"abstract":"<p>The steep topography of Ellesmere Island and the northern Greenland coast, combined with a stable boundary layer, generates intense low-level winds in Nares Strait, that influence sea ice transport. In Smith Sound, a gap flow forms impacting the most productive North Water Polynya. We use the non-hydrostatic regional climate model CCLM with horizontal resolutions of 14, 5 and 1 km to study these processes for the period of an aircraft-based experiment in June 2010. Additionally, CARRA reanalysis is included. All model data resolve the small channel and represent the stable boundary conditions realistically. The highest winds of around 14 m <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mi>s</mi>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 <annotation> ${mathrm{s}}^{-1}$</annotation>\u0000 </semantics></math> are present in the exit region of Smith Sound for June 2010. Comparisons with aircraft profiles for potential temperature and wind speed show small biases and correlations higher than 0.84 for CCLM, along with a good representation of the boundary layer structure. Increasing model resolution from 14 to 5 km yields notable improvements in the representation of these variables. CARRA data are similar or better than CCLM data, except for the potential temperature, where a relatively large warm bias was found. Maximum winds occur at gap exit region of Smith Sound and are associated with gravity waves generated by the barrier, resulting in downward flow within the gap. Model simulations offer the advantage of studying the temporal development and the full three-dimensional structure. The wind maximum is influenced by the gap flow structure for mountain Froude numbers less than 1 and by the position and intensity of a low pressure system in Baffin Bay.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 5","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003912","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144074550","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":"Enhancing Surface PM2.5 Air Quality Estimates in GEOS Using CATS Lidar Data","authors":"Alexander V. Matus,&nbsp;Edward P. Nowottnick,&nbsp;John E. Yorks,&nbsp;Arlindo M. da Silva","doi":"10.1029/2024EA004078","DOIUrl":"https://doi.org/10.1029/2024EA004078","url":null,"abstract":"&lt;p&gt;Spaceborne lidar offers unique advantages for improving global estimates of fine particulate matter (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mtext&gt;PM&lt;/mtext&gt;\u0000 &lt;mn&gt;2.5&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; ${text{PM}}_{2.5}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;), traditionally limited by critical data gaps in the vertical dimension. Here, we present a new method to retrieve &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mtext&gt;PM&lt;/mtext&gt;\u0000 &lt;mn&gt;2.5&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; ${text{PM}}_{2.5}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; relying on ensembles on aerosol extinction available within the GEOS Aerosol Data Assimilation. This study uses 1064-nm backscatter lidar data from the NASA Cloud-Aerosol Transport System (CATS) and model priors from the GEOS model. First, we developed a 1-D ensemble-based variational technique (1-D EnsVar) to perform vertically resolved retrievals of speciated aerosol extinction and surface &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mtext&gt;PM&lt;/mtext&gt;\u0000 &lt;mn&gt;2.5&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; ${text{PM}}_{2.5}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;. Next, we evaluated the performance of 1-D EnsVar retrievals of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mtext&gt;PM&lt;/mtext&gt;\u0000 &lt;mn&gt;2.5&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; ${text{PM}}_{2.5}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; and extinction through an independent validation using measurements from spaceborne, airborne, and ground-based platforms. This approach overcomes traditional limitations by leveraging the strengths of complementary vertical aerosol information from CATS and GEOS to better resolve speciated aerosol optical properties and mass. Assimilating CATS lidar data with the GEOS model reduced bias in surface &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mtext&gt;PM&lt;/mtext&gt;\u0000 &lt;mn&gt;2.5&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; ${text{PM}}_{2.5}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; prediction by 1.1 &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;μ&lt;/mi&gt;\u0000 &lt;mi&gt;g&lt;/mi&gt;\u0000 &lt;mo&gt;/&lt;/mo&gt;\u0000 &lt;msup&gt;\u0000 &lt;mi&gt;m&lt;/mi&gt;\u0000 ","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 5","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004078","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144074551","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":"Improving “23.07” Heavy Rainstorm Simulation Through Assimilating FY-3E MWTS-3 Radiance Data","authors":"Shengjie Zhu,&nbsp;Deqin Li,&nbsp;Shibo Gao,&nbsp;Qian Xie,&nbsp;Xiao Pan","doi":"10.1029/2025EA004363","DOIUrl":"https://doi.org/10.1029/2025EA004363","url":null,"abstract":"<p>FengYun-3E (FY-3E) satellite, launched on 5 July 2021, is equipped with the third-generation Microwave Temperature Sounder (MWTS-3). Two window channels of MWTS-3 are used to build a cloud detection module, and efficient assimilation of MWTS-3 clear-sky radiance data is implemented in the Weather Research and Forecasting Model (WRF). To assess the influence of assimilating MWTS-3 radiances on extreme precipitation weather forecasting, a heavy rainstorm event that occurred in North China from 29 July to 2 August 2023, has been selected. After assimilating MWTS-3 radiance data, the geopotential height field adjusted, leading to a northwestward shift of the Western Pacific Subtropical High as well as an eastward adjustment of the weather system. The forecasts with assimilated MWTS-3 data reduced the root mean square error of temperature, zonal wind, and meridional wind, with clear improvements in temperature forecast skill at the 100–400 hPa levels within the first 33 hr of the forecast period. The 6–hr maximum rainfall center located southwest of Beijing was more successfully reproduced in the model simulation. Additionally, the precipitation forecast skill above the threshold of 15 mm was improved. Moreover, the water vapor transport and vertical motion conditions in Beijing has improved after assimilating MWTS-3 data, which are likely to be contributing factors to the enhancements in the precipitation forecasting performance.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 5","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025EA004363","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949900","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}