{"title":"Analyzing the Inversion Performance of a Permanent Urban Column GHG Network: An OSSE Perspective","authors":"Jun Zhang, Jia Chen, Kai Wu, Haoyue Tang","doi":"10.1029/2024EA004175","DOIUrl":"https://doi.org/10.1029/2024EA004175","url":null,"abstract":"<p>Observations of atmospheric columns offer an effective approach to monitoring greenhouse gas (GHG) emissions, as they are less sensitive to the dynamics of atmospheric transport in comparison to in situ measurements. MUCCnet, the world's first permanent urban ground-based column network, has been utilized as an innovative method for measuring column GHGs. We present here an observing system simulation experiment framework to characterize the behavior of this unique network in estimating urban CO<sub>2</sub> emissions. An assumed in situ tower-based network (AISTnet) is performed to improve our understanding of MUCCnet's observing performance. We conduct a set of Bayesian atmospheric inversions to validate the current network deployment strategy and analyze its sensitivity to large point sources (LPSs). From our base inversions, we found overall good consistency between MUCCnet and AISTnet inversions, with nearly all grid cells showing corrections in the same direction during the inversions. While the sensitivities of in situ CO<sub>2</sub> synthetic observations are approximately an order of magnitude higher than those of column measurements, the column measurements have the advantage of broader coverage. This leads to larger uncertainty reduction around the site locations in the AISTnet inversions, while the MUCCnet inversions present larger values over the area away from the network. The inaccurate information of the LPSs provided in the prior estimate can adversely impact the estimated emissions. Our results suggest that MUCCnet is less sensitive to LPSs errors compared to AISTnet. The findings of this work can contribute valuable insights for advancing future observing strategies in an urban environment.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 5","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004175","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144140377","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}
K. Stephan, K. Rammelkamp, M. Baqué, S. Schröder, A. Pisello, K. Gwinner, G. Ortenzi, P. Irmisch, F. Sohl, V. Unnithan
{"title":"Multi-Spectral Field Study of Planetary Analog Material in Extreme Environments—Alteration Products of Volcanic Deposits of Vulcano/Italy","authors":"K. Stephan, K. Rammelkamp, M. Baqué, S. Schröder, A. Pisello, K. Gwinner, G. Ortenzi, P. Irmisch, F. Sohl, V. Unnithan","doi":"10.1029/2024EA004036","DOIUrl":"https://doi.org/10.1029/2024EA004036","url":null,"abstract":"<p>The potential of multi-spectral investigations for planetary exploration strongly depends on the specific geologic environment and related science questions. In this work, we used a visible-near infrared spectrometer, a laser-induced breakdown spectroscopy (LIBS) instrument, and a Raman spectrometer for studying acid alteration of volcanic deposits in the field as an analog for what can be potentially observed on Mars. These deposits were studied on Vulcano, one of the Aeolian Islands/Italy, where volcanic deposits are affected by active hydrothermal alteration processes and fumarolic activity. The results show that VIS-NIR spectroscopy is sufficient to identify the major minerals formed through the alteration process. This is the only technique that can identify and characterize hydrated silica, the major alteration residue, whose spectral properties vary depending on environmental conditions and the formation process. However, only LIBS spectra allow a detailed insight into the geochemistry of the pristine volcanic deposits, which is needed to define the starting point of the alteration process. LIBS also indicated the existence of chemical elements for which no corresponding mineral could be identified in the VIS-NIR data, presumably since their spectral signature is masked by strongly absorbing species. These minerals, however, could be confirmed in the Raman spectra—nicely completing the achieved results and highlighting the high potential of the sensor suite for our study.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 5","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004036","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144140378","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":"Retrospective Mapping of Global Snow and Ice Cover Beyond the Satellite Observational Era","authors":"Kingsley K. Kumah, Omid Zandi, 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, Zhuowei Xiao, Jinlai Hao, 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}
G. Nodjoumi, C. H. Brandt, J. E. Suárez-Valencia, E. Luzzi, M. Valiante, A. P. Rossi
{"title":"Collaborative and Reproducible Planetary Science Through the Europlanet GMAP JupyterHub Processing Environment","authors":"G. Nodjoumi, C. H. Brandt, J. E. Suárez-Valencia, E. Luzzi, M. Valiante, 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}
R. R. Nelson, S. S. Kulawik, C. W. O’Dell, J. McDuffie, A. Eldering
{"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, S. S. Kulawik, C. W. O’Dell, J. McDuffie, A. Eldering","doi":"10.1029/2024EA003975","DOIUrl":"https://doi.org/10.1029/2024EA003975","url":null,"abstract":"<p>NASA's Orbiting Carbon Observatory-2 (OCO-2) has the goal of accurately estimating column-averaged dry-air mole fractions of carbon dioxide (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mi>X</mi>\u0000 <msub>\u0000 <mrow>\u0000 <mi>C</mi>\u0000 <mi>O</mi>\u0000 </mrow>\u0000 <mn>2</mn>\u0000 </msub>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${X}_{{mathrm{C}mathrm{O}}_{mathrm{2}}}$</annotation>\u0000 </semantics></math>). In order to fit the measured radiances, many parameters besides <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mtext>CO</mtext>\u0000 <mn>2</mn>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${text{CO}}_{mathrm{2}}$</annotation>\u0000 </semantics></math> are included in the optimal estimation state vector, including atmospheric water vapor and temperature. The current operational <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mi>X</mi>\u0000 <msub>\u0000 <mrow>\u0000 <mi>C</mi>\u0000 <mi>O</mi>\u0000 </mrow>\u0000 <mn>2</mn>\u0000 </msub>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${X}_{{mathrm{C}mathrm{O}}_{mathrm{2}}}$</annotation>\u0000 </semantics></math> 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 <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mtext>CO</mtext>\u0000 <mn>2</mn>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${text{CO}}_{mathrm{2}}$</annotation>\u0000 </semantics></math> 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}