Earth and Space Science最新文献

{"title":"Atmospheric Constituent Data Assimilation in NASA's Goddard Earth Observing System","authors":"B. Weir,&nbsp;K. Wargan,&nbsp;C. A. Keller,&nbsp;K. E. Knowland,&nbsp;P. A. Wales,&nbsp;V. Shah,&nbsp;B. M. Karpowicz,&nbsp;N. Balashov,&nbsp;L. E. Ott,&nbsp;R. Todling,&nbsp;S. E. Cohn,&nbsp;S. Pawson","doi":"10.1029/2026EA005031","DOIUrl":"https://doi.org/10.1029/2026EA005031","url":null,"abstract":"<p>Many of today's most significant and urgent scientific questions, including those about the impact of human activity on air quality and radiative forcing, require the synthesis of observations of atmospheric constituents and scientific theory. The past three decades have each seen a step-change in the coverage, accuracy, and precision of constituent observations, and hence the questions they can address, especially from the growing constellation of Earth-observing satellites. This paper is the first comprehensive description of the design and implementation of NASA's Constituent Data Assimilation System (CoDAS), a software package for assimilating constituent observations into the Goddard Earth Observing System (GEOS), an integrated collection of Earth system models. CoDAS is a generalized, tracer-agnostic system that extends previous capabilities to assimilate multiple species from multiple sensors configurable at run time. Data assimilation provides a means of monitoring constituent changes, intercomparing heterogeneous types of observations, and reconciling data and models. Assimilation produces an optimal synthesis of the ingested data and model, taking advantage of the fact that the statistics of model-data differences are simpler than those of the observations themselves. The use of a model enables the propagation of information from all previous observations across space and time, which can improve estimates even where and when data are unavailable.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"13 8","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2026EA005031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616768","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":"Quantitative Evaluation and Error Analysis of GPM DPR Rainfall Products (Version 07) Over Yangtze-and-Huai River Basin in China","authors":"Zelin Wang,&nbsp;Xiong Hu,&nbsp;Jing Sun,&nbsp;Weihua Ai,&nbsp;Zitong Chen,&nbsp;Liang Leng,&nbsp;Shensen Hu,&nbsp;Junqi Qiao,&nbsp;Xianbin Zhao","doi":"10.1029/2025EA004778","DOIUrl":"https://doi.org/10.1029/2025EA004778","url":null,"abstract":"<p>The Dual-Frequency Precipitation Radar (DPR) aboard the Global Precipitation Measurement (GPM) core satellite has advanced global precipitation monitoring with consistent long-term data. This study evaluates the rainfall rate from GPM DPR version 07 Level-2 products over the Yangtze-and-Huai River Basin with rain gauge observations. Although most DPR precipitation samples underestimate rain rates, a few extreme overestimates push the overall average to overestimate. While the algorithm adequately captures light precipitation, its performance degrades significantly as rain intensity increases. The Heidke Skill Score (HSS) and Hanssen and Kuiper Skill Score (KSS) were applied, which reveal that the DPR stably classifies spatially uniform stratiform systems at moderate intensities. However, a critical algorithmic limitation emerges during severe torrential rain (&gt;20 mm/hr). In these extreme events, the stratiform false alarm ratio approaches 100% and the multi-categorical skill scores drop below zero, indicating a failure to identify deep convective cores embedded within broad stratiform regions. This mechanism further confirmed by the absence of a bright band and intense vertical reflectivity extension in their profiles. A deeper investigation was carried out to analyze the vertical structure of samples with significant discrepancies between DPR and rain gauge data. They feature a warm-rain structure, where a high concentration of small raindrops coexists with a low concentration of large ones. It amplifies the conflict between DPR's regional average observations and rain gauges' single-point measurements.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"13 7","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025EA004778","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616293","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 Free and Open-Source Tool for Spectral Unit Mapping Through Clustering of NIR Hyperspectral Images on Mars","authors":"Beatrice Baschetti,&nbsp;Mario D'Amore,&nbsp;Cristian Carli,&nbsp;Matteo Massironi,&nbsp;Francesca Altieri","doi":"10.1029/2026EA005053","DOIUrl":"https://doi.org/10.1029/2026EA005053","url":null,"abstract":"<p>Spectral and hyperspectral data provide essential information on the surface composition of rocky planetary bodies, through which we can reconstruct and understand their origin and geologic evolution. In this field, applications of advanced tools, such as machine learning-based algorithms, are steadily increasing. This study presents a newly developed, open-source, Python-based tool and workflow for the identification and mapping of spectral units with CRISM near-infrared (NIR) data using unsupervised machine-learning (clustering) techniques. Data pre-processing, dimensionality reduction, clustering, and visualization are integrated into an interactive user-friendly environment. The tool employs two different established clustering algorithms: K-means and Gaussian Mixture Models. Dimensionality reduction methods include the use of projection-based and manifold-based dimensionality reduction techniques, respectively Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP). We showcase the abilities of the tool on a set of selected CRISM images. We identify multiple spectral units, from which we retrieve spectral signatures ranging from mafic minerals, with broad absorption bands, to Fe/Mg phyllosilicates and sulfates, which exhibit multiple narrow absorption features. Results highlight how clustering methods can support and enhance the findings derived from classical analysis of spectral parameters, for instance, by highlighting subtle spectral variations. Moreover, with minor tuning and modifications, the tool can be applied to different spectral ranges as well as to spectral or hyperspectral data from other planetary surfaces.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"13 7","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2026EA005053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148754089","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":"Structural and Process Drivers of Spring Surface Albedo Feedback Uncertainty and Its Future Evolution Across Northern Hemisphere Extratropical Lands","authors":"Linfei Yu,&nbsp;Guoyong Leng","doi":"10.1029/2026EA005209","DOIUrl":"https://doi.org/10.1029/2026EA005209","url":null,"abstract":"<p>Surface albedo feedback (SAF) strongly amplifies spring warming across Northern Hemisphere extratropical land (&gt;30°N), yet Earth system models (ESMs) show substantial inter-model spread. Here we quantify spring (March–May) mean SAF and decompose its uncertainty into snow-cover dynamics (SNC) and temperature-driven snow albedo modulation (TEM) components across 34 ESMs from Coupled Model Intercomparison Project Phase 6 (CMIP6). Historical (1985–2014) multi-model mean SAF is 1.68 W m⁻² K⁻¹ (range 0.93–2.69 W m⁻² K⁻¹). In late-century (2070–2099), SAF shows a slight decline, with multi-model means ranging from 1.51 W m⁻² K⁻¹ to 1.62 W m⁻² K⁻¹ and inter-model spreads of 0.94–2.82 W m⁻² K⁻¹ across the four SSP scenarios. Relative uncertainty, quantified by the coefficient of variation, stays remarkably stable at 26.8%–30.6%. Spatially, high-latitude persistent snow regions exhibit the strongest SAF and uncertainty, where TEM dominates (contributing 59.6%–62.0%). In contrast, transitional mid-latitude zones are mainly controlled by SNC (contributing 40%–60%). Random Forest and SHAP (SHapley Additive exPlanations) analyses reveal that SNC uncertainty arises primarily from near-surface air temperature and snow structural variability, whereas TEM uncertainty is driven by snow depth, black carbon wet/dry deposition, and surface energy processes. Under high-emission scenarios, thermodynamic forcing and snow depth variability increasingly dominate both components, producing pronounced nonlinear responses and a regime shift in uncertainty sources. These findings demonstrate that spring SAF uncertainty is process-dependent, spatially heterogeneous, and dynamically reorganized by warming intensity. Improving snow optical parameterization and aerosol–snow interactions is therefore critical to reduce uncertainty in extratropical climate projections.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"13 7","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2026EA005209","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615812","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":"Machine Learning-Based Swath Bias Correction for OCO-3 Snapshot Area Mapping Mode Observations","authors":"Steffen Mauceri,&nbsp;Robert R. Nelson,&nbsp;Christopher W. O’Dell,&nbsp;Thomas E. Taylor,&nbsp;Matthäus Kiel,&nbsp;Steven Massie,&nbsp;Vivienne H. Payne,&nbsp;Abhishek Chatterjee","doi":"10.1029/2025EA004853","DOIUrl":"https://doi.org/10.1029/2025EA004853","url":null,"abstract":"<p>Space-based monitoring of urban carbon dioxide (CO₂) emissions is essential for climate policy, but systematic measurement biases can compromise emission quantification accuracy. NASA's Orbiting Carbon Observatory-3 (OCO-3) provides unique Snapshot Area Mapping (SAM) mode observations that can capture city-scale CO₂ plumes in single overpasses, yet some SAMs exhibit swath-based XCO₂ biases of 1–3 ppm that can mask or artificially enhance true emission signals. Here, we develop a machine learning framework to identify and correct these artifacts while preserving genuine atmospheric CO₂ variations. We trained a random forest classifier on 1,723 manually labeled SAMs spanning August 2019 to February 2025 to distinguish scenes requiring bias correction from those with legitimate spatial gradients in XCO₂. The model uses five key diagnostic features identified through systematic feature selection: inter-swath XCO₂ discontinuity, scene homogeneity, coarse- and fine-mode aerosol optical depths, and spectral alignment corrections. A median-alignment algorithm then removes detected biases by adjusting swath-level offsets while conserving total column CO₂. The classifier achieves a precision and recall of 79% and 63%, respectively, prioritizing the avoidance of false corrections that could otherwise remove true atmospheric signals. Applied to the complete OCO-3 SAM record, consisting of 19,839 unique SAMs, we estimate that approximately 9% have an identifiable swath bias. This work addresses a critical OCO-3 data quality issue that has hindered the operational use of OCO-3 SAMs for urban fossil fuel emissions monitoring. The proposed correction framework enhances OCO-3 data reliability for use in updating emission inventories and provides methodological insights for future greenhouse gas satellite missions.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"13 7","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025EA004853","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615743","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":"Antarctic Snowfall Patterns and Trends: A Regional Analysis Across Multiple Data Sets","authors":"Valerie Song,&nbsp;Yuekui Yang,&nbsp;Surendra Bhatta","doi":"10.1029/2025EA004834","DOIUrl":"https://doi.org/10.1029/2025EA004834","url":null,"abstract":"<p>Snowfall is the dominant positive component of the Antarctic surface mass balance (SMB) and a key factor regulating global sea level. However, substantial uncertainty remains in its spatial distribution and long-term variability due to sparse in situ observations and retrieval challenges over polar ice sheets. This study evaluates Antarctic snowfall patterns and trends using multiple data sets, including satellite-based products (Global Precipitation Climatology Project (GPCP) V3.3, IMERG V7), atmospheric reanalyzes (MERRA-2, ERA5), and CloudSat radar retrievals as an observational benchmark. To better examine spatial heterogeneity and regional variability, the Antarctic domain is subdivided into six regions: East and West Antarctica, coastal and inland zones, the high plateau (elevation ≥2,250 m), and lowland areas (&lt;2,250 m). CloudSat comparisons show that GPCP, MERRA-2, and ERA5 reproduce the large-scale spatial and seasonal snowfall patterns, whereas IMERG severely underestimates accumulation. During 2000–2024, GPCP and ERA5 both exhibit statistically significant positive snowfall trends, while MERRA-2 shows no statistically significant change. Regional and grid-point analyses reveal coherent positive trends across East Antarctica, and localized coastal regions. Overall, the results indicate a modest but spatially consistent increase in Antarctic snowfall during 2000–2024, in line with enhanced atmospheric moisture transport under a warming climate, with implications for understanding the evolving Antarctic SMB and its contribution to sea-level change.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"13 7","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025EA004834","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615744","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":"Strategic Micro-Awards Train the Next Generation and Grow a Cross-Disciplinary Scientific Community: The AGeS Program Example","authors":"R. M. Flowers,&nbsp;J. R. Arrowsmith","doi":"10.1029/2025EA004884","DOIUrl":"https://doi.org/10.1029/2025EA004884","url":null,"abstract":"<p>Strategic micro-grant programs have tremendous scientific and educational value. Such programs are cost-effective and can be leveraged to support student training and incentivize new cross-disciplinary science. Over the last 12 years, the AGeS (Advancing Geochronology Science, Spaces, and Systems) initiative developed three proposal-driven micro-award programs ($8-$18k awards) to better train the future scientific workforce and build a web of new collaborative relationships across the Earth sciences. AGeS grew to include 72 geochronology labs and &gt;120 geochronologists, ran 12 proposal cycles, saw 540 submitted proposals, made 169 awards, distributed &gt;$1M in funding to 140 graduate students for collaborative research in labs, generated numerous publications from new scientific partnerships, held dozens of steering and review committee meetings, ran four workshops, and involved hundreds across the Earth sciences in research, training, review, and governance activities. The AGeS program was terminated in April 2025 during the U.S. federal transition, when the National Science Foundation (NSF) rescinded support that had been allocated for 100 more AGeS micro-awards. Initiatives like AGeS are crucial for training and building the networks of the next generation of scientists, growing a scientific community that can work together across traditionally siloed fields. A cross-disciplinary and highly-trained community of Earth scientists is vital for solving the most pressing Earth science problems of our time, from critical mineral needs to geohazards to climate change.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"13 7","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025EA004884","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615479","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":"Topographic and Geological Factors Affecting Martian Transverse Aeolian Ridges: A Multi-Source Remote Sensing Analysis of Morphology and Local Sediment Provenance","authors":"Jie Zhang,&nbsp;Sicong Liu,&nbsp;Xiaohua Tong,&nbsp;Paolo Gamba,&nbsp;Huan Xie,&nbsp;Yonggang Xiong,&nbsp;Jiang Long,&nbsp;Kaiyue Luo,&nbsp;Kecheng Du","doi":"10.1029/2026EA005080","DOIUrl":"https://doi.org/10.1029/2026EA005080","url":null,"abstract":"<p>Transverse aeolian ridges (TARs) on Mars exhibit distinct characteristics relative to other aeolian features, yet their formation and evolutionary mechanisms remain unclear. A comprehensive investigation of TAR's distribution, morphology, composition, and environmental influences is critical for advancing our understanding of their development and implications for Martian climatic history. This research leverages multi-source orbital remote sensing data to explore TARs across various Martian regions and quantify the hierarchical controls of regional environment on TAR characteristics. High-resolution orbital images were employed to extract and quantify TAR parameters (area ratio, width, and length) for 33,946 identified TARs across six study areas. Results demonstrated a strong positive correlation (<i>r</i> &gt; 0.9) between TAR area ratio and elevation/roughness, with a weaker correlation (<i>r</i> &lt; 0.5) to slope, particularly when the area ratio exceeds 0.6, indicating that steep slopes act as a limiting threshold that inhibits TAR clustering. Morphologically, TAR width exhibits stronger linearity with topography (<i>r</i> &gt; 0.759) than length (<i>r</i> &gt; 0.588); the correlation for length diminishes notably as length increases, suggesting that topographic obstacles (roughness) physically fragment TAR continuity. Spectral analyses, validated by laboratory standard spectra (RELAB), identify diverse minerals (including olivine, hematite, and chlorides) that match local bedrock compositions. These findings confirm the predominantly local provenance of TAR sediments and highlight that regional variations in TAR characteristics are intrinsically driven by distinct geological evolution histories.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"13 7","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2026EA005080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148753716","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":"Howard University Beltsville Campus Ozonesonde Record: Data Homogenization and Uncertainties","authors":"Joshua A. D. Richards,&nbsp;Ryan M. Stauffer,&nbsp;Debra E. Kollonige,&nbsp;Anne M. Thompson,&nbsp;Belay Demoz,&nbsp;Ricardo K. Sakai,&nbsp;Adrian Flores","doi":"10.1029/2026EA005120","DOIUrl":"https://doi.org/10.1029/2026EA005120","url":null,"abstract":"<p>Ensuring the quality and consistency of multidecadal electrochemical concentration cell (ECC) ozonesonde records is essential because changes in sonde instrumentation, sensing solutions, operating procedures, and data-processing methods can introduce biases that compromise their suitability for trend analyses and model/satellite evaluation. Following best practices outlined by global ozonesonde experts through the Assessment of Standard Operating Procedures for Ozonesondes 2.0 (ASOPOS 2.0), we summarize the “homogenization” of the Howard University Beltsville Campus (HUBC; 39.054°N, 76.877°W, 53 m elevation) ozonesonde data set from 2006 to 2022. The HUBC station lies in an urbanized Mid-Atlantic region north of Washington, DC, serving as a prototype for most of the US east coast where mountains, megacities, the coastal plain, and large bodies of water yield complex transport patterns and challenges for model and satellite ozone estimations. We present an overview of the data reprocessing methods, their application to the HUBC time-series, and evaluate the homogenized HUBC data set relative to the original records. Total column ozone (TCO) from the reprocessed record, with uncertainties, is compared to TCO from satellite and a nearby Brewer spectrometer. The results of the homogenization: (a) sonde total column ozone increases by up to +3%; (b) stratospheric negative biases in the Aura MLS-sonde comparison improve from a mean of −3.2% before homogenization to −1.8%; (c) Total column ozone comparisons of both satellite-sonde and Brewer-sonde display similar improved percent differences with a decreased variance and reveal satellite sensitivity issues in capturing lower-tropospheric measurements; (d) sonde ozone uncertainties fall below 7% throughout profile, regardless of season.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"13 7","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2026EA005120","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148754252","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":"Sensitivity of Physical Schemes in WRF Simulations on the Track and Intensity of Landfalling Tropical Cyclones in East China","authors":"Yuemin Fan,&nbsp;Zhenshou Yu,&nbsp;Jianing Feng","doi":"10.1029/2026EA005095","DOIUrl":"https://doi.org/10.1029/2026EA005095","url":null,"abstract":"<p>The selection of appropriate physical parameterization schemes (PPSs) is crucial for improving the simulation accuracy of numerical weather prediction models. This study evaluates the sensitivity of landfalling Tropical Cyclones (TCs), to PPSs in the Weather Research and Forecasting model. We are focusing on the TCs of Lekima (2019), Hagupit (2020), and In-Fa (2021). For each of the three TC cases, 42 sensitivity experiments are conducted, resulting in a total of 126 simulations across three categories, which are planetary boundary layer parameterization scheme (PBLPS), cumulus convection parameterization scheme (CCPS), and cloud microphysics parameterization scheme (CMPS). Among the three categories, the PBLPS category achieves the best simulation on TC intensity, including landfall intensity. Both of TC track and intensity are the most sensitive to CMPS, and CMPS impacts TC track more during the post-landfall stage than pre-landfall stage. TC track is insensitive to PBLPS, especially during the post-landfall stage, because of the influence of large-scale forcing. PBLPS category has the smallest average track error. Furthermore, both landfall position and intensity have local sensitive to PBLPS. On average within the categories, the CMPS category shows the best performance in the simulation of TCs landfall position, while the PBLPS category has the minimum error in the simulation of TCs landfall intensity. Finally, the optimal schemes for TC track and intensity can respectively provide reference for the simulation of landfalling TCs in the future.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"13 7","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2026EA005095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148467242","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}