Search Results (243)

Mesoscale eddies play a key role in oceanic transport, yet their characterization in marginal seas like the Gulf of California remains challenging due to complex coastlines and bathymetry that hinder conventional detection methods. This study addresses this gap by presenting a robust hybrid framework—integrating dynamical (Okubo–Weiss), velocity geometry (Nencioli), and closed-contour (Chelton) criteria—applied to the high-resolution (${0.01}^{\circ }$) Neural Ocean Surface Topography (NeurOST) altimetry product (2010–2024). Temporal continuity is ensured through a cost-based tracking algorithm optimized to tolerate observational gaps and track quasi-stationary features. This census, representing the first systematic, high-resolution sea surface height anomaly (SSHA)-based characterization for this region, identified 344 persistent trajectories (≥14 days) and revealed a fundamental dichotomy in the Gulf’s dynamics: a transient, tidally dominated regime in the north (dominated by short-lived features) contrasting sharply with a persistent, topographically trapped regime in the south. Focusing on the long-lived population (lifetimes $>30$ days), our analysis confirms that multi-year, quasi-stationary cyclonic eddies are trapped in the southern basins, while a subset of energetic tracks exhibits coherent poleward propagation consistent with advection by the Mexican Coastal Current. Cyclonic structures dominate the ten longest-lived tracks (90%) and include two events with lifetimes confirmed to exceed 500 days. We also identify a robust seasonal decoupling between SSHA and sea surface temperature anomalies (SSTA) in spring, when surface heating masks the thermal signature of cyclones. This census, which documents multi-year structures and distinguishes the two regional regimes, establishes a new baseline for quantifying mesoscale transport and serves as a transferable framework for the new generation of satellite altimetry observations (i.e., the Surface Water and Ocean Topography, SWOT, mission). Full article

Severe convective weather is marked by abrupt onset, rapid evolution, and substantial destructive potential, posing major threats to economic activities and human safety. To address this challenge, this study proposes MRKAN, a multi-parameter prediction algorithm for dual-polarization radar that integrates Mamba, radial basis functions (RBFs), and the Kolmogorov–Arnold Network (KAN). The method predicts radar reflectivity, differential reflectivity, and the specific differential phase, enabling a refined depiction of the dynamic structure of severe convective systems. MRKAN incorporates four key innovations. First, a Cross-Scan Mamba module is designed to enhance global spatiotemporal dependencies through point-wise modeling across multiple complementary scans. Second, a Multi-Order KAN module is developed that employs multi-order $\beta$-spline functions to overcome the linear limitations of convolution kernels and to achieve high-order representations of nonlinear local features. Third, a Gaussian and Inverse Multiquadratic RBF module is constructed to extract mesoscale features using a combination of Gaussian radial basis functions and Inverse Multiquadratic radial basis functions. Finally, a Multi-Scale Feature Fusion module is designed to integrate global, local, and mesoscale information, thereby enhancing multi-scale adaptive modeling capability. Experimental results show that MRKAN significantly outperforms mainstream methods across multiple key metrics and yields a more accurate depiction of the spatiotemporal evolution of severe convective weather. Full article

Accurate prediction of the effective thermal conductivity (ETC) of nuclear fuels is essential for optimizing fuel performance and ensuring reactor safety. However, the experimental determination of ETC is often limited by cost and complexity, while high-fidelity simulations are computationally intensive. This study presents a novel hybrid framework that integrates experimental data, validated mesoscale finite element simulations, and machine-learning (ML) models to efficiently and accurately estimate ETC for advanced uranium-based nuclear fuels. The framework was demonstrated on three fuel systems: UO₂-BeO composites, UO₂-Mo composites, and U-10Zr metallic alloys. Mesoscale simulations incorporating microstructural features and interfacial thermal resistance were validated against experimental data, producing synthetic datasets for training and testing ML algorithms. Among the three regression methods evaluated, namely Bayesian Ridge, Random Forest, and Multi-Polynomial Regression, the latter showed the highest accuracy, with prediction errors below 10% across all fuel types. The selected multi-polynomial model was subsequently used to predict ETC over extended temperature and composition ranges, offering high computational efficiency and analytical convenience. The results closely matched those from the validated simulations, confirming the robustness of the model. This integrated approach not only reduces reliance on costly experiments and long simulation times but also provides an analytical form suitable for embedding in engineering-scale fuel performance codes. The framework represents a scalable and generalizable tool for thermal property prediction in nuclear materials. Full article

Historic waterfront streets are not only an important component of urban public spaces but also highlight the distinctive features and historical contexts of the city. High-quality streetscape visual perception plays a crucial role in advancing the cultural, social, environmental, and economic sustainability of the urban street space. This study was initiated to construct a multi-dimension and multi-scale comprehensive evaluation framework to assess the visual quality of waterfront streets, taking “Water City” Liaocheng as a typical case. Technical methods of semantic segmentation, sDNA (Spatial Design Network Analysis), GIS (Geographic Information System), and statistical analysis were utilized. Following the extraction and classification of street space elements, a multi-dimensional evaluation index system of natural coordination, artificial comfort, and historical culture for the visual assessment was established. Space syntax was performed on waterfront streets by sDNA to quantify macro-level scale spatial structure and meso-level scale pedestrian accessibility. The results of micro-scale visual perception, meso-scale behavioral walkability, and macro-scale spatial structure, were integrated to construct a multi-scale diagnostic framework for eight classifications. This framework provides a scientific basis to put forwards the refined and sustainable optimization strategies for historic waterfront streets. Full article

Proton exchange membrane fuel cells (PEMFC) are recognized as promising next-generation energy technology. Yet, their performance is critically limited by inefficient gas transport and water management in conventional flow channels. Current rectangular gas channels (GC) restrict reactive gas penetration into the gas diffusion layer (GDL) due to insufficient longitudinal convection. At the same time, the complex multiphase interactions at the mesoscale pose challenges for numerical modeling. To address these limitations, this study proposes a novel cathode channel design featuring laterally contracted fin-shaped barrier blocks and develops a mesoscopic multiphase coupled transport model using the lattice Boltzmann method combined with the volume-of-fluid approach (LBM-VOF). Through systematic investigation of multiphase flow interactions across channel geometries and GDL surface wettability effects, we demonstrate that the optimized barrier structure induces bidirectional forced convection, enhancing oxygen transport compared to linear channels. Compared with the traditional straight channel, the optimized composite channel achieves a 60.9% increase in average droplet transport velocity and a 56.9% longer droplet displacement distance, while reducing the GDL surface water saturation by 24.8% under the same inlet conditions. These findings provide critical insights into channel structure optimization for high-efficiency PEMFC, offering a validated numerical framework for multiphysics-coupled fuel cell simulations. Full article

Observations of building-induced airflow disturbances arising from the new terminal building at the Hong Kong International Airport (HKIA) are documented in this paper. Two case studies are conducted: one involving turbulent flow downstream of the building and another involving a coherent “building-induced wave”. To capture these phenomena under realistic atmospheric forcing, we employ a coupled mesoscale–computational fluid dynamics modelling system. This approach integrates mesoscale boundary-layer conditions with building-resolving simulations for real airport disturbance analysis. The main features of the actual observation are largely captured by the simulations. As such, the simulated data are studied to find out the reason for the difference in the airflow behavior. The difference could be related to the stability of the “background” atmospheric boundary layer. This stability is influenced by a number of complicated factors, including the background mesoscale atmospheric stability, Foehn effect of the terrain, and solar heating of the sea/land surface. The study further discusses potential implications for runway operations using aviation-relevant indicators, including the 7-knot criterion and turbulence intensity. Full article

Local Climate Zones (LCZs) have become a foundational framework for urban climate modeling, yet their use across model families has not been systematically evaluated. Crucially, the LCZ framework itself has served as a developmental basis, revealing the progression of urban canopy parameterizations (UCP) from early models to the diverse model families currently in use. This evolution is exemplified by systems like the Weather Research and Forecasting (WRF) model, where the application of LCZ has fundamentally shifted from an experimental add-on to a basic, built-in feature of its urban-modeling capabilities. This review synthesizes a decade of LCZ-based studies to clarify how LCZ improves surface representation, enhances comparability, and supports multiscale modeling workflows. It provides a comprehensive overview of peer-reviewed work up to the end of 2024, offering a baseline for understanding the field’s rapid recent growth. Using a structured evidence-mapping approach, we categorize applications into three maturity stages: testing and measurement, operational and planning-oriented applications, and expansions beyond urban climate to chemistry, hydrology, and Earth-system modeling. The assessment covers various iterations of mesoscale systems (WRF, SURFEX/TEB, COSMO), local-scale climatologies (MUKLIMO-3, UrbClim), microscale models (ENVI-met, CFD), and supporting tools including SUEWS, SOLWEIG, RayMan, VCWG, and CESM-CLMU. Results show clear divisions of labor: WRF and SURFEX/TEB anchor process-rich regional simulations; MUKLIMO-3 and UrbClim offer computationally efficient long-horizon or multi-city assessments; ENVI-met and CFD provide design-scale insight when parameterized with LCZ archetypes. Across all families, model skill is strongly constrained by LCZ data quality and by inconsistencies in LCZ to UCP translation. We conclude that advancing LCZ-based urban climate modeling will depend on improved LCZ products, standardized parameter libraries, and formalized cross-scale model couplings that allow existing tools to interoperate more reliably under growing urban-climate pressures. Full article

Coastal Trapped Waves (CTWs) represent an important class of mesoscale fluctuations in nearshore shelf regions and play a crucial role in modulating coastal circulation. The South China Sea (SCS), the largest semi-enclosed marginal sea in the western Pacific Ocean, features a continental shelf approximately 200 km wide. During summer, the SCS is frequently impacted by typhoons, which often trigger significant CTWs. This study investigates the characteristics of CTWs generated by Typhoon ‘Soudelor’ (No. 1513) in the northwestern SCS, based on current observations and numerical model simulations. Under the influence of Soudelor, CTWs characterized by elevated water levels nearshore and depressed water levels offshore were initially generated by wind-induced Ekman transport in the Taiwan Strait. These waves subsequently propagated southwestward along the coastline with phase velocities ranging from 7.2 to 18.3 m/s. Model results indicate that the CTW influenced current fields up to 160 km offshore, with a maximum CTW-induced current velocity exceeding 0.7 m/s. The vertical structure of the CTW-induced current field exhibited a barotropic characteristic. The influence of CTWs on current fields diminished with propagation distance, accompanied by a reduction in the induced current velocity. This attenuation was particularly pronounced between Xiamen (XM) and Shanwei (SW). Sensitivity experiments further revealed that the slowed propagation phase velocity of CTWs between XM and SW was attributable to strong reflection, scattering, and nonlinear effects caused by the abrupt topographic changes of the Taiwan Bank. Full article

This study investigates the impact of steel slag coarse aggregate (SSA) particle size on the macroscopic mechanical properties of concrete. Considering that the macroscopic behavior of concrete is significantly influenced by its mesoscale structural characteristics, and that coarse aggregate particle size is a key factor determining these features, uniaxial compression experiments together with mesoscale simulations were carried out to develop a model linking the mesoscale structure to the mechanical response of steel slag coarse aggregate concrete (SSAC). The results show that SSAC exhibits a failure pattern comparable to that of natural aggregate concrete (NAC), but its stress–strain curve exhibits a steeper ascending branch and higher peak stress. With the increasing SSA replacement ratio, the peak stress continuously increases; within the same particle size range, the elastic modulus shows an initial increase followed by a subsequent decrease, reaching its maximum at a 50% replacement ratio. Expanding the particle size range changes the peak strain response from approximately linear to rapidly increasing; smaller particle sizes result in a gentler post-peak drop, whereas higher replacement ratios produce a steeper decline. The mesoscale model further shows that for SSA particle sizes of 5–20 mm, 5–15 mm, and 5–10 mm, the cohesive strength of the interfacial transition zone (ITZ) increases by 75%, 106%, and 92%, respectively, compared with NAC. Increasing the coarse aggregate volume fraction further enhances the ITZ strength improvement. This study offers valuable insights for improving the mixture design and performance of SSAC. Full article

Global Navigation Satellite System Reflectometry (GNSS-R) provides all-weather, high-resolution ocean wind speed monitoring that offers additional benefits for forecasting tropical cyclones and severe weather events. However, existing GNSS-R wind retrieval models often lack interpretability and suffer accuracy degradation during high wind conditions. To address these limitations, we leverage a mathematical equivalence between Transformers and graph neural networks (GNNs) on complete graphs, which provides a physically grounded interpretation of self-attention as spatiotemporal influence propagation in GNSS-R data. In our model, each GNSS-R footprint is treated as a graph node whose multi-head self-attention weights quantify localized interactions across space and time. This aligns physical influence propagation with the computational efficiency of GPU-accelerated Transformers. Multi-head attention disentangles processes at multiple scales—capturing local (25–100 km), mesoscale (100 km–500 km), and synoptic (>500 km) circulation patterns. When applied to Level 1 Version 3.2 data (2023–2024) from four Asian sea regions, our Transformer–GNN achieves an overall wind speed RMSE reduction of 32% (to 1.35 m s⁻¹ from 1.98 m s⁻¹) and substantial gains in high-wind regimes (winds >25 m s⁻¹: 3.2 m s⁻¹ RMSE). The model is trained on ERA5 reanalysis 10 m equivalent-neutral wind fields, which serve as the primary reference dataset, with independent validation performed against Stepped Frequency Microwave Radiometer (SFMR) aircraft observations during tropical cyclone events and moored buoy measurements where spatiotemporally coincident data are available. Interpretability analysis with SHAP reveals condition-dependent feature attributions and suggests coupling mechanisms between ocean surface currents and wind fields. These results demonstrate that our model advances both predictive accuracy and interpretability in GNSS-R wind retrieval. With operationally viable inference performance, our framework offers a promising approach toward interpretable, physics-aware Earth system AI applications. Full article

Marine low clouds have a strong impact on Earth’s system but remain a major source of uncertainty in anthropogenic radiative forcing simulated by general circulation models. This uncertainty arises from incomplete understanding of the many processes controlling their evolution and interactions. A key feature of these clouds is their diverse mesoscale morphologies, which are closely tied to their microphysical and radiative properties but remain difficult to characterize with satellite retrievals and numerical models. Here, we develop and apply a vision–language model (VLM) to classify marine low cloud morphologies using two independent datasets based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery: (1) mesoscale cellular convection types of sugar, gravel, fish, and flower (SGFF; 8800 total samples) and (2) marine stratocumulus (Sc) types of stratus, closed cells, open cells, and other cells (260 total samples). By conditioning frozen image encoders on descriptive prompts, the VLM leverages multimodal priors learned from large-scale image–text training, making it less sensitive to limited sample size. Results show that the k-fold cross-validation of VLM achieves an overall accuracy of 0.84 for SGFF, comparable to prior deep learning benchmarks for the same cloud types, and retains robust performance under the reduction in SGFF training size. For the Sc dataset, the VLM attains 0.86 accuracy, whereas the image-only model is unreliable under such a limited training set. These findings highlight the potential of VLMs as efficient and accurate tools for cloud classification under very low samples, offering new opportunities for satellite remote sensing and climate model evaluation. Full article

Mesoscale convective systems (MCSs) play a pivotal role in the occurrence of severe weather phenomena, with convective cells constituting their fundamental elements. The precise identification of these cells from satellite imagery is crucial yet presents significant challenges, including issues related to merging errors and sensitivity to threshold parameters. This study introduces a novel detection algorithm for convective cells that leverages H-maxima transformation and incorporates multichannel data from the FY-2F satellite. The proposed method utilizes H-maxima transformation to identify seed points while maintaining the integrity of core structural features, followed by a novel neighborhood labeling method, region growing and adaptive merging criteria to effectively differentiate adjacent convective cells. The neighborhood labeling method improves the accuracy of seed clustering and avoids “over-clustering” or “under-clustering” issues of traditional neighborhood criteria. When compared to established methods such as RDT, ETITAN, and SA, the algorithm demonstrates superior performance, attaining a Probability of Detection (POD) of 0.87, a False Alarm Ratio (FAR) of 0.21, and a Critical Success Index (CSI) of 0.71. These results underscore the algorithm’s efficacy in elucidating the internal structures of convective complexes and mitigating false merging errors. Full article

Under harsh sea states, the dynamic characteristics of ocean mesoscale eddies (OMEs) become significantly more complex, posing substantial challenges to their accurate detection and identification. In this study, we propose an artificial intelligence detection method for OMEs based on the domain adaptation technique to accurately perform pixel-level segmentation and ensure its effectiveness under harsh sea states. The proposed model (LCNN) utilizes large kernel convolution to increase the model’s receptive field and deeply extract eddy features. To deal with the pronounced cross-domain distribution shifts induced by harsh sea states, an adversarial learning framework (ADF) is introduced into LCNN to enforce feature alignment between the source (normal sea states) and target (harsh sea states) domains, which can also significantly improve the segmentation performance in our constructed dataset. The proposed model achieves an accuracy, precision, and Mean Intersection over Union of 1.5%, 6.0%, and 7.2%, respectively, outperforming the existing state-of-the-art technologies. Full article

Low-level wind shear (LLWS) is a critical issue in aviation meteorology, posing serious risks to flight safety—especially at plateau airports with high elevation and complex terrain. This study investigates a convective wind shear event at Xining Airport on 29 May 2021. Multi-source observations—including the Doppler Wind Lidar (DWL), the Doppler weather radar (DWR), reanalysis datasets, and automated weather observation systems (AWOS)—were integrated to examine the event’s fine-scale structure and temporal evolution. High-resolution simulations were conducted using the Large Eddy Simulation (LES) framework within the Weather Research and Forecasting (WRF) model. Results indicate that the formation of this wind shear was jointly triggered by convective downdrafts and the gust front. A northwesterly flow with peak wind speeds of 18 m/s intruded eastward across the runway, generating multiple radial velocity couplets on the eastern side, closely associated with mesoscale convergence and divergence. A vertical shear layer developed around 700 m above ground level, and the critical wind shear during aircraft go-around was linked to two convergence zones east of the runway. The event lasted about 30 min, producing abrupt changes in wind direction and vertical velocity, potentially causing flight path deviation and landing offset. Analysis of horizontal, vertical, and glide-path wind fields reveals the spatiotemporal evolution of the wind shear and its impact on aviation safety. The WRF-LES accurately captured key features such as wind shifts, speed surges, and vertical disturbances, with strong agreement to observations. The integration of multi-source observations with WRF-LES improves the accuracy and timeliness of wind shear detection and warning, providing valuable scientific support for enhancing safety at plateau airports. Full article

This study presents a high-fidelity image acquisition method for asphalt film structure to address the challenge of capturing mesoscale structures, especially fine mineral filler and asphalt mastic. The method is particularly applied to the analysis of the mortar structure in reclaimed asphalt pavement (RAP) mixtures. A digital camera combined with image stacking and texture suppression techniques was used to develop a reproducible imaging protocol. The resulting sub-pixel images significantly improved clarity and structural integrity, particularly for particles smaller than 0.075 mm. U-Net-based segmentation identified 588,513 aggregate particles—34 times more than in standard images (17,428). Among them, 95% were smaller than 0.075 mm compared to just 45% in standard images. Furthermore, segmentation accuracy reached 99.3% in high-resolution images, surpassing the 98.1% in standard images. These results confirm the method’s strong capability to preserve microscale features and enhance fine particle recognition, making it more effective than conventional imaging approaches. This study bridges physical and digital workflows in asphalt material analysis, offering a scalable, reproducible pipeline for fine-structure identification. The methodology provides foundational support for data-driven pavement modeling, material optimization, and future integration into digital twin frameworks for intelligent infrastructure systems. Full article