Search Results (53)

Tropical cyclones (TCs) induce pronounced sea surface temperature (SST) cooling, which strongly influences their intensity. Accurate prediction of TC intensity is particularly important in coastal regions where landfall occurs. While SST cooling has been extensively studied in the open ocean, its characteristics in coastal seas remain less understood. Using satellite and reanalysis data from 2004 to 2021, this study systematically characterizes SST cooling in China’s coastal seas—the Yellow Sea, East China Sea, Taiwan Strait, and northern South China Sea—and compares the cooling with adjacent offshore regions. Composite analyses of about 6300 TC track points reveal that coastal SST cooling shows significant differences relative to their offshore cooling. Regionally, the Yellow Sea exhibits significantly stronger coastal cooling (−2.5 °C vs. −1.8 °C), whereas the Taiwan Strait shows weaker coastal cooling. Further analyses using a statistical subsampling method reveal that coastal–offshore cooling differences result from the combined effects of TC attributes and pre-TC oceanic conditions, with temperature stratification exerting the dominant control. Furthermore, an increasing trend in coastal cooling is linked to enhanced temperature stratification. These findings highlight the critical role of pre-TC temperature structure in modulating coastal SST responses, with implications for improving intensity forecasts and risk assessments. Full article

Accurate retrieval of three-dimensional (3D) typhoon wind fields over the open ocean remains a critical challenge due to observational gaps and physical inconsistencies in existing methods. Based on multi-channel data from the Himawari-8/9 geostationary satellites, this study proposes a physics-informed deep learning framework for high-resolution 3D wind field reconstruction of open-ocean typhoons. A convolutional neural network was designed to establish an end-to-end mapping from 16-channel satellite imagery to the 3D wind field across 16 vertical levels. To enhance physical consistency, the continuity equation, enforcing mass conservation, was embedded as a strong constraint into the loss function. Four experimental scenarios were designed to evaluate the contributions of multi-channel data and physical constraints. Results demonstrate that the full model, integrating both visible/infrared channels and the physical constraint, achieved the best performance, with mean absolute errors of 2.73 m/s and 2.54 m/s for U- and V-wind components, respectively. This represents significant improvements over the baseline infrared-only model (29.6% for U, 21.6% for V), with notable error reductions in high-wind regions (>20 m/s). The approach effectively captures fine-scale structures like eyewalls and spiral rainbands while maintaining vertical physical coherence, offering a robust foundation for typhoon monitoring and reanalysis. Full article

Our understanding of the molecular mechanisms by which DNA meiotic recombination occurs has significantly increased in the past decades. A more representative molecular model has also undergone repeated revisions and upgrades with the continuous expansion of experimental data. Considering several apparent issues in the field, we intend to make necessary upgrades to previous models and reanalyze those data, exploring structural details and molecular mechanisms of DNA meiotic recombination. Eligible studies were identified from PubMed/Medline (up to June 2024). Key related publications and experimental data were retrieved from eligible studies, displaying five major issues. Meanwhile, the biophysical modeling method was used to establish an enlacement model. Then, the model was used to wholly reanalyze the collected data. An updated molecular model was supplemented. In the current model, a copy choice mechanism can initiate DNA meiotic recombination. The copy choice is based on a branched structure of DNA, which results from relative motion between homologous single strands. The reanalysis of previous experimental data based on this model can lead to new interpretations that can better address the discrepancies between previous experimental observations and theoretical models, including (1) the intertwinement model having embodied the particular characteristics of the SDSA model; (2) hDNA arising from JM resolution rather than being followed by a JM; (3) strand specificity of hDNA mismatch repair seeming to be an illusion and copy choice more likely to be the actual state; (4) parity in resolution patterns of a dHJ leading to parity of gene conversion; (5) the cooperation of multiple HJs readily generating a high correlation between gene conversion and crossover; and (6) transpositional recombination and site-specific recombination seeming to have a common pathway to meiotic recombination. The results indicate that both revisions and reanalysis are necessary. The novel interpretations would be critical to the understanding of the mechanisms of DNA recombination as well as its role in DNA repair. Additionally, the work could have implications for how the field views the importance of factors such as Spo11 or the mechanisms that drive meiotic pairing. Full article

Tropical cyclones (TCs) rank among the most destructive natural hazards globally, with core damaging potential originating from regions of intense wind shear and steep wind speed gradients within the eyewall and spiral rainbands. Accurately characterizing these fine-scale structural features is therefore critical for understanding TC intensity evolution, wind hazard distribution, and disaster mitigation. Recently, the deep learning-based downscaling methods have shown significant advantages in efficiently obtaining high-resolution wind field distributions. However, existing methods are mainly used to downscale general wind fields, and research on downscaling extreme wind field events remains limited. There are two main difficulties in downscaling TC wind fields. The first one is that high-quality datasets for TC wind fields are scarce; the other is that general deep learning frameworks lack the ability to capture the dynamic characteristics of TCs. Consequently, this study proposes a novel deep learning framework, CycloneWind, for downscaling TC surface wind fields: (1) a high-quality dataset is constructed by integrating Cyclobs satellite observations with ERA5 reanalysis data, incorporating auxiliary variables like low cloud cover, surface pressure, and top-of-atmosphere incident solar radiation; (2) we propose CycloneWind, a dynamically constrained Transformer-based architecture incorporating three wind field dynamical operators, along with a wind dynamics-constrained loss function formulated to enforce consistency in wind divergence and vorticity; (3) an Adaptive Dynamics-Guided Block (ADGB) is designed to explicitly encode TC rotational dynamics using wind shear detection and wind vortex diffusion operators; (4) Filtering Transformer Layers (FTLs) with high-frequency filtering operators are used for modeling wind field small-scale details. Experimental results demonstrate that CycloneWind successfully achieves an 8-fold spatial resolution reconstruction in TC regions. Compared to the best-performing baseline model, CycloneWind reduces the Root Mean Square Error (RMSE) for the U and V wind components by 9.6% and 4.9%, respectively. More significantly, it achieves substantial improvements of 23.0%, 22.6%, and 20.5% in key dynamical metrics such as divergence difference, vorticity difference, and direction cosine dissimilarity. Full article

Aircraft encounter complex ground and air scenarios during service, necessitating a comprehensive analysis of extensive global load cases during the design phase to ensure structural reliability and safety. While high-fidelity finite element analysis enables precise assessment of load case criticality, its prohibitive human and computational costs constrain aircraft iterative development. To overcome this challenge, this study proposes a Global Load Case Analysis (GLCA) system for identifying critical load cases across structural sections. The method is driven by aerodynamic load data and structural response data from coarse-grid models. First, it achieves a quantitative ranking of global load case criticality, providing engineers with a standardized severity metric. Second, based on defined criticality relationships, it identifies coverage, coupling, and differentiation patterns among load cases to establish criticality hierarchies. Finally, a novel 1DCNN architecture with specialized training strategies learns the GLCA system’s behavioral patterns, enabling accurate prediction of criticality for newly added load cases without computationally intensive reanalysis. The results demonstrate strong agreement between GLCA and high-fidelity model analyses: quantitative ranking achieves 95.98% average accuracy with complete identification of critical load cases. Predictions for new load cases yield coefficients of determination (R²) > 0.98 and 97.91% average criticality classification accuracy. Furthermore, GLCA operates 335 times more efficiently than high-fidelity finite element analysis. This approach effectively substitutes high-fidelity modeling during load development, reducing human effort and shortening aircraft design iteration cycles. Full article

Accurately and timely estimating three-dimensional ocean states is crucial for improving operational ocean forecasting capabilities. Although satellite observations provide valuable evolutionary information, they are confined to surface-level variables. While in situ observations can offer subsurface information, their spatiotemporal distribution is highly uneven, making it difficult to obtain complete three-dimensional ocean structures. This study developed an operational-oriented lightweight framework for three-dimensional ocean state reconstruction by integrating multi-source observations through a computationally efficient multivariate empirical orthogonal function (MEOF) method. The MEOF method can extract physically consistent multivariate ocean evolution modes from high-resolution reanalysis data. We utilized these modes to further integrate satellite remote sensing and buoy observation data, thereby establishing physical connections between the sea surface and subsurface. The framework was tested in the South China Sea, with optimal data integration schemes determined for different reconstruction variables. The experimental results demonstrate that the sea surface height (SSH) and sea surface temperature (SST) are the key factors determining the subsurface temperature reconstruction, while the sea surface salinity (SSS) plays a primary role in enhancing salinity estimation. Meanwhile, current fields are most effectively reconstructed using SSH alone. The evaluations show that the reconstruction results exhibited high consistency with independent Argo observations, outperforming traditional baseline methods and effectively capturing the vertical structure of ocean eddies. Additionally, the framework can easily integrate sparse in situ observations to further improve the reconstruction performance. The high computational efficiency and reasonable reconstruction results confirm the feasibility and reliability of this framework for operational applications. Full article

Kármán vortex streets are quintessential phenomena in fluid dynamics, manifested by the periodic shedding of vortices as airflow interacts with obstacles. The genesis and characteristics of these vortex structures are significantly influenced by various atmospheric parameters, including temperature, humidity, pressure, and wind velocities, which collectively dictate their formation conditions, spatial arrangement, and dynamic behavior. Although deep learning methodologies have advanced the automated detection of Kármán vortex streets in remote sensing imagery, existing approaches largely emphasize visual feature extraction without adequately incorporating critical atmospheric variables. To overcome this limitation, this study presents an innovative auxiliary network framework that integrates essential atmospheric physical parameters to bolster the detection performance of Kármán vortex streets. Utilizing reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF-ERA5), representative atmospheric features are extracted and subjected to feature permutation importance (PFI) analysis to quantitatively evaluate the influence of each parameter on the detection task. This analysis identifies five pivotal variables: geopotential, specific humidity, temperature, horizontal wind speed, and vertical air velocity, which are subsequently employed as inputs for the auxiliary task. Building upon the YOLOv8s object detection model, the proposed auxiliary network systematically examines the impact of various atmospheric variable combinations on detection efficacy. Experimental results demonstrate that the integration of horizontal wind speed and vertical air velocity achieves the highest detection metrics (precision of 0.838, recall of 0.797, mAP50 of 0.865, and mAP50-95 of 0.413) in precision-critical scenarios, outperforming traditional image-only detection method (precision of 0.745, recall of 0.745, mAP50 of 0.759, and mAP50-95 of 0.372). The optimized selection of atmospheric parameters markedly improves the detection metrics and reliability of Kármán vortex streets, underscoring the efficacy and practicality of the proposed methodological framework. This advancement paves the way for more robust automated analysis of atmospheric fluid dynamics phenomena. Full article

Dynamical processes during the different stages of evolution of tropical cyclones play crucial roles in their development and intensification, making them one of the most powerful natural forces on Earth. Given their classification as extreme atmospheric events resulting from multiple interacting factors, it is significant to study their dynamical behavior and the nonlinear effects generated by emerging structures during scales and intensity transitions, correlating them with the surrounding environment. This study investigates the extraordinary and record-breaking case of Tropical Cyclone Freddy (2023 Indian Ocean tropical season) from a purely dynamical perspective, examining the superposition of energetic structures at different spatio-temporal scales, by mainly considering thermal fluctuations over 12 days of its evolution. The tool used for this investigation is the Proper Orthogonal Decomposition (POD), in which a set of empirical basis functions is built up, retaining the maximum energetic content of the turbulent flow. The method is applied on a satellite imagery dataset acquired from the SEVIRI radiometer onboard the Meteosat Second Generation-8 (MSG-8) geostationary platform, from which the cloud-top temperature scalar field is remote sensed looking at the cloud’s associated system. For this application, considering Freddy’s very long life period and exceptionally wide path of evolution, reanalysis and tracking data archives are taken into account in order to create an appropriately dynamic spatial grid. Freddy’s eye is followed after its first shape formation with very high temporal resolution snapshots of the temperature field. The energy content in three different characteristic scale ranges is analyzed through the associated spatial and temporal component spectra, focusing both on the total period and on the transitions between different categories. The results of the analysis outline several interesting aspects of the dynamics of Freddy related to both its transitions stages and total period. The reconstructions of the temperature field point out that the most consistent vortexes are found in the outermost cyclonic regions and in proximity of the eyewall. Additionally, we find a significant consistency of the results of the investigation of the maximum intensity phase of Freddy’s life cycle, in the spatio-temporal characteristics of its dynamics, and in comparison with one analogous case study of the Faraji tropical cyclone. Full article

Satellite and reanalysis-based precipitation products have played a crucial role in addressing the challenges associated with limited ground-based observational data. These products are widely utilized in hydrometeorological research, particularly in data-scarce regions like the Qinghai–Tibetan Plateau (QTP). This study proposed an ensemble streamflow simulation method using remote sensing precipitation data as input. By employing a 1D Convolutional Neural Networks (1D CNN), streamflow simulations from multiple models are integrated and a Shapley Additive exPlanations (SHAP) interpretability analysis was conducted to examine the contributions of individual models on ensemble streamflow simulation. The method is demonstrated using GPM IMERG (Global Precipitation Measurement Integrated Multi-satellite Retrievals) remote sensing precipitation data for streamflow estimation in the upstream region of the Ganzi gauging station in the Yalong River basin of QTP for the period from 2010 to 2019. Streamflow simulations were carried out using models with diverse structures, including the physically based BTOPMC (Block-wise use of TOPMODEL) and two machine learning models, i.e., Random Forest (RF) and Long Short-Term Memory Neural Networks (LSTM). Furthermore, ensemble simulations were compared: the Simple Average Method (SAM), Weighted Average Method (WAM), and the proposed 1D CNN method. The results revealed that, for the hydrological simulation of each individual models, the Kling–Gupta Efficiency (KGE) values during the validation period were 0.66 for BTOPMC, 0.71 for RF, and 0.74 for LSTM. Among the ensemble approaches, the validation period KGE values for SAM, WAM, and the 1D CNN-based nonlinear method were 0.74, 0.73, and 0.82, respectively, indicating that the nonlinear 1D CNN approach achieved the highest accuracy. The SHAP-based interpretability analysis further demonstrated that RF made the most significant contribution to the ensemble simulation, while LSTM contributed the least. These findings highlight that the proposed 1D CNN ensemble simulation framework has great potential to improve streamflow estimations using remote sensing precipitation data as input and may provide new insight into how deep learning methods advance the application of remote sensing in hydrological research. Full article

As offshore wind turbines develop into deepwater operations, accurately quantifying the impact of stochastic excitation in complex sea environments on offshore wind turbines and conducting structural fatigue reliability analysis has become challenging. In this paper, based on long-term wind–wave reanalysis data from the South China Sea, a novel direct probability integral method (DPIM) is developed for the stochastic response and fatigue reliability analysis of the key components for the floating offshore wind turbine structures, under combined wind–wave excitation. A 5 MW floating offshore wind turbine is considered as the research object, and a comprehensive analysis of the wind turbine system is performed to assess the short-term fatigue damage at the tower base and blade root. The proposed method’s accuracy and efficiency are validated by comparing the results to those obtained from Monte Carlo simulations (MCS) and a subset simulation (SSM). Additionally, a sensitivity analysis is conducted to evaluate the impact of different environmental parameters on fatigue damage, providing valuable insights for the design and operation of FOWTs in varying sea conditions. Furthermore, the results indicate that the fatigue life of floating offshore wind turbine (FOWT) structures under combined wind–wave excitation meets the design requirements. Notably, the fatigue reliability of the wind turbine under aligned wind–wave conditions is lower compared to misaligned wind–wave conditions. Full article

Existing route-clustering methods often fail to identify abnormal sections or similarities between routes, mainly when working with large or long datasets. While sub-route clustering can detect regional patterns, it struggles to accurately capture the overall route structure. The present study proposes a new ship route-clustering method that enhances computational efficiency and noise recognition while addressing these limitations. We refined Automatic Identification System data via four data-cleaning processes and applied a statistical distance measurement to assess ship trajectory similarity. Dimensionality reduction was then used to facilitate clustering. The clustering of ship route similarities is non-parametric and can be applied to datasets not separated based on density to find clusters of various densities. Density-Based Spatial Clustering of Applications (DBSCA) applies to many research fields; using the DBSCA with Noise (DBSCAN) algorithm, we propose an improved DBSCAN algorithm that automatically determines the parameters Epsilon and MinPts. In this study, as a core ship route-clustering process, we propose a sub-route clustering process by setting the distance and density of data points to clear standards for re-analysis and completion. The proposed approach demonstrates markedly enhanced clustering performance, offering a more sophisticated and efficient basis for ship route decision-making. Full article

This study investigates and evaluates methods for the three-dimensional thermohaline reconstruction of the Arctic Ocean using multi-source observational data. A multivariate statistical regression model based on sea ice seasonal variation is developed, driving by satellite data, and in situ data is used to validate the model output. The study indicates that the multivariate statistical regression model effectively captures the characteristics of the three-dimensional thermohaline structure of the Arctic Ocean. Areas with large reconstruction errors are primarily observed in the salinity values of ice-free regions and the temperature values of ice-covered regions. The statistical regression experiments reveal that salinity errors in ice-free regions are caused by inaccuracies in the satellite salinity data, while temperature errors in ice-covered areas mainly result from the inadequate representation of the under-ice temperature structure of the reanalysis data. The continuous and stable thermohaline data produced using near real-time satellite data as input provide an important foundation for studying Arctic marine environmental characteristics and assessing climate change. Full article

In response to the demand for high-precision acoustic support under the condition of limited data, this study utilized high-resolution reanalysis data and in situ observation data to extract the Kuroshio Extension Front (KEF) section through front-line identification methods. By combining the parameterized oceanic front model and the statistical features of big data, the parameterized oceanic front was reconstructed. A proxy dataset was generated using the Latin hypercube sampling method, and the sound speed reconstruction model based on the PIX2PIX model was trained and validated using single sound speed profiles at different positions of the oceanic front, combined with the parameterized oceanic front model. The experimental results show that the proposed sound speed reconstruction model can significantly improve the reconstruction accuracy by introducing the parameterized front model as an additional input, especially in the shallow-water area. The mean absolute error (MAE) of the full-depth sound speed reconstruction for this model is 0.63~0.95 m·s⁻¹, and the structural similarity index (SSIM) is 0.76~0.78. The MAE of the sound speed section within a 1000 m depth is reduced by 6.50~37.62%, reaching 1.95~3.31 m·s⁻¹. In addition, the acoustic support capabilities and generalization of the model were verified through ray tracing models and in situ data. This study contributes to advancing high-precision acoustic support in data-limited oceanic environments, laying a solid groundwork for future innovations in marine acoustics. Full article

Through the integration of genomic information, transcriptome sequencing data, and bioinformatics methods, we conducted a comprehensive identification of the ALDH gene family in melon. We explored the impact of this gene family on melon growth, development, and their expression patterns in various tissues and under different stress conditions. Our study discovered a total of 17 ALDH genes spread across chromosomes 1, 2, 3, 4, 5, 7, 8, 11, and 12 in the melon genome. Through a phylogenetic analysis, these genes were classified into 10 distinct subfamilies. Notably, genes within the same subfamily exhibited consistent gene structures and conserved motifs. Our study discovered a pair of fragmental duplications within the melon ALDH gene. Furthermore, there was a noticeable collinearity relationship between the melon’s ALDH gene and that of Arabidopsis (12 times), and rice (3 times). Transcriptome data reanalysis revealed that some ALDH genes consistently expressed highly across all tissues and developmental stages, while others were tissue- or stage-specific. We analyzed the ALDH gene’s expression patterns under six stress types, namely salt, cold, waterlogged, powdery mildew, Fusarium wilt, and gummy stem blight. The results showed differential expression of CmALDH2C4 and CmALDH11A3 under all stress conditions, signifying their crucial roles in melon growth and stress response. RT-qPCR (quantitative reverse transcription PCR) analysis further corroborated these findings. This study paves the way for future genetic improvements in melon molecular breeding. Full article

Exploring the spatial distribution of sea surface height involves two primary methodologies: utilizing gridded reanalysis data post-secondary processing or conducting direct fitting along-track data. While processing gridded reanalysis data may entail information loss, existing direct fitting methods have limitations. Therefore, there is a pressing need for novel direct fitting approaches to enhance efficiency and accuracy in sea surface height fitting. This study demonstrates the viability of Legendre polynomial surface fitting, benchmarked against bicubic quasi-uniform B-spline surface fitting, which has been proven to be a well-established direct fitting method. Despite slightly superior accuracy exhibited by bicubic quasi-uniform B-spline surface fitting under identical order combinations, Legendre polynomial surface fitting offers a simpler structure and enhanced controllability. However, it is pertinent to note that significant expansion of the spatial scope of fitting often results in decreased fitting efficacy. To address this, the current research achieves the precise fitting of sea surface height across expansive spatial ranges through a regional stitching methodology. Full article