Search Results (82)

This study employs the storm surge numerical model originally developed for the Bohai Sea. In contrast to the previous work, the current simulation focuses on the South China Sea and severe weather events, with a case study conducted through the numerical simulation of the storm surge induced by Typhoon “Mangkhut” on 15–17 September 2018. The typhoon wind field serves as a critical forcing factor influencing simulation accuracy. Therefore, five different wind fields were employed as driving conditions: the ERA5 reanalysis wind field (EWF), the parameterized Jelesnianski (JWF) and Holland (HWF) wind fields, and two hybrid wind fields (JEWF and HEWF) combining EWF with JWF and HWF, respectively. And the adjoint data assimilation method was applied to invert a physically more consistent wind stress drag coefficient (C_D). The results indicate the model effectively reproduced this dynamic process. After assimilation, the simulated water levels showed significantly improved agreement with measurements, with errors reduced by 50% (EWF), 47% (JWF), 48% (JEWF), 26% (HWF), and 42% (HEWF), respectively. And JWF not only exhibited a smaller MAE than HWF but also yielded a more reasonable C_D, demonstrating its superior performance in this typhoon case. The hybrid strategy further reduced errors to 0.14 m, effectively mitigating the limitations of individual wind fields. This study offers a new perspective on adjoint assimilation-based numerical modeling of storm surges in the South China Sea. Full article

The global pursuit of sustainable development amplifies renewable energy’s strategic importance, positioning wind power as a vital modern grid component. Accurate wind forecasting is essential to counter inherent volatility, enabling robust grid operations, security protocols, and optimization strategies. Such predictive precision directly governs wind energy systems’ stability and sustainability. This research introduces a novel spatio-temporal hybrid model integrating convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and graph convolutional networks (GCN) to extract temporal patterns and meteorological dynamics (wind speed, direction, temperature) across 134 wind turbines. Building upon conventional methods, our architecture captures turbine spatio-temporal correlations while assimilating multivariate meteorological characteristics. Addressing data integrity compromises from equipment failures and extreme weather-which undermine data-driven models-we implement Wasserstein GAN (WGAN) for generative missing-value interpolation. Validation across severe data loss scenarios (30–90% missing values) demonstrates the model’s enhanced predictive capacity. Rigorous benchmarking confirms significant accuracy improvements and reduced forecasting errors. Full article

In this study, we compare two novel hybrid data assimilation (DA) methods: Localized Weighted Ensemble Kalman filter (LWEnKF) and Implicit Equal-Weights Variational Particle Smoother (IEWVPS). These methods integrate a particle filter (PF) with traditional DA methods. LWEnKF combines the PF with EnKF, while IEWVPS integrates the PF with the four-dimensional variational (4DVAR) method. These hybrid DA methods not only overcome the limitations of linear or Gaussian assumptions in traditional assimilation methods but also address the issue of filter degeneracy in high-dimensional models encountered by pure PFs. Using the Regional Ocean Model System (ROMS), the effects of different DA methods for mesoscale eddies in the northern South China Sea (SCS) are examined using simulation experiments. The hybrid DA methods outperform the linear deterministic variational and Kalman filter methods: compared to the control experiment (no assimilation), EnKF, LWEnKF, IS4DVar and IEWVPS reduce the sea level anomaly (SLA) root-mean-squared error (RMSE) by 55%, 65%, 65% and 80%, respectively, and reduce the sea surface temperature (SST) RMSE by 77%, 78%, 74% and 82%, respectively. In the short-term assimilation experiment, IEWVPS exhibits superior performance and greater stability compared to 4DVAR, and LWEnKF outperforms EnKF (LWEnKF’s posterior SLA RMSE is 0.03 m, lower than EnKF’s value of 0.04 m). Long-term forecasting experiments (16 days, starting on 20 July 2017) are also conducted for mesoscale eddy prediction. The variational methods (especially IEWVPS) perform better in simulating the flow field characteristics of eddies (maintaining accurate eddy structure for the first 10 days, with an average SLA RMSE of 0.05 m in the studied AE1 eddy region), while the filters are more advantageous in determining the total root-mean-squared error (RMSE), as well as the temperature under the sea surface. Overall, compared to EnKF and 4DVAR, the hybrid DA methods better predict mesoscale eddies across both short- and long-term timescales. Although the computational costs of hybrid DA are higher, they are still acceptable: specifically, IEWVPS takes approximately 907 s for a single assimilation cycle, whereas LWEnKF only takes 24 s, and its assimilation accuracy in the later stage can approach that of IEWVPS. Given the computational demands arising from increased model resolution, these hybrid DA methods have great potential for future applications. Full article

Surface drifter trajectory prediction is essential for applications in environmental management, maritime safety, and climate studies. This survey paper reviews research from the past two decades, and systematically classifies the evolution of methodologies into six successive generations, including numerical models, data assimilation, statistical and probabilistic approaches, machine learning, deep learning, and hybrid or AI-based data assimilation (1st–5.5th Generation). To our knowledge, this is the first systematic generational classification of trajectory prediction methods. Each generation revealed distinct strengths and limitations. Numerical models ensured physical consistency but suffered from accumulated forecast errors in observation-sparse regions. Data assimilation improved short-term accuracy as observing networks expanded, while machine learning and deep learning enhanced short-range forecasts but faced challenges such as error accumulation and insufficient physical constraints in longer horizons. More recently, hybrid frameworks and AI-based data assimilation have emerged, combining physical models with deep learning and traditional statistical techniques, thereby opening new possibilities for accuracy improvements. By comparing methodologies across generations, this survey provides a roadmap that helps researchers and practitioners select appropriate approaches depending on observation density, forecast lead time, and application objectives. Finally, this paper highlights that future systems should shift focus from deterministic tracks toward credible uncertainty estimates, region-aware designs, and physically consistent prediction frameworks. Full article

The Altai Republic remains a geographic region with an uncovered microbial diversity hiding yet undescribed potential species. Here, we describe the strain al37.1^T from the Altai soil. It showed genomic similarity with the Bacillus mycoides strain DSM 2048^T. However, the in silico DNA–DNA hybridization (DDH) was 61.6%, which satisfies the accepted threshold for delineating species. The isolate formed circular, smooth colonies, in contrast to the rhizoidal morphology typical of B. mycoides. The strain showed optimal growth under the following conditions: pH 6.5, NaCl concentration 0.5% w/v, and +30 °C. The major fraction of fatty acids was composed of C_16:0 (34.77%), C_18:1 (15.20%), C_14:0 (9.06%), and C_18:0 (7.88%), which were sufficiently lower in DSM 2048^T (C_16:0–15.6%, C_14:0–3.7%). In contrast to DSM 2048^T, al37.1^T utilized glycerol, D-mannose, and D-galactose, while being unable to assimilate D-sorbitol, D-melibiose, and D-raffinose. The strain contains biosynthetic gene clusters (BGCs) associated with the production of fengycin, bacillibactin, petrobactin, and paeninodin, as well as loci coding for insecticidal factors, such as Spp1Aa, chitinases, Bmp1, and InhA1/InhA2. The comparative analysis with the 300 closest genomes demonstrated that these BGCs and Spp1Aa could be considered core for the whole group. Most of the strains, coupled with al37.1^T, contained full nheABC and hblABC operons orchestrating the synthesis of enteric toxins. We observed a cytotoxic effect (≈19 and 22% reduction in viability) of the strain on the PANC-1 cell line. Given the unique morphological features and genome-derived data, we propose a new species, B. altaicus, represented by the type strain al37.1^T. Full article

The integrated assessment of watershed ecosystems is increasingly critical for sustainable water resource management amid global environmental change. Multi-source data integration—encompassing in situ monitoring, remote sensing, and model-based observations—has significantly expanded the spatial and temporal scales at which watershed processes can be analyzed. Concurrently, advances in model coupling strategies, ranging from loose to embedded architectures, have enabled more dynamic and holistic representations of interactions among hydrology, water quality, and ecological systems. However, a unifying operational framework that links multi-source data, cross-scale coupling, and rigorous uncertainty propagation to actionable, real-time decision support is still missing, largely due to gaps in interoperability and stakeholder engagement. Addressing these limitations demands the development of intelligent, adaptive modeling frameworks that leverage hybrid physics-informed machine learning, cross-scale process integration, and continuous real-time data assimilation. Open science practices and transparent model governance are essential for ensuring reproducibility, stakeholder trust, and policy relevance. The recent literature indicates that loose coupling predominates, physics-informed ML tends to generalize better in data-sparse settings, and uncertainty communication remains uneven. Building on these insights, this review synthesizes methods for data harmonization and cross-scale integration, compares coupling architectures and data assimilation schemes, evaluates uncertainty and interoperability practices, and introduces the Smart Integrated Watershed Eco-Assessment Framework (SIWEAF) to support adaptive, real-time, stakeholder-centered decision-making. Full article

As urbanization intensifies, the quantification of methane (CH₄) emissions at city scales faces unprecedented challenges due to spatial heterogeneities from industrial and transportation activities and land use changes. This paper provides a review of the current state of top-down atmospheric CH₄ emission inversion at the city scale, with a focus on CH₄ emission inventories, CH₄ observations, atmospheric transport models, and data assimilation methods. The Bayesian method excels in capturing spatial variability and managing posterior uncertainty at the kilometer-scale resolution, while the hybrid method of variational and ensemble Kalman approaches has the potential to balance computational efficiency in complex urban environments. This review highlights the significant discrepancy between top-down inversion results and bottom-up inventory estimates at the city scale, with inversion uncertainties ranging from 11% to 28%. This indicates the need for further efforts in CH₄ inversion at the city level. A framework is proposed to fundamentally shape city-scale CH₄ emission inversion by four synergistic advancements: developing high-resolution prior emission inventories at the city scale, acquiring observational data through coordinated satellite–ground systems, enhancing computational efficiency using artificial intelligence techniques, and applying isotopic analysis to distinguish CH₄ sources. Full article

Physics-informed neural networks (PINNs) offer a mesh-free approach to solving partial differential equations (PDEs) with embedded physical constraints. Although PINNs have gained traction in various engineering fields, their adoption for railway bridge analysis remains under-explored. To address this gap, a systematic review was conducted across Scopus and Web of Science (2020–2025), filtering records by relevance, journal impact, and language. From an initial pool, 120 articles were selected and categorised into nine thematic clusters that encompass computational frameworks, hybrid integration with conventional solvers, and domain decomposition strategies. Through natural language processing (NLP) and trend mapping, this review evidences a growing but fragmented research landscape. PINNs demonstrate promising capabilities in load distribution modelling, structural health monitoring, and failure prediction, particularly under dynamic train loads on multi-span bridges. However, methodological gaps persist in large-scale simulations, plasticity modelling, and experimental validation. Future work should focus on scalable PINN architectures, refined modelling of inelastic behaviours, and real-time data assimilation, ensuring robustness and generalisability through interdisciplinary collaboration. Full article

This paper presents a detailed analysis of the energy dynamics of the Mediterranean tropical-like cyclone, Medicane Ianos, by using a moist static energy (MSE) budget framework. Medicanes are hybrid cyclonic systems that share characteristics of both extratropical and tropical cyclones, making their classification and prediction challenging. Using high-resolution ERA5 reanalysis data, we analyzed the life cycle of Ianos, which is one of the strongest recorded medicanes, employing the vertically integrated MSE spatial variance budget to quantify the contributions of different energy sources to the cyclone’s development. The chosen study area was approximately 2500² km², covering the entire track of the cyclone. The budget was calculated after tracking Ianos and applying Hart phase space analysis to assess the cyclone phases. The results show that the MSE budget can reveal that the cyclone development was driven by a delicate balance between convection and dynamical factors. The interplay between vertical and horizontal advection, in particular the upward transport of moist air and the lateral inflow of warm, moist air and cold, dry air, was a key mechanism driving the evolution of Ianos, followed by surface fluxes and radiative feedback. By analyzing what process contributes most to the increase in MSE variance, we concluded that Ianos can be assimilated in the tropical framework within a radius of 600 km around the cyclone center, but only during its intense phase. In this way, the budget can contribute as a diagnostic tool to the ongoing debate regarding medicanes classification. Full article

This study introduces two distinct post-processing strategies to address systematic biases in sea surface temperature (SST) numerical forecasts, thereby enhancing SST predictive accuracy. The first strategy implements a spatiotemporal four-dimensional multi-grid analysis (4D-MGA) scheme within a three-dimensional variational (3D-Var) data assimilation framework. The second strategy establishes a hybrid deep learning architecture integrating empirical orthogonal function (EOF) analysis, empirical mode decomposition (EMD), and a backpropagation (BP) neural network (designated as EE–BP). The 4D-MGA strategy dynamically corrects systematic biases through a temporally coherent extrapolation of analysis increments, leveraging its inherent capability to characterize intrinsic temporal correlations in model error evolution. In contrast, the EE–BP strategy develops a bias correction model by learning the systematic biases of the SST numerical forecasts. Utilizing a satellite fusion SST dataset, this study conducted bias correction experiments that specifically addressed the daily SST numerical forecasts with 7-day lead times in the Kuroshio region south of Japan during 2017, systematically quantifying the respective error reduction potentials of both strategies. Quantitative verification reveals that EE–BP delivers enhanced predictive skill across all forecast horizons, achieving 18.1–22.7% root–mean–square error reduction compared to 1.2–9.1% attained by 4D-MGA. This demonstrates deep learning’s unique advantage in capturing nonlinear bias evolution patterns. Full article

This paper attempts to find alternative ways in which heating, ventilation, air conditioning and refrigeration systems can be made more energy efficient and sustainable at a global level. Eight technologies or solutions that either passively or supplementarily reduce the heating or cooling load required by a structure are detailed. These technologies or solutions were then presented to heating, ventilation, air conditioning and refrigeration industry professionals in New Zealand to determine their viability and further establish market readiness towards integrating new, innovative, and sustainable solutions in New Zealand. A literature review was conducted to establish the performance of the selected solutions and understand their operational principles and the efficiency they provided. Qualitative research and data collected via semi-structured interviews provided the data for assessing the viability of the selected technologies in the New Zealand market. Following a thematic and hybrid-thematic analysis of the data, the technologies were ranked, and suggestions were made to help improve innovation and energy efficiency in the heating, ventilation, air conditioning, and refrigeration industry in New Zealand. Of the technologies selected, airtightness, heat recovery ventilation retrofits, materials and design principles, and photovoltaic hot water heating were identified as the most viable. The New Zealand market was deemed not to be in a good position to adopt new or alternative solutions. The main issues affecting New Zealand’s market readiness to assimilate innovative and energy-efficient solutions are a lack of new technologies, poor standards of education throughout the industry, a lack of regulation, and a lack of government incentives. Full article

The COVID-19 pandemic has significantly increased the incidence of post-infection cardiovascular events, particularly myocardial infarction, in individuals over 40. While the underlying mechanisms remain elusive, this study employs a hybrid machine learning approach to analyze epidemiological data in assessing 13 key heart attack risk factors and their susceptibility. Based on a unique dataset that combines demographic, biochemical, ECG, and thallium stress tests, this study aims to design, develop, and deploy a clinical decision support system. Assimilating outcomes from five clustering techniques applied to the ‘Kaggle heart attack risk’ dataset, the study categorizes distinct subpopulations against varying risk profiles and then divides the population into ‘at-risk’ (AR) and ‘not-at-risk’ (NAR) groups using clustering algorithms. The GMM algorithm outperforms its competitors (with clustering accuracy and Silhouette coefficient scores of 84.24% and 0.2623, respectively). Subsequent analyses, employing Pearson correlation and linear regression as descriptors, reveal a strong association between the likelihood of experiencing a heart attack and the 13 risk factors studied, and these are statistically significant (p < 0.05). Our findings provide valuable insights into the development of targeted risk stratification and preventive strategies for high-risk individuals based on heart attack risk scores. The aggravated risk for postmenopausal patients indicates compromised individual risk factors due to estrogen depletion that may be further compromised by extraneous stress impacts, like anxiety and fear, aspects that have traditionally eluded data modeling predictions. The model can be repurposed to analyze the impact of COVID-19 on vulnerable populations. Full article

Flow separation is a fundamental phenomenon in fluid mechanics governed by the Navier–Stokes equations, which are second-order partial differential equations (PDEs). This phenomenon significantly impacts aerodynamic performance in various applications across the aerospace sector, including micro air vehicles (MAVs), advanced air mobility, and the wind energy industry. Its complexity arises from its nonlinear, multidimensional nature, and is further influenced by operational and geometrical parameters beyond Reynolds number (Re), making accurate prediction a persistent challenge. Traditional models often struggle to capture the intricacies of separated flows, requiring advanced simulation and prediction techniques. This review provides a comprehensive overview of strategies for enhancing aerodynamic design by improving the understanding and prediction of flow separation. It highlights recent advancements in simulation and machine learning (ML) methods, which utilize flow field databases and data assimilation techniques. Future directions, including physics-informed neural networks (PINNs) and hybrid frameworks, are also discussed to improve flow separation prediction and control further. Full article

This paper conducts an observing system simulation experiment (OSSE) to assess the impact of assimilating traditional sounding and surface data, along with dropsonde observations over the northern South China Sea (SCS) on heavy rain forecasts in Taiwan. Utilizing the hybrid ensemble transform Kalman filter (ETKF) and the three-dimensional variational (3DVAR) data assimilation (DA) system, this study focuses on an extreme precipitation event near Taiwan on 22 May 2020. The event was mainly influenced by strong southwesterly flow associated with an eastward-moving southwest vortex (SWV) from South China to the north of Taiwan. A nature run (NR) serves as the basis, generating virtual observations for radiosonde, surface, and dropsonde data. Three experiments—NODA (no DA), CTL (traditional observation DA), and T5D24 (additional dropsonde DA)—are configured for comparative analyses. The NODA experiment shows premature and weaker precipitation events across all regions compared with NR. The CTL experiment improved upon NODA’s forecasting capabilities, albeit with delayed onset but prolonged precipitation duration, particularly noticeable in southern Taiwan. The inclusion of dropsonde DA in the T5D24 experiment further enhanced precipitation forecasting, aligning more closely with NR, particularly in southern Taiwan. Investigations of DA impact reveal that assimilating traditional observations significantly enhances the SWV structure and wind fields, as well as the location of frontal systems, with improvements persisting for 40 to 65 h. However, low-level moisture field enhancements are moderate, leading to insufficient precipitation forecasts in southern Taiwan. Additional dropsonde DA over the northern SCS further refines low-level moisture and wind fields over the northern SCS, as well as the occurrence of frontal systems, extending positive impacts beyond 35 h and thus improving the rain forecast. Full article

Water deficiency and potential drought periods could be important ecological factors influencing cultivation areas and productivity once different crops are established. The principal supply of vegetable oil for oil crops is oil palm, and new challenges are emerging in the face of climatic changes. This study investigated the photosynthetic performance of 12 genotypes of Elaeis exposed to drought stress under controlled conditions. The assay included genotypes of Elaeis guineensis, Elaeis oleifera, and the interspecific O×G hybrid (E. oleifera × E. guineensis). The principal results showed that the E. guineensis genotype was the most efficient at achieving photosynthesis under drought stress conditions, followed by the hybrid and E. oleifera genotypes. The physiological parameters showed good prospects for vegetal breeding with different O×G hybrids, mainly because of their ability to maintain the equilibrium between CO₂ assimilation and stomatal aperture. We validated 11 genes associated with drought tolerance, but no differences were detected. These results indicate that no allelic variants were represented in the RNA during sampling for the validated genotypes. In conclusion, this study helps to define genotypes that can be used as parental lines for oil palm improvement. The gas exchange data showed that drought stress tolerance could define guidelines to incorporate the available genetic resources in breeding programs across the early selection in nursery stages. Full article