Search Results (19,764)

Pistachio acreage is increasing noticeably in Spain. However, water management in these plantations still remains a challenge due to the fact that irrigation in new production regions is still not well defined. In this context, the current study aimed to assess the impact of two contrasting irrigation doses on the water relations, yield, and nut quality of pistachio trees (cv. Kerman) in La Seca (central Spain) over five years. Specifically, the high irrigation treatment (H) received 50% more water than the control (C). Soil moisture, stem water potential (Ψ_s), gas exchange parameters, growth, yield, and nut quality traits were monitored. During summer, a slight decline in Ψ_s was observed, with trees from the C treatment exhibiting the most negative values, indicating a slight dehydration. The dry weight of split nuts, with greater marketing value, was higher in H compared to C over the five-year study period. In general, the nutritional composition of the nuts did not differ between treatments. These results provide useful information for the establishment of a suitable irrigation strategy for pistachio cv. Kerman in central Spain and other regions with similar weather conditions. Full article

Wireless Power Transfer (WPT) for electric vehicles is transitioning from laboratory prototypes to deployable charging infrastructure, driven by the demand for safer, automated, and weather-robust charging in residential parking, depots, and public bays, and more recently by pilot electric-road concepts. This review focuses on near-field resonant inductive WPT and explicitly frames the discussion around standardization and deployment readiness, with SAE J2954 and related international frameworks as reference points for interoperability, alignment, conformance testing, and certification planning across static, quasi-dynamic, and dynamic solutions. Recent surveys and representative demonstrators are synthesized to consolidate dominant research and engineering themes, including magnetic coupler and shielding design, compensation-network and control co-design, segment architecture and handover strategies for dynamic tracks, safety functions, electromagnetic exposure verification, electromagnetic compatibility constraints, bidirectional operation, and data-driven methods supporting design and field adaptation. For light-duty static charging, interoperable pad families, alignment procedures, and mature compensation topologies enable repeatable high-efficiency operation and increasingly standardized validation workflows, supporting early commercial availability. Heavy-duty depot charging appears technically attractive where duty cycles favor opportunity charging and packaging constraints are manageable. Dynamic WPT has reached pilot readiness via segmented selective-energization tracks and coordinated localization and handover, but corridor-scale rollout remains limited by maintainability, seasonal reliability, cost per kilometer, and route and site-specific verification of safety, exposure, and EMC margins. Full article

Accurate prediction of wind direction is a critical requirement for coastal safety management, renewable energy optimization, and weather-driven risk mitigation, particularly in highly dynamic atmospheric environments where statistical and deep learning models often struggle to capture nonlinear interactions and temporal dependencies. Existing approaches typically rely on raw or weakly processed meteorological inputs and treat directional information implicitly, which limits their ability to exploit the underlying physical structure of wind evolution. To address these challenges, this research designs a novel Physics Vector Driven (PVD) data pre-processing framework that explicitly encodes physically meaningful gradients and directional dynamics from multivariate meteorological observations, transforming raw measurements into sequence-aware vector representations suitable for deep time-series learning. Building on this foundation, a novel Directional Temporal Gradient Vector Network (DTGVectorNet) is proposed, which fuses a Directional Gradient Attention ResNet (DGResNet 1D CNN) for spatial-directional feature extraction with a Temporal Gradient LSTM (TGLSTM) designed to model the temporal evolution of wind vectors. The tight integration of Directional Gradient Attention (DGA) and Temporal Gradient (TG) memory enables the network to jointly learn instantaneous directional cues and their temporal propagation, significantly enhancing predictive fidelity. An experimental evaluation of the Busan wind datasets demonstrates that the proposed DTGVectorNet achieves a wind direction prediction accuracy of 99.12%, substantially outperforming conventional state-of-the-art baselines. These results confirm that physics-aware vector preprocessing combined with directional-temporal gradient fusion provides a powerful and generalizable paradigm for high-precision wind direction forecasting. To ensure reproducibility and facilitate further research, the complete dataset and implementation details of DTGVectorNet are publicly available through an open-access repository, Zenodo. Full article

High renewable energy penetration creates significant operational challenges for power systems, especially during extreme weather that disrupts supply–demand balance. This study introduces a framework that integrates extreme scenario identification, data augmentation, and power balance optimization. It defines extreme wind speed events, such as sudden drops, surges, and persistent anomalies, and uses a sliding-window algorithm to extract these events from historical meteorological data. To address the scarcity of extreme samples, a new data augmentation method combines the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and iterative distribution shifting. This approach focuses the generated data on distribution tails while preserving diversity and temporal consistency. An optimization model, which includes various generation resources, energy storage, and load shedding, is developed to assess system flexibility under extreme conditions. Case studies on the projected 2030 Northeast China Power Grid show that the augmentation method expands extreme scenario datasets from 150 to 1000 samples, maintains extremity and temporal consistency, and reveals that wind curtailment rises sharply above 70% renewable share, with storage systems providing key flexibility in high-output scenarios. Full article

This study develops a machine learning-based predictive model for identifying high-level clouds (HLCs). The model uses meteorological parameters as input features and is trained against human-recorded meteorological observations. A statistical analysis of the relationship between two independent methods of registering HLCs—lidar and meteorological observations—has been performed. Optimal thresholds for the total amount of cloud cover, at which meteorological data are consistent with lidar data, have been determined. The results demonstrate the promising performance of ML models in identifying the links between weather conditions and the probability of HLC detection, which is confirmed by ROC AUC (Area Under the Curve of the Receiver Operating Characteristic) values in the range of 0.87–0.88 for the presence and 0.77–0.78 for the absence of clouds, as well as balanced metrics Precision, Recall, and F1. The XGBoost (eXtreme Gradient Boosting) model proved to be the most robust, demonstrating the ability to effectively integrate data of various types for reliable prediction in various conditions. Full article

An extreme rainfall event that occurred from 16 to 18 December 2021 along the coastal regions of Peninsular Malaysia (PM) caused widespread flooding and substantial socioeconomic impacts. This study investigates the mechanisms leading to this event, focusing on the roles of climatic phenomena and local terrains. Two atmospheric interactions play key roles in triggering the event. Firstly, a strong cold surge (CS) associated with the East Asian winter monsoon (EAWM) interacted with the easterly surge over the southern South China Sea, leading to the formation of Borneo vortex. Secondly, a strong northeasterly and CS largely contributed to enhancing and transporting the vortex towards the PM and across the Titiwangsa mountain ranges. The phase change of the Indian Ocean Dipole (IOD) facilitated the eastward propagation of the vortex. Sumatra and PM terrains significantly modulated vortex evolution and moisture convergence over the Strait of Malacca. These findings are analyzed to shed light on interactions between large-scale climate drivers and localized terrain in generating extreme rainfall, emphasizing the necessity of multi-scale analysis for model accuracy. Full article

In complex marine environments, the safety and efficiency of ship navigation face dual challenges from static obstacles, such as shallow waters and islands, and extreme dynamic meteorological threats, such as typhoons. Existing path-planning algorithms often struggle to achieve an optimal balance between computational efficiency and risk-avoidance effectiveness when addressing high-frequency dynamic meteorological changes. To address this limitation, this study proposes a Time-Dynamic Theta* (TDM-Theta*) approach. From an algorithmic perspective, this method extends traditional any-angle path planning by introducing a temporal dimension to the search space. For maritime application, it integrates real-time significant wave height as a spatio-temporal dynamic constraint, thereby dynamically evaluating the actual impact of marine meteorology on ship navigability. Simulation tests were conducted through nine experimental cases designed under three typical navigation scenarios: unrestricted waters, complex terrains, and typhoon transits. The results demonstrate that the TDM-Theta* algorithm not only efficiently generates the shortest paths in statically complex terrains but also achieves a 100% proactive risk avoidance rate within the boundaries of the evaluated extreme weather scenarios with multiple concurrent typhoons, incurring negligible computational overhead and low path costs. This research provides robust theoretical and methodological support for real-time safe route decision-making for intelligent ships in complex and volatile environments. Full article

Infrared and visible image fusion is a critical technology for enhancing the all-weather perception capabilities of autonomous driving systems. However, the inherent physical parallax of vehicle-mounted sensors combined with motion-induced vibrations makes it difficult to achieve strict alignment between the source images. Direct fusion of such misaligned pairs leads to ghosting artifacts, which significantly compromises driving safety. To address this challenge, this paper proposes a cascaded deep fusion framework tailored for autonomous driving scenarios. A dual-modal perception dataset is first constructed, incorporating realistic physical parallax and non-rigid deformations. Subsequently, a decoupled strategy is established, characterized by geometric correction followed by semantic fusion: the Static-Feature Recursive Registration (SFRR) network is utilized to explicitly correct the spatial misalignments caused by parallax, thereby establishing geometric consistency; then, the Hierarchical Invertible Block Fusion (HIBF) network achieves lossless integration of cross-modal features by combining spatial frequency separation with invertible interaction techniques. Experimental results demonstrate that the proposed method outperforms representative algorithms across several metrics, including Mutual Information (MI), Visual Information Fidelity (VIF), Structural Similarity (SSIM), and Correlation Coefficient (CC), producing high-quality fused images with clear structural definitions. Full article

For effective winter road management in snow-prone regions, timely snow removal that reflects weather and traffic conditions is required. In Akita City, Japan, city hall staff measure snow depth and dispatch contracted snow removal crews only when a predefined threshold is exceeded. Consequently, dispatch decisions depend heavily on staff experience. This study demonstrates objective, experience-independent dispatching based on predicting the number of snowplow dozers in operation, thereby reducing the municipal decision burden and improving contractor efficiency. The target variable is highly imbalanced, with long non-operational periods and wide variations in the number of deployed units during snowfall events. When trained directly on such data, models tend to regress toward near-median values and face difficulty capturing operational dynamics. To address this issue, we propose a two-stage framework: firstly, a classifier predicts whether snow removal operations will occur; secondly, a regressor estimates the number of operating dozers based on the operation. We further integrate multi-year datasets to enhance generalization across diverse snow conditions. Experiments showed that the proposed approach achieved an AUPRC of 0.84 for operation occurrence and an RMSE of 1.85 for dozer-count estimation, outperforming models trained on a single year. Full article

Italy and the surrounding seas are recognised as one of the European hotspots for tornadoes and waterspouts. In recent years, the town of Rosignano Solvay (on the Northern Tyrrhenian coast) experienced repeated waterspouts affecting the same areas, raising local concern about the possible influence of heated wastewater discharged into the sea by a nearby industrial site. We reconstruct the mesoscale meteorological conditions of four intense waterspouts near Rosignano Solvay using a limited-area weather model at a high-to-very-high resolution (inner domain grid spacing of 500 m; sensitivity tests at 100 m). At the reported event times, the intensity of key mesoscale precursors (low-level wind shear, 1 km storm-relative helicity, maximum updraft intensity, and lifting condensation level) is consistent with the values typically associated with EF1 (or stronger) tornadoes and waterspouts. The model systematically predicts the peak of instability indices 2–3 h earlier than the reported event times. For one case study, we conduct two sea surface temperature sensitivity experiments to assess the potential atmospheric impact of heated wastewater discharge (temperature increases of +1.5 K and +5 K over a 10 km² area). The resulting changes in instability indices are marginal, with differences of at most 3% relative to the control run. A simple mass-balance estimate for the modified sea patch suggests that, given the reported discharge rates, a plausible impact of the warm water released from the industrial site could lead to an increase in the local sea surface temperature of approximately +0.7 °C over two months. We conclude that synoptic and mesoscale conditions primarily govern waterspout initiation in this region, while the direct effect of the small warm coastal plume from the industrial discharge appears to be minor. Full article

Utilizing multi-source meteorological data for lightning identification is crucial for monitoring severe convective weather. However, several key challenges persist in this field: dimensional imbalance and modal competition among multi-source heterogeneous data, model training bias caused by the extreme sparsity of lightning samples, and an imbalance between false alarms and missed detections resulting from complex background noise. To address these challenges, this paper proposes a lightning identification network guided by physical priors and constrained by supervision. First, to tackle the issue of modal competition in fusing satellite (high-dimensional) and radar (low-dimensional) data, a physical prior-guided asymmetric radar information enhancement mechanism is introduced. This mechanism uses radar physical features as contextual guidance to selectively enhance the latent weak radar signatures. Second, at the architectural level, a multi-source multi-scale feature fusion module and a weighted sliding window–multilayer perceptron (MLP) enhanced decoding unit are constructed. The former achieves the coupling of multi-scale physical features at a 2 km grid scale through cross-level semantic alignment, building a highly consistent feature field that effectively improves the model’s ability to detect lightning signals. The latter leverages adaptive receptive fields and the nonlinear modeling capability of MLPs to effectively smooth spatially discrete noise, ensuring spatial continuity in the reconstructed results. Finally, to address the model bias caused by severe class imbalance between positive and negative samples—resulting from the extreme sparsity of lightning events—an asymmetrically weighted BCE-DICE loss function is designed. Its “asymmetric” characteristic is implemented by assigning different penalty weights to false-positive and false-negative predictions. This loss function balances pixel-level accuracy and inter-class equilibrium while imposing high-weight penalties on false-positive predictions, achieving synergistic optimization of feature enhancement and directional suppression. Experimental results show that the proposed method effectively increases the hit rate while substantially reducing the false alarm rate, enabling efficient utilization of multi-source data and high-precision identification of lightning strike areas. Full article

The Early Cretaceous Hongshanwan landform in the Lanzhou Basin hosts distinctive multicolored rhythmic sedimentary layers, yet the factors controlling their coloration remain debated. This study integrates mineralogical observations, whole-rock geochemistry, and detrital zircon U-Pb geochronology to investigate the controls on sediment coloration and basin evolution. Sharp and stratigraphically consistent color boundaries indicate that coloration was largely established during sedimentation and early diagenesis, with limited influence from late-stage weathering. Geochemical data suggest that the sediments were predominantly derived from intermediate-to-mafic igneous rocks under low-to-moderate chemical weathering and dominantly oxidizing conditions. Reddish-brown strata are mainly colored by fine-grained authigenic hematite formed during early diagenesis, whereas bluish-gray and pale-yellow layers inherit their colors from calcareous and mafic components with limited post-depositional alteration. Detrital zircon age distributions reveal three principal age populations (1322–1994 Ma, 331–376 Ma and 217–286 Ma), providing first-order constraints on provenance evolution and episodic sediment supply linked to multiple orogenic cycles in a back-arc foreland basin setting. Overall, the multicolored stratigraphy reflects a coupled influence of provenance composition, depositional redox state, diagenetic processes, and tectonic forcing, offering new insights into the origin and evolution of continental red-bed systems in inland basins of northern China. Full article

Lightweight day-ahead photovoltaic (PV) forecasting models encounter a significant technical challenge: under resource-constrained deployment conditions, it is difficult to simultaneously address weather-regime heterogeneity, maintain model interpretability, and preserve adaptability as operating conditions evolve. To address this issue, we propose a multimodal short-term photovoltaic (PV) forecasting method that integrates weather-mode partitioning using the Clustering Using Representatives (CURE) algorithm with a self-updating Batch-Lasso model. First, the meteorological-PV dataset is partitioned along two dimensions by combining seasonal grouping with CURE clustering within each season, producing representative weather modes and enhancing the fidelity of weather pattern classification. Second, to extract informative predictors from high-dimensional meteorological inputs while maintaining interpretability, we formulate per-mode Lasso regression and adopt the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) to efficiently solve for the sparse regression coefficients. Third, we introduce a batch-based self-update and correction mechanism with rollback verification, enabling the mode-specific models to be refreshed as new historical data become available while preventing performance degradation. Compared with representative machine learning baselines, the proposed method maintains competitive accuracy with substantially lower computational and storage overhead, enabling high-frequency and energy-efficient inference on resource-constrained terminals, thereby reducing operational burdens and computational energy costs and better meeting the deployment needs of sustainable energy systems under heterogeneous weather conditions. Full article

Against the backdrop of the global energy transition toward deep electrification, the natural gas industry faces challenges, including increased load forecasting uncertainty and frequent extreme weather impacts. To enhance natural gas load forecasting accuracy and support system resilience planning, this study constructs a forecasting model based on quadratic decomposition and hybrid deep learning, incorporating an electricity substitution coefficient to characterize the coupling substitution effect between electricity and natural gas. Under the basic scenario, the VMD-WPD-TCN-BiGRU model is proposed. It employs variational mode decomposition and wavelet packet denoising for secondary signal denoising, combined with a time-series convolutional network and bidirectional gated recurrent unit to extract temporal features. Experiments demonstrate that, compared to mainstream methods such as CNN, BiLSTM, SVM, and XGBoost, this model achieves statistically significant reductions in MSE (11.11–96.21%), MAE (0.89–76.50%), and MAPE (4.10–67.94%), significantly improving forecasting accuracy. In the deep electrification scenario, the introduction of the electricity substitution coefficient further optimizes peak load forecasting for system pressure days under extreme low temperatures, elevating the overall R² to 0.9905 in the deep electrification scenario. Research indicates that the proposed model not only effectively improves the accuracy of short-term natural gas load forecasting but also provides quantitative support for enterprises to plan peak-shaving facilities, optimize pipeline networks, and respond to extreme weather emergencies in data silo environments. This contributes to strengthening the adaptability and long-term resilience of natural gas systems during the energy transition, thereby supporting the sustainable development of energy infrastructure. Full article

Climate change poses an urgent challenge to Canada’s sustainable development. The country experiences increasing extreme weather events, rising temperatures, and pressures on energy systems—particularly in remote northern regions. In Newfoundland and Labrador, isolated communities are vulnerable because reliance on diesel-based electricity increases greenhouse gas emissions, energy costs, and environmental risks, highlighting the need for resilient energy solutions. This study uses a systematic methodology combining literature review, local energy demand data, and site-specific wind resources to design and optimize hybrid renewable energy systems (HRESs) for Makkovik. It employs HOMER Pro and the Monte Carlo method to evaluate uncertainties in cost, fuel consumption, and renewable fraction. The objectives are to quantify how renewable integration can reduce emissions, improve energy reliability, and support sustainable development in remote communities. The novelty lies in combining location-specific modeling with probabilistic Monte Carlo analysis and providing robust, system-level insights into environmental and economic outcomes while guiding climate-resilient energy planning. The proposed HRES significantly mitigates climate change impacts, reducing annual CO₂ emissions from 72,500 kg/year to 15,190 kg/year. Monte Carlo analysis indicates economic feasibility with a net present cost of $14.5 million, a levelized cost of electricity of 0.256 $/kWh, and diesel consumption reduced from 29,970 L/year to 5854 L/year. Wind energy provides 99.6% of total annual electricity, ensuring a high renewable fraction and reliable power, enhancing energy resilience and adaptation potential. This study demonstrates that a well-designed hybrid renewable energy system can deliver measurable emission reductions, economic feasibility, and enhanced energy resilience. It supports sustainable development and climate change mitigation in remote Canadian communities. Full article