Search Results (611)

Understanding the variability of turbulence intensity (TI) under different wind regimes is essential for the design and safety of offshore wind turbines. The IEC Normal Turbulence Model (NTM), though widely adopted in industry, does not incorporate directional dependence or account for extreme wind events such as typhoons, which can lead to substantial underestimation of turbulence in complex offshore environments. In this study, field measurements from two coastal sites in China, Huilai and Pingtan, were analyzed. At Pingtan, two months of observations captured both normal and typhoon-affected winds, providing a unique dataset for assessing turbulence under typhoon-affected conditions. The results show that wind speeds during the typhoon-affected period were approximately 14% higher than those during normal periods. At Huilai, TI was evaluated under northeasterly and southeasterly sea breezes, revealing that the IEC NTM underestimated TI by 15–42%, with more pronounced discrepancies under northeasterly winds. Based on these findings, revised NTM parameters and correction factors are proposed for different wind conditions, enhancing the applicability of the model to offshore wind turbine design. This work underscores the importance of incorporating directional and event-specific modifications into IEC turbulence standards to ensure reliable structural assessment across diverse wind regimes. Full article

Extreme heat across the North Indian Plain has intensified in recent decades, with the temperature in Delhi repeatedly exceeding 48 °C. We present a physically interpretable and computationally efficient typology of heatwave risk using aggregated station observations of daily mean temperature, relative humidity, wind speed, and pressure from 1997 to 2016. Quality-controlled, standardized daily features (PCA-verified) were clustered with k-means; internal validity indices (Silhouette, Calinski–Harabasz, and Davies–Bouldin) identified an optimal partition with k = 3, defining three distinct weather regimes. Coupling these regimes with an absolute heatwave criterion (daily mean ≥30 °C for ≥3 days) revealed a pronounced gradient: a dry–hot, high-pressure regime (41% of days) accounted for 63% of heatwave days (mean 33.4 °C; median duration ≈17 days); a mild–humid background (59%) yielded ~8% incidence; and a rare blocking-driven dry intrusion (<1%) produced heatwaves each time, with mean temperatures of >35 °C and episodes persisting for ≥30 days. Regime–heatwave relationships were statistically significant and robust across sensitivity tests, including variations in k, alternative clustering algorithms, and bootstrap resampling. This four-stage workflow consists of data preparation, feature extraction, regime classification, and heatwave risk attribution and provides a transparent basis for regime-aware early warning, demand-side energy management, and public health protection in Delhi and is transferable to other rapidly urbanizing regions. Full article

Windborne debris generated during tornadoes and hurricanes plays a critical role in building damage. This damage occurs either through direct impact on structural and nonstructural components or indirectly by increasing internal pressure when debris penetrates openings (e.g., windows and doors) or creates new ones. These breaches can significantly raise internal pressure, even at lower wind speeds compared to debris-free conditions. Current provisions in ASCE 7, the nationally adopted standard for wind load calculations in the United States, account for factors such as building geometry, location, and exposure category. However, they do not consider the effects of windborne debris on internal pressure coefficients. This study proposes an enhancement to ASCE 7 by incorporating debris effects through the use of a more conservative enclosure classification. Real-world damage observations from three tornado-impacted residential buildings are presented, followed by a failure mechanism analysis, supporting analytical fragility data, and numerical simulations of debris effects on building damage. The findings suggest that treating buildings as Partially Enclosed under ASCE 7 can more accurately reflect debris-induced internal pressures and improve building resilience under extreme wind events. Full article

Wind is an important renewable energy source, and even minor variations in wind speed will significantly impact wind power generation. The objective of this study was to systematically assess the impacts of climate change on wind energy resources in the South China Sea (SCS) under future climate projections. To achieve this, we employed a multi-model ensemble approach based on Coupled Model Intercomparison Project Phase 6 (CMIP6) data under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The results demonstrated that, in comparison with scatterometer wind data, the CMIP6 historical results (1995–2014) showed good performance in capturing the spatiotemporal distribution of wind power density (WPD) in the SCS. There were regional discrepancies in the central SCS due to the complex monsoon-driven wind dynamics. Future projections revealed an overall increase in annual mean wind power density (WPD) across the entire SCS by the mid-21st century (2046–2065) and late 21st century (2080–2099). The seasonal analyses indicated significant WPD increases in summer, especially in the northern SCS and the region adjacent to the Kalimantan strait. The increase in summer (>40 × 10⁻⁴ m/s/year under SSP5-8.5) is about triple that in winter. In the late 21st century, an increase in WPD exceeding 10% can be generally anticipated under the SSP2-4.5 and SSP5-8.5 scenarios in all seasons. The extreme wind in the northern and central SCS will further increase by 5% under the three scenarios, which will add an extra extreme load to wind turbines and related marine facilities. These assessments are essential for wind farm planning and long-term energy production evaluations in the SCS. Based on the findings in this study, specific areas of concern can be targeted to conduct localized downscaling analyses and risk assessments. Full article

The growth environment of corps requires necessary improvements by Chinese solar greenhouses with Pad–Fan Cooling (PFC) systems for reducing their high temperatures in summer. Although computational fluid dynamics (CFD) could dynamically display the changes in humidity, temperature, and wind speed in solar greenhouses, its computational efficiency and accuracy are relatively low. In addition, the use of PFC systems can cool down solar greenhouses in summer, but they will also cause excessive humidity inside the greenhouses, thereby reducing the production efficiency of crops. Most existing studies only verify the effectiveness of a single machine learning (such as ARMA or ARIMA) or deep learning model (such as LSTM or TCN), lacking systematic comparison of different models. In the current study, two machine learning algorithms and three deep learning algorithms were used for their ability to predict a PFC system’s cooling effect, including on humidity, temperature, and wind speed, which were examined using Auto Regression Moving Average (ARMA), Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Time Convolutional Network (TCN), and Glavnoe Razvedivatelnoe Upravlenie (GRU), respectively. These results show that deep learning algorithms are significantly more effective than traditional machine learning algorithms in capturing the complex nonlinear relationships and spatiotemporal changes inside solar greenhouses. The LSTM model achieves R² values of 0.918 for temperature, 0.896 for humidity, and 0.849 for wind speed on the test set. TCN showed strong performance in identifying high-frequency fluctuations and extreme nonlinear features, particularly in wind speed prediction (test set R² = 0.861). However, it exhibited limitations in modeling certain temperature dynamics (e.g., T6 test set R² = 0.242) and humidity evaporation processes (e.g., T7 training set R² = −0.856). GRU delivered excellent performance, achieving a favorable balance between accuracy and efficiency. It attained the highest prediction accuracy for temperature (test set R² = 0.925) and humidity (test set R² = 0.901), and performed only slightly worse than TCN in wind speed prediction. In summary, deep learning models, particularly GRU, offer more reliable methodological support for greenhouse microclimate prediction, thereby facilitating the precise regulation of cooling systems and scientifically informed crop management. Full article

Climate change in Antarctica has profound global implications, influencing sea level rise, atmospheric circulation, and the Earth’s energy balance. This study presents a data-driven baseline analysis of meteorological observations from a British Antarctic Survey automatic weather station (2020–2024). Temporal and seasonal analyses reveal strong insolation-driven variability in temperature, snow depth, and solar radiation, reflecting the extreme polar day–night cycle. Correlation analysis highlights solar radiation, upwelling longwave flux, and snow depth as the most reliable predictors of near-surface temperature, while humidity, pressure, and wind speed contribute minimally. A linear regression baseline and a Random Forest model are evaluated for temperature prediction, with the ensemble approach demonstrating superior accuracy. Although the short data span limits long-term trend attribution, the findings underscore the potential of lightweight, reproducible pipelines for site-specific climate monitoring. All analysis codes are openly available in github, enabling transparency and future methodological extensions to advanced, non-linear models and multi-site datasets. Full article

To address the complex operating conditions and challenging dynamic characteristics of bearings in the main shaft transmission system of wind turbines, this study investigates a specific wind turbine model. By comprehensively considering factors such as main shaft structure, cumulative damage, and stochastic wind loads, we adopt a modular analysis framework integrating the wind field, aerodynamics, the structural response, and fatigue life prediction to establish a method for predicting the fatigue life of main shaft bearings under stochastic wind conditions. To verify this method, the fixed-end main shaft bearing of a 4.5 MW wind turbine was selected as a case study. The research results show the following: (1) Increases in both average wind speed and turbulence intensity significantly shorten the fatigue life of the bearing. (2) Higher turbulence intensity amplifies the dispersion of the speed and load of rolling elements, thereby increasing the probability of extreme operating conditions and exerting an adverse impact on fatigue life. (3) The average wind speed has a significant influence on the overall fatigue life: within a specific range, the fatigue failure probability of the main bearing increases as the average wind speed decreases. (4) The impact of wind speed fluctuations on the hub center load is much greater than that caused by rotational speed changes. (5) In addition, the modular design method adopted in this study calculates that the fatigue life of the fixed-end bearing is 28.8 years, with an overall error of only 0.8 years compared to the 29.6-year fatigue life obtained using Romax simulation software. This research provides important theoretical references and engineering value for improving the operational reliability of wind turbines and enhancing the accuracy of bearing fatigue life prediction. Full article

The precision of ERA5 reanalysis datasets and their applicability in the mountainous regions of central China are essential for weather forecasting and climate change research in the transitional zone between northern and southern China. This study employs three months of continuous measurements collected from a high-precision remote sensing platform located in a representative mountainous valley (Xinyang city) in central China, spanning December 2024 to February 2025. Our findings indicate that both horizontal and vertical wind speeds from the ERA5 dataset exhibit diminishing deviations as altitude increases. Significant biases are observed below 500 m, with horizontal mean wind speed deviations ranging from −4 to −3 m/s and vertical mean wind speed deviations falling between 0.1 and 0.2 m/s. Conversely, minimal biases are noted near the top of the boundary layer. Both ERA5 and observations reveal a dominance of northeasterly and southwesterly winds at near-surface levels, which aligns with the valley orientation. This underscores the substantial impact of heterogeneous mountainous terrain on the low-level dynamic field. At an altitude of 1000 m, both datasets present similar frequency patterns, with peak frequencies of approximately 15%; however, notable discrepancies in peak wind directions are evident (north–northeast for observations and north–northwest for ERA5). In contrast to dynamic variables, ERA5 temperature deviations are centered around 0 K within the lower layers (0–500 m) but show a slight increase, varying from around 0 K to 6.8 K, indicating an upward trend in deviation with altitude. Similarly, relative humidity (RH) demonstrates an increasing bias with altitude, although its representation of moisture variability remains insufficient. During a typical cold event, substantial deviations in multiple ERA5 variables highlight the needs for further improvements. The integration of machine learning techniques and mathematical correction algorithms is strongly recommended as a means to enhance the accuracy of ERA5 data under such extreme conditions. These findings contribute to a deeper understanding of the use of ERA5 datasets in the mountainous areas of central China and offer reliable scientific references for weather forecasting and climate modelings in these areas. Full article

This study assesses the efficacy of the ZJWARMS model’s AI-based post-processing correction method for temperature and wind speed forecasts in complex terrain. By analyzing 72 h forecasts at four stations with varying elevations (from 273 m to 1327 m) in the Liuchun Lake region during December 2021–December 2022, the study found that AI-based corrections substantially enhanced both forecast accuracy and stability. The results indicate that, after correction, temperature forecast accuracy at all stations exceeded 99%, with the most notable relative gains at higher elevations (up to 48.1%). The mean absolute error (MAE) for temperature declined from 3.08 °C to below 0.8 °C at Octagonal Palace, and from 3.29 °C to below 0.6 °C at Mountaintop. Wind speed forecast accuracy also increased from approximately 60–70% to nearly 100%, with MAE generally constrained to the range of 0.2–0.4 m/s. In terms of extreme error control, the number of samples with temperature errors exceeding ±2 °C was markedly reduced. For instance, at Mountainside, the count dropped from 127 to 0. Extreme wind speed errors were also effectively eliminated. After correction, error distributions became more concentrated, and both temporal stability and spatial consistency showed notable improvement. These gains enhance operational forecasting and risk management in mountainous regions, for example, through threshold-based wind-hazard alerts and support for mountain-road icing, by providing more reliable, high-confidence guidance. Full article

In addition to air temperature and personal factors, other weather quantities govern the outdoor human thermal perception. This study provides a new targeted approach for the evaluation of extreme events based on a specific multivariable bioclimate index. Heat waves (HWs) and outdoor human thermal stress (OHTS) events that occurred in downtown Rome (Italy) over the years 2018–2023 are identified, characterized, and compared through appropriate indices based on the air temperature for HWs and the Mediterranean Outdoor Comfort Index (MOCI) for OHTS events. The overlap between the two types of events is evaluated for each year through the hit (HR) and false alarm rates. The outcomes reveal severe traits for HWs and OHTS events and higher values of HR (minimum of 66%) with OHTS as a predictor of extreme conditions. This pilot investigation confirms that the use of air temperature threshold underestimates human physiological stress, revealing the importance of including multiple parameters, such as weather variables (temperature, wind speed, humidity, and solar radiation) and personal factors, in the assessment of hazards for the population living in a specific geographical region. This type of approach reveals increasingly critical facets and can provide key strategies to establish safe outdoor conditions for occupational and leisure activities. Full article

This study presents an integrated assessment of four decades (1985–2023) of environmental and climate alterations in the principal metropolitan conurbation of the eastern Brazilian Amazon, encompassing Belém and its adjacent municipalities. By combining high-resolution land use/land cover (LULC) dynamics with in situ meteorological data, including understudied elements, such as relative humidity (RH) and wind speed, and satellite-derived precipitation estimates (CHIRPS v3), we advance the scientific understanding of regional climate trends. Our results document significant climate shifts, including pronounced dry-season warming (+1.5 °C), atmospheric drying (−4% in RH), attenuated wind patterns (−0.4 m s⁻¹), and altered precipitation regimes, which exhibit strong spatiotemporal coupling with extensive forest loss (−20%) and rapid urban expansion (+84%) between 1985 and 2023. Multivariate analyses reveal that these land–climate interactions are strongest during the dry regime, underscoring the role of surface–atmosphere feedbacks in amplifying regional changes. Comparative analysis of past (1980–1999) and present (2005–2024) decades demonstrates a marked intensification in the frequency and magnitude of extreme seasonal climate events. These findings elucidate a critical feedback mechanism that exacerbates climate risks in tropical urban areas. Consequently, we argue that mitigation public policies must prioritize the strict conservation of peri-urban forest fragments (vital for moisture recycling and local climate regulation) and the strategic implementation of green infrastructure aligned with prevailing wind patterns to enhance thermal comfort and resilience to hydrological extremes. Full article

Xinjiang is a critical wind energy region in China. This study characterizes extreme wind speed (EWS) events in Xinjiang by using ERA5 reanalysis (1979–2023) and station observations (2022–2023). Through k-means clustering and wind power density classification, four distinct regions and representative nodes were identified, aligned with the “Three Mountains and Two Basins” topography: Region #1 (eastern wind-rich corridor), Region #2 (Tarim Basin, west–east increasing wind power density), Region #3 (northern valleys), and Region #4 (mountainous areas with weakest wind power density). Peaks-over-threshold analysis revealed 10~30-year return levels varying regionally, with 10-year return level for Node #1 reaching Beaufort Scale 11 but only Scale 6 for Node #4. Since 2001, EWS occurrences increased, with Nodes #2–4 showing doubled 10-year event occurrences in 2012–2023. Events exhibit consistent afternoon peaks and spring dominance (except Node #2 with summer maxima). Such long-term trends and diurnal and seasonal preferences of EWS could be partly explained by diverging synoptic drivers: orographic effects and enhanced pressure gradients (Node #1 and #3) associated with Ural blocking and polar vortex shifts, both showing intensification trends; thermal lows in the Tarim Basin (Node #2) accounting for their summer prevalence; boundary-layer instability that leads to localized wind intensification (Node #4). The results suggest the necessity of region-specific forecasting strategies for wind energy resilience. Full article

Extreme weather events, such as typhoons, induce strong wave–current interactions that significantly alter nearshore hydrodynamic conditions, particularly in shallow intertidal zones. This study investigates the influence of wind speed and water depth on wave–current coupling under typhoon conditions in Shenhu Bay, southeastern China—a semi-enclosed bay that hosts multiple ancient forest relics within its intertidal zone. A two-tier numerical modeling framework was developed, comprising a regional-scale hydrodynamic model and a localized high-resolution model centered on a protective structure. Validation data were obtained from in situ field observations. Three structural scenarios were tested: fully intact, bottom-blocked, and damaged. Results indicate that wave-induced radiation stress plays a dominant role in enhancing flow velocities when wind speeds exceed 6 m/s, with wave contributions approaching 100% across all water depths. However, the linear relationship between water depth and wave contribution observed under non-typhoon conditions breaks down under typhoon forcing. A critical depth range was identified, within which wave contribution peaked before declining with further increases in depth—highlighting its potential sensitivity to storm energy. Moreover, structural simulations revealed that bottom-blocked devices, although seemingly more enclosed, may be vulnerable to vertical pressure loading due to insufficient water exchange. In contrast, perforated designs facilitate an internal–external hydrodynamic balance, thereby enhancing protective effect. This study provides both theoretical and practical insights into intertidal structure design and paleo-heritage conservation under extreme hydrodynamic stress. Full article

Accurate estimation of daily reference evapotranspiration (ET_o) is crucial for sustainable water resource management and irrigation scheduling, especially in water-scarce regions like Arizona. The standardized Penman–Monteith (PM) method is costly and requires specialized instruments and expertise, making it generally impractical for commercial growers. This study developed 35 ET_o models to predict daily ET_o across Coolidge, Maricopa, and Queen Creek in Pinal County, Arizona. Seven input combinations of daily meteorological variables were used for training and testing five machine learning (ML) models: Artificial Neural Network (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Support Vector Machine (SVM). Four statistical indicators, coefficient of determination (R²), the normalized root-mean-squared error (RMSE_n), mean absolute error (MAE), and simulation error (S_e), were used to evaluate the ML models’ performance in comparison with the FAO-56 PM standardized method. The SHapley Additive exPlanations (SHAP) method was used to interpret each meteorological variable’s contribution to the model predictions. Overall, the 35 ET_o-developed models showed an excellent to fair performance in predicting daily ET_o over the three weather stations. Employing ANN10, RF10, XGBoost10, CatBoost10, and SVM10, incorporating all ten meteorological variables, yielded the highest accuracies during training and testing periods (0.994 ≤ R² ≤ 1.0, 0.729 ≤ RMSE_n ≤ 3.662, 0.030 ≤ MAE ≤ 0.181 mm·day⁻¹, and 0.833 ≤ S_e ≤ 2.295). Excluding meteorological variables caused a gradual decline in ET-developed models’ performance across the stations. However, 3-variable models using only maximum, minimum, and average temperatures (T_max, T_min, and T_ave) predicted ET_o well across the three stations during testing (17.655 ≤ RMSE_n ≤ 13.469 and S_e ≤ 15.45%). Results highlighted that T_max, solar radiation (R_s), and wind speed at 2 m height (U₂) are the most influential factors affecting ET_o at the central Arizona sites, followed by extraterrestrial solar radiation (R_a) and T_ave. In contrast, humidity-related variables (RH_min, RH_max, and RH_ave), along with T_min and precipitation (P_r), had minimal impact on the model’s predictions. The results are informative for assisting growers and policymakers in developing effective water management strategies, especially for arid regions like central Arizona. Full article

Wildfire activity is intensifying globally as climate change amplifies heat waves, droughts and wind extremes, threatening biodiversity. South Korea (63% forested) has experienced a sharp rise in large fires. We analysed 905 wildfires ≥ 5 ha from 1980–2024, linking burned area to maximum wind speed, relative humidity, temperature and forest structure (conifer, broadleaf and mature–stand ratios, forest cover). Pearson correlations, HC3-corrected regression, a 1000-tree Random Forest and five-fold validated XGBoost interpreted with SHAP captured linear and nonlinear effects; WUI influences were examined qualitatively. Each 1 m s⁻¹ increase in peak wind expanded burned area by ~8.5 ha, whereas a 1% rise in humidity reduced area by ~3 ha (p < 0.01). Broadleaf prevalence restrained spread, while high conifer and mature–stand proportions enlarged it. Machine learning raised explanatory power from R² = 0.62 to 0.66 and showed that very dry air, strong winds and conifer cover above half the landscape coincided with the largest events. Burned area during 2020–2024 reached 29,905 ha—sevenfold that of 2015–2019. These results imply that extreme fire weather, flammable pine fuels and expanding WUI settlements jointly elevate risk; implementing real-time meteorological thresholds, targeted fuel treatments and stricter WUI zoning can help mitigate this risk. Full article