Search Results (881)

Wind power plays an increasingly vital role in sustainable energy development. However, accurately simulating wind turbine aerodynamics, particularly in offshore wind farms, remains challenging due to complex environmental factors such as the marine atmospheric boundary layer. This study investigates the integration and assessment of the Actuator Line Model (ALM) within the high-order spectral/hp element framework, Nektar++, for wind turbine aerodynamic simulations. The primary objective is to evaluate the implementation and effectiveness of the ALM by analyzing aerodynamic loads, wake behavior, and computational demands. A three-bladed NREL-5MW turbine is modeled using the ALM in Nektar++, with results compared against established computational fluid dynamics (CFD) tools, including SOWFA and AMR-Wind. The findings demonstrate that Nektar++ effectively captures velocity and vorticity fields in the turbine wake while providing aerodynamic load predictions that closely align with finite-volume CFD models. Furthermore, the spectral/hp element framework exhibits favorable scalability and computational efficiency, indicating that Nektar++ is a promising tool for high-fidelity wind turbine and wind farm aerodynamic research. Full article

The northwest region of China’s loess plateau is an important area for wind power development. However, the unclear understanding of the evolution mechanism of the near-ground atmospheric boundary layer (ABL), which is influenced by its unique geomorphological features, has compromised the safety and stability of wind turbine operations. To address this challenge, wind tunnel experiments were conducted to investigate the mean and turbulent characteristics of wake flow generated by mountains in the loess plateau. The results indicate that the terrain significantly affects both the average velocity deficit and turbulence intensity distribution within the wake. Specifically, topographic features dominate turbulent energy transfer and modulate coherent structures in the inertial subrange. Additionally, the scale of these features enhances turbulence energy input at corresponding scales in the fluctuating wind speed spectrum, leading to a non-decaying energy interval within the inertial subregion. Full article

Diagnosing ozone (O₃) formation sensitivity using tropospheric observations of O₃ and its precursors is important for formulating O₃ pollution control strategies. Photochemical reactions producing O₃ occur at the earth’s surface and in the elevated layers, indicating the importance of diagnosing O₃ formation sensitivity at different layers. Synchronous measurements of tropospheric O₃ and its precursors nitrogen dioxide (NO₂) and formaldehyde (HCHO) were performed in urban Hefei to diagnose O₃ formation sensitivity at different atmospheric layers using multi-axis differential optical absorption spectroscopy observations. The retrieved surface NO₂ and O₃ were validated with in situ measurements (correlation coefficients (R) = 0.81 and 0.80), and the retrieved NO₂ and HCHO vertical column densities (VCDs) were consistent with TROPOMI results (R = 0.81 and 0.77). The regime transitions of O₃ formation sensitivity at different layers were derived using HCHO/NO₂ ratios and O₃ profiles, with contributions of VOC-limited, VOC-NO_x-limited, and NO_x-limited regimes of 74.19%, 7.33%, and 18.48%, respectively. In addition, the surface O₃ formation sensitivity between HCHO/NO₂ ratios and O₃ (or increased O₃, ΔO₃) had similar regime transitions of 2.21–2.46 and 2.39–2.71, respectively. Moreover, the O₃ formation sensitivity of the lower planetary boundary layer on polluted and non-polluted days was analyzed. On non-polluted days, the contributions of the VOC-limited regime were predominant in the lower planetary boundary layer, whereas those of the NO_x-limited regime were predominant in the elevated layers during polluted days. These results will help us understand the evolution of O₃ formation sensitivity and formulate O₃ mitigation strategies in the Yangtze River Delta region. Full article

The determination of the wave turbopause is vital for understanding the dynamics of atmospheric processes in the Mesosphere and Lower Thermosphere (MLT). In this study, we introduce a novel approach for identifying the wave turbopause, using SABER/TIMED temperature data and number density data, addressing the limitations associated with traditional linear fitting methods that can lead to ambiguities in results. Our approach is grounded in the conservation-of-energy principle, which facilitates the introduction of an energy index to effectively delineate the boundaries of the turbopause layer. This method allows us to define several key parameters: the lower boundary height, upper boundary height, turbopause height, and turbopause layer thickness. Analyzing long-term SABER data specifically over Beijing, we observed that the turbopause layer exhibited significant seasonal and inter-annual variations. Our findings indicated that the average height of the lower boundary was approximately 69.17 km, while the average height of the upper boundary was around 93.85 km. The energy index provided a comprehensive assessment of atmospheric wave activity, revealing periodic variations at different altitudes within the turbopause layer. The proposed method not only offers a more precise and applicable characterization of the turbopause but also enhances our capacity for atmospheric modeling and empirical investigations. Future work will focus on extending this methodology, to analyze the comprehensive SABER data collected globally. We aim to uncover insights into the seasonal characteristics of the turbopause across various geographic regions, allowing for a more detailed understanding of its behavior under different climatic conditions, ultimately contributing to a deeper understanding of MLT dynamics. Full article

The accurate forecasting of time, intensity, and spatial distribution is fundamental to weather prediction. However, the limitations of numerical weather prediction (NWP) models, as well as uncertainties in inital conditions, often lead to temporal biases in forecasts. This study addresses these biases by employing visible reflectance data from the Himawari-9/AHI satellite and RTTOV (TOVS radiation transfer) simulations derived from CMA-MESO model outputs. The time-shift method was applied to analyze two precipitation events—20 October 2023 and 30 April 2024—in order to assess its impact on precipitation forecasts. The results indicate the following: (1) the time-shift method improved cloud simulations, necessitating a 30 min advance for Case 1 and a 3.5 h delay for Case 2; (2) time-shifting reduced the standard deviation of observation-minus-background (OMB) bias in certain regions and enhanced spatial uniformity; (3) the threat score (TS) demonstrated an improvement in forecast accuracy, particularly in cases exhibiting significant movement patterns. The comparative analysis demonstrates that the time-shift method effectively corrects temporal biases in NWP models, providing forecasters with a valuable tool to optimize predictions through the integration of high-temporal- and spatial-resolution visible light data, thereby leading to more accurate and reliable weather forecasts. Full article

This work concerns the Taylor formula for the turbulence kinetic energy dissipation rate in the stable atmospheric boundary layer. The formula relates the turbulence kinetic energy dissipation rate to statistics at large scales, namely, the turbulence kinetic energy and the integral length scale. In parameterization schemes for atmospheric turbulence, it is usually assumed that the dissipation coefficient $C_{ϵ}$ in the Taylor formula is constant. However, a series of recent theoretical works and laboratory experiments showed that $C_{ϵ}$ depends on the local Reynolds number. We calculate turbulence statistics, including the dissipation rate, the standard deviation of fluctuating velocities and integral length scales, using observational data from the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition. We show that the dissipation coefficient $C_{ϵ}$ varies considerably and is a function of the Reynolds number, however, the functional form of this dependency in the stably stratified atmospheric boundary layer is different than in previous studies. Full article

A novel atmospheric pollutant transport quantification model (APTQM) has been developed to analyze and quantify cross-regional air pollutant transport pathways and fluxes. The model integrates high-resolution numerical simulations, Geographic Information System (GIS) capabilities, and advanced statistical evaluation metrics with boundary pixel decomposition methods to effectively characterize complex pollutant transport dynamics while ensuring computational efficiency. To evaluate its performance, the model was applied to a representative winter pollution event in Beijing in December 2021, using fine particulate matter (PM_2.5) as the target pollutant. The results underscore the model’s capability to accurately capture spatial and temporal variations in pollutant dispersion, effectively identify major transport pathways, and quantify the contributions of inter-regional sources. Cross-validation with established methods reveals strong spatial and temporal correlations, further substantiating its accuracy. APTQM demonstrates unique strengths in resolving dynamic transport processes within the boundary layer, particularly in scenarios involving complex cross-regional pollutant exchanges. However, the model’s reliance on a simplified chemical framework constrains its applicability to pollutants significantly influenced by secondary chemical transformations, such as ozone and nitrate. Consequently, APTQM is currently optimized for the quantification of primary pollutant transport rather than modeling complex atmospheric chemical processes. Overall, this study presents APTQM as a reliable and computationally efficient tool for quantifying inter-regional air pollutant transport, offering critical insights to support regional air quality management and policy development. Full article

Currently, large-scale burning is an important straw disposal method in most developing countries. To execute prescribed burning while mitigating air pollution, it is crucial to explore the maximum possible range of meteorological changes. This study conducted a three-year monitoring program in Changchun, a core agricultural area in Northeast China severely affected by straw burning. The data included ground-level pollutant monitoring, ground-based polarized LiDAR observations, and ground meteorological factors such as planetary boundary layer height (PBLH), relative humidity (RH), and wind speed (WS). Using response surface methodology (RSM), this study analyzed key weather parameters to predict the optimal range for emission reduction effects. The results revealed that PM_2.5 was the primary pollutant during the study period, particularly in the lower atmosphere from March to April, with PM_2.5 rising sharply in April due to the exponential increase in fire points. Furthermore, during this phase, the average WS and PBLH increased, whereas the RH decreased. Univariate analysis confirmed that these three factors significantly impacted the PM_2.5 concentration. The RSM relevance prediction model (MET-PM_2.5) established a correlation equation between meteorological factors and PM_2.5 levels and identified the optimal combination of meteorological indices: WS (3.00–5.03 m/s), RH (30.00–38.30%), and PBLH (0.90–1.45 km). Notably, RH (33.1%) emerged as the most significant influencing factor, while the PM_2.5 value remained below 75 μg/m³ when all weather indicators varied by less than 20%. In conclusion, these findings could provide valuable meteorological screening schemes to improve planned agricultural residue burning policies, with the aim of minimizing pollution from such activities. Full article

High-quality observational datasets are essential for climate research and models, but validating and filtering decades of meteorological measurements is an enormous task. Advances in machine learning provide opportunities to expedite and improve quality control while offering insight into non-linear interactions between the meteorological variables. The Cabauw Experimental Site for Atmospheric Research in the Netherlands, known for its 213 m observation mast, has provided in situ observations for over 50 years. Despite high-quality instrumentation, measurement errors or non-representative data are inevitable. We explore machine-learning-assisted quality control, focusing on wind vane stalling at 10 m height. Wind vane stalling is treated as a binary classification problem as we evaluate five supervised methods (Logistic Regression, K-Nearest Neighbour, Random Forest, Gaussian Naive Bayes, Support Vector Machine) and one semi-supervised method (One-Class Support Vector Machine). Our analysis determines that wind vane stalling occurred 4.54% of the time annually over 20 years, often during stably stratified nocturnal conditions. The K-Nearest Neighbour and Random Forest methods performed the best, identifying stalling with approximately 75% accuracy, while others were more affected by data imbalance (more non-stalling than stalling data points). The semi-supervised method, avoiding the effects of the inherent data imbalance, also yielded promising results towards advancing data quality assurance. Full article

This study presents a framework for integrating traffic simulation with high-resolution air pollution modeling to design adaptive traffic management policies aimed at reducing urban air pollution. Building on prior work that establishes the coupling of the MATSim traffic model with the PALM-4U urban climate model, this second part focuses on implementing a feedback loop to inform traffic management decisions based on simulated air pollution concentration levels. The research explores how traffic volumes and atmospheric conditions, such as boundary layer dynamics, influence air quality throughout the day. In an artificial case study of Berlin, a time-based toll is introduced, aimed at mitigating concentration peaks in the morning hours. The toll scheme is tested in two simulation scenarios and evaluated regarding the effectiveness of reducing air pollution levels, particularly NO₂ during the morning hours. The case study results serve to illustrate the framework’s capabilities and highlight the potential of integrating traffic and environmental models for adaptive policy design. The presented approach provides a model for responsive urban traffic management, effectively aligning transportation policies with environmental goals to improve air quality in urban settings. Full article

This study provides a comprehensive evaluation of the vertical accuracy of ERA5 reanalysis data for boundary layer height and key meteorological variables, based on high-precision observational data from Baotou, located on the Mongolian Plateau, during the winter (January–March) and summer (July–August) months of 2021. Results indicate that ERA5 exhibits significant biases in horizontal wind speed, with deviations ranging from −5 to 8 m/s at 50 m, primarily driven by sandstorms in winter and convective weather in summer. The most pronounced errors occur below 500 m. Vertical wind speeds are consistently underestimated in both seasons, with biases reaching up to 1 m/s, particularly during active summer convection. ERA5 also struggles to reproduce low-level wind directions accurately. In winter, correlation coefficients range from 0.43 to 0.64 below 200 m and improve to above 0.7 at 500 m. In summer, correlation coefficients are lower, ranging from 0.3 to 0.5 below 200 m, with reduced accuracy at 500 m compared to winter. Temperature deviations increase above 2000 m, with a relative overestimation of 3% at 3000 m. Relative humidity is generally overestimated by 5–20% between 1000 and 2000 m in winter and by 10–30% in summer. For boundary layer heights, ERA5 overestimates daytime mixed-layer heights by up to 2000 m in summer and 500–800 m in winter. In contrast, ERA5 captures nocturnal stable boundary layer heights well during winter. This comprehensive evaluation of the vertical structure accuracy of ERA5 reanalysis data, conducted in a heavily industrialized city on the Mongolian Plateau, offers essential insights for improving meteorological studies and refining climate models in the region. The findings provide valuable reference data for enhancing weather forecasting and supporting climate change research, particularly in complex terrain areas. Full article

Aerosol vertical stratification significantly influences the Earth’s radiative balance and particulate-matter-related air quality. Continuous vertically resolved observations remain scarce compared to surface-level and column-integrated measurements. This work presents and makes available a novel, long-term (2016–2022) aerosol dataset derived from continuous (24/7) vertical profile observations from three selected stations (Aosta, Rome, Messina) of the Italian Automated Lidar-Ceilometer (ALC) Network (ALICENET). Using original retrieval methodologies, we derive over 600,000 quality-assured profiles of aerosol properties at the 15 min temporal and 15 metre vertical resolutions. These properties include the particulate matter mass concentration (PM), aerosol extinction and optical depth (AOD), i.e., air quality legislated quantities or essential climate variables. Through original ALICENET algorithms, we also derive long-term aerosol vertical layering data, including the mixed aerosol layer (MAL) and elevated aerosol layers (EALs) heights. Based on this new dataset, we obtain an unprecedented, fine spatiotemporal characterisation of the aerosol vertical distributions in Italy across different geographical settings (Alpine, urban, and coastal) and temporal scales (from sub-hourly to seasonal). Our analysis reveals distinct aerosol daily and annual cycles within the mixed layer and above, reflecting the interplay between site-specific environmental conditions and atmospheric circulations in the Mediterranean region. In the lower troposphere, mixing processes efficiently dilute particles in the major urban area of Rome, while mesoscale circulations act either as removal mechanisms (reducing the PM by up to 35% in Rome) or transport pathways (increasing the loads by up to 50% in Aosta). The MAL exhibits pronounced diurnal variability, reaching maximum (summer) heights of >2 km in Rome, while remaining below 1.4 km and 1 km in the Alpine and coastal sites, respectively. The vertical build-up of the AOD shows marked latitudinal and seasonal variability, with 80% (30%) of the total AOD residing in the first 500 m in Aosta-winter (Messina-summer). The seasonal frequency of the EALs reached 40% of the time (Messina-summer), mainly in the 1.5–4.0 km altitude range. An average (wet) PM > 40 μg m⁻³ is associated with the EALs over Rome and Messina. Notably, 10–40% of the EAL-affected days were also associated with increased PM within the MAL, suggesting the entrainment of the EALs in the mixing layer and thus their impact on the surface air quality. We also integrated ALC observations with relevant, state-of-the-art model reanalysis datasets (ERA5 and CAMS) to support our understanding of the aerosol patterns, related sources, and transport dynamics. This further allowed measurement vs. model intercomparisons and relevant examination of discrepancies. A good agreement (within 10–35%) was found between the ALICENET MAL and the ERA5 boundary layer height. The CAMS PM₁₀ values at the surface level well matched relevant in situ observations, while a statistically significant negative bias of 5–15 μg m⁻³ in the first 2–3 km altitude was found with respect to the ALC PM profiles across all the sites and seasons. Full article

In this study, a drone-based measurement system equipped with miniaturized optical and condensation particle counters was deployed to investigate the vertical distribution of particulate matter and meteorological variables up to 2.5 km in altitude. Measurements captured at various altitudes demonstrated notable vertical variations in particle concentration and significant correlations with meteorological factors, particularly relative humidity (RH). Near the surface, within a well-mixed boundary layer, particle concentrations remained stable despite RH changes, indicating both anthropogenic and natural influences. At higher altitudes, a clear positive relationship between RH and particle number concentration emerged, particularly for smaller particles, while temperature inversions and distinct wind patterns influenced aerosol dispersion. The unmanned aerial vehicle system’s robust performance, validated against standard meteorological tower data, underscores its potential for high-resolution atmospheric profiling. These insights are crucial for understanding particle behavior in diverse atmospheric layers and have implications for refining air quality monitoring and climate models. Future work should incorporate chemical analysis of aerosols to further expand these findings and assess their environmental impact. Full article

An effective approach to deal with the structural failures of transmission towers in tornadic events is to develop good structural health monitoring (SHM) systems for them. However, the strategy for SHM of transmission towers against tornados should be different from the conventional atmospheric boundary layer (ABL) winds oriented ones, as the non-stationary nature of the tornados significantly differentiates them from the ABL winds. To satisfy the need of obtaining the highly time-varying whole-field stress on the structure in the course of the tornadic event for effective SHM, an innovative transient tornadic load distribution identification method is proposed for use, which is based on the field structural mode shape measurement and dynamic finite-element (FE) model updating. Via a numerical case study, it is noted that good effectiveness is achieved for the new load distribution identification method. Employing the Modal Assurance Criteria based FE model updating technique, the new method has the advantage of being easily embraced in practical SHM systems. It is found that when the transient tornadic velocity profile to be identified is noticeably different from the mode shape of the structure without undertaking external loads, the identified load pattern is very accurate for the new approach. Full article

Coastal lagoons play an essential role in the energy balance and heat exchange to the atmosphere. Furthermore, at mesoscale Monsoon systems and at local scales, sea breeze influences surface processes; however, there is a lack of information on such processes in arid and semiarid regions. We aimed to characterize the atmospheric conditions during sea and land breeze in different seasons and analyze at different temporal scales the variation of atmospheric stability, turbulent fluxes, lifting condensation level, and atmospheric boundary layer height. The study site is a subtropical semiarid coastal lagoon, Estero El Soldado, located in Northwestern Mexico (27°57.248′ N, 110°58.350′ W). Measurements were performed from January 2019 to September 2020 with an Eddy Covariance system (EC) and micrometeorological instruments over the water surface. Results show that there is a strong seasonality that enhances sea–land breeze dominance; sea breeze was 83% more frequent during the Monsoon, and the land breeze was 55% more frequent in the Post-Monsoon. Specific humidity (23.32 ± 3.84 g kg⁻¹, q), potential temperature (307 ± 2.98 K, θ_p), latent heat (135 W m⁻², LE), and turbulent kinetic energy (0.81 m² s⁻², TKE) were significantly higher during the Monsoon season at sea breeze events. Atmospheric boundary layer (ABL) and lifting condensation level (LCL) were higher in the Pre-Monsoon season (3250 ± 71 m and 1142 ± 565 m, respectively). During the Monsoon, surface conditions lead to lower LCL (~800 m) due to the amount of water vapor (q = 23.3 g kg⁻¹). Full article