Search Results (274)

The performance of FastEddy, a GPU-based large eddy simulation model, in simulating building-induced turbulent flow in an operating airport is studied for the first time through four examples, including a super typhoon case at Hong Kong International Airport (HKIA) and a real case of low-level wind effect. The simulation results are quantitatively compared with wind observations from Light Detection and Ranging (LIDAR) systems for selected cases, and with aircraft data and pilot reports in one example of low-level wind effect. The FastEddy model is found to perform reasonably well through these case studies, even for the radial component of the winds exceeding 20 m/s in a highly turbulent airflow simulation of a typhoon, as well as turbulent airflow features in a building complex at and around HKIA. The building-induced turbulent flow as observed by the LIDARs and the aircraft are largely reproduced. The scatter plots of the model-simulated and the observed Doppler velocities have good correlation in terms of the slope of the best-fit linear equation, correlation coefficient and root-mean-square difference. Moreover, for the case of low-level wind effect, FastEddy simulation is found to provide useful insight into the turbulent flow arising from the new terminal building over the northeastern part of HKIA (near 22.325° N 113.918° E) under construction. Further research directions for studying the performance of FastEddy are also discussed, such as considering more complex urban environments, comparison with in situ measurements of anemometers, and direct output of the eddy dissipation rate (EDR) from the model for comparing with LIDAR and anemometer-based measurements. Full article

The humid coastal Lingnan region of South China, including the Chaoshan area of eastern Guangdong, is frequently exposed to disaster-prone weather conditions such as high humidity, typhoon-related winds, heavy rainfall, and salt-laden coastal air. These long-term environmental exposures may contribute to surface deterioration risks of architectural heritage. Located in Shantou, Yingchuan Shijia has shown five visible surface deterioration types—cracks, staining, saltpetering, plants, and spalling—under the combined influence of environmental exposure, material aging, previous disturbance, and insufficient maintenance. To address the limitations of manual inspection, this study explores a conservation-oriented intelligent workflow integrating YOLO-based detection, digital documentation, and screening-level conservation interpretation. Digital documentation used UAV imagery, mobile LiDAR scanning, measured drawings, and SketchUp-based three-dimensional modeling. The dataset was built in three stages: a 99-image preliminary dataset, where YOLOv8 showed only basic learning capability with low performance metrics, including Precision of 33.0 ± 3.0%, Recall of 28.0 ± 1.0%, mAP50 of 25.0 ± 1.0%, and mAP50-95 of 11.0 ± 1.0%; a 362-image non-augmented case-study dataset, where YOLOv8 still showed limited performance, with mAP50 of 20.0 ± 1.0% and mAP50-95 of 8.0 ± 1.0%; and a final YOLO-format case-study dataset of 2000 images after training-set-only augmentation using 11 geometric and photometric transformation methods. After augmentation, YOLOv8 mAP50 increased to 62.0 ± 2.0%. Under the same augmented-data condition, YOLOv13 showed Precision of 89.0 ± 1.0%, Recall of 77.0 ± 1.0%, mAP50 of 84.0 ± 1.0%, and mAP50-95 of 65.0 ± 1.0%, indicating relatively higher validation performance than YOLOv8. In the normalized confusion matrix, the background missed-detection values for cracks and saltpetering were 0.29 and 0.22, respectively, indicating that weak-feature and low-contrast deterioration types remained challenging. Based on YOLOv13, a mini program was developed to organize detection outputs and provide field-oriented preliminary conservation hints. Overall, this study provides a preliminary workflow linking digital collection, image-based deterioration detection, Grad-CAM visualization, and assisted field recording for the preventive conservation of Chaoshan overseas-Chinese residences in humid coastal heritage environments. Full article

Accurate wind estimation is essential for wind resource assessment. In this study, using scanning lidar measurements and high-resolution WRF simulations from two nearshore areas in Japan, we developed two extensions of the Temporal Correction (TC) method, which corrects wind fields generated by the Weather Research and Forecasting (WRF) model using on-site measurements. First, when using a single measurement point for correction, we derived two empirical formulas to predict appropriate correction coefficients based on reference–target correlation coefficients of wind speed obtained from WRF simulations and developed a method (TC-pred) using these formulas. TC-pred was shown to have higher wind speed estimation accuracy and a broader range of applicability than the conventional TC method. Next, we extended the TC-pred method to allow the use of multiple measurement points as references by introducing a weighting formula for each reference point. Wind speed estimation accuracy improved as the number of reference points increased, primarily because the probability of including reference points with high reference–target correlation coefficients increased. This suggests that it is effective for the suppression of wind estimation uncertainty to determine measurement layout such that the correlation coefficient between at least one reference point and each target point in the target area exceeds a certain value. Full article

Accurate measurements of wind fields in the troposphere and stratosphere are essential for advancing atmospheric dynamics research, improving weather prediction, and supporting aerospace operations. However, a single Doppler lidar technique usually has limited capability to provide vertically extended wind profiles across both aerosol-rich lower altitudes and molecular-dominated higher altitudes. In this paper, we present a hybrid Doppler lidar system that combines a 355 nm scanning incoherent Rayleigh Doppler lidar with a 1550 nm coherent aerosol Doppler lidar for multi-scale wind field detection. The coherent Doppler lidar is used for boundary-layer wind retrievals, while the Rayleigh Doppler lidar, based on the double-edge technique, extends wind profiling from the upper boundary layer to approximately 40 km. Field deployments demonstrate continuous wind profiling from 50 m to 40 km, extending from the boundary layer to the stratosphere. Comparisons with radiosonde measurements show good agreement during the field campaigns, supporting the feasibility of this hybrid configuration for vertically extended wind profiling. The resulting high-resolution wind measurements across multiple atmospheric regions provide valuable data sources for studies of multi-scale circulation research, gravity wave dynamics, and climate-related atmospheric processes. Full article

Accurate validation of spaceborne lidar data is fundamental for reliable quantification of aerosol vertical distributions, which strongly influence air quality and climate effects. This study presents a comparative analysis of aerosol profiles from the 532 nm High-Spectral-Resolution Lidar (HSRL) onboard China’s DQ-1 satellite (ACDL) and ground-based observations from the Asian Dust and Aerosol Lidar Observation Network (AD-Net). Using one year of measurements under minimized spatiotemporal mismatches at three representative coastal stations (Matsue, Tokyo, Hedo), we quantify the sources of observational differences. Results show that discrepancies in detection targets (aerosols/clouds) dominate the total variance (>75%), while instrumental differences contribute 10–25%. Horizontal wind speed, particularly its north–south component, correlates more strongly with discrepancies than vertical wind speed, except in high-concentration aerosol layers where vertical motions become influential. Furthermore, larger differences are associated with increased aerosol extinction coefficients (α) and particle depolarization ratios (δ). This work demonstrates that integrated applications of multi-platform lidar data must account for both meteorological controls on aerosol transport and particle microphysical properties. These findings provide a quantitative validation framework for current and future spaceborne HSRL missions and support the integrated application of multi-platform lidar observations in regional aerosol monitoring, air quality assessment, and climate effect research. Full article

Industrial activities represent the primary source of anthropogenic carbon dioxide (CO₂) emissions, and accurate monitoring of industrial CO₂ emissions is critical to mitigating greenhouse gas emissions. Due to the lack of quantifiable and direct measurement technologies, industrial CO₂ emissions are typically calculated based on fuel combustion consumption and emission factors. However, the calculation method is not applicable to the quantification of fugitive emissions of CO₂. This work demonstrates the capability of remotely measuring industrial CO₂ emissions by mobile Differential Absorption Lidar (DIAL) system. The two-dimensional concentration distributions of the CO₂ plume were remotely measured using DIAL system, and the CO₂ emission rate was obtained with wind field information. The DIAL measurements were cross-validated using in-stack CEMS data and emission-factor calculations. Results show that the relative deviations of CO₂ emission rates between DIAL and CEMS range from −5.83% to +2.57% across four tests, all within ±6%, and the coefficient of variation (CV) of 27 valid datasets is 7.24%. In contrast, the emission factor method yields consistently higher estimates, with relative deviations of +4.61% compared to DIAL measurements. Furthermore, the mobile DIAL system was deployed in three industrial scenarios with different emission intensities: a natural gas-fired industrial park, a photovoltaic glass manufacturing plant (low-emission steady-state), and a coal-fired power plant (high-emission dynamic), demonstrating its preliminary adaptability under different operating conditions. This study indicates the feasibility and potential reliability of the mobile DIAL system for high spatio-temporal resolution remote measurement of industrial CO₂ emissions. Full article

Tree mechanical stability is essential for forest management and urban safety. Although static pulling tests are currently the standard for non-destructive advanced risk assessments, these tests have significant methodological limitations. Large trees require high applied forces to produce measurable signals, which poses safety risks and causes equipment wear. Conversely, structurally compromised ancient, veteran, or dead trees (snags) may yield poor signal-to-noise ratios at low loads, leading to unstable model fits and unreliable safety factor extrapolations. Additionally, standard inclinometers often experience interference from motion-induced accelerations. This study introduces a high-resolution, low-noise measurement approach that resolves small basal inclinations and stem bending responses. This method uses laser-based tracking to monitor stem bending, torsion, and inclination under mechanical load. Experimental data were collected by combining traditional pulling tests with this novel system, as well as by conducting a pilot study that monitored tree movement during low-strength wind gusts. The proposed method enables more precise characterization of the initial load-response curve. Improving the signal-to-noise ratio at lower force levels allows for more robust safety extrapolations. When combined with a 3D LiDAR scan, the method can reveal deviations from the theoretical bending line in order to locate internal defects and variations in wood properties. These findings bridge a critical gap in tree risk assessment by improving the applicability of static testing to massive trees, as well as ecologically valuable yet structurally vulnerable snags and ancient and veteran trees. Full article

This study investigates the wind field characteristics of long-span suspension bridges in mountain valleys terrain, with a particular focus on the disturbance effects caused by bridge structure on wind measurements. Field data are collected using the Wind3D 6000 LiDAR installed near the bridge. By comparing wind field characteristics before and after bridge completion, this study evaluates the influence of the bridge structure on both mean and turbulent wind characteristics. The findings show that the presence of the bridge tower and deck reduces the measured mean wind speed and modifies its probability distribution. The bridge tower increases the effective ground roughness coefficient, thereby attenuating the vertical wind speed gradient. In addition, the bridge tower raises the measured turbulence intensity, alters its probability distribution, and decreases the agreement between the turbulent wind power spectrum and the von Kármán spectrum. It is necessary to correct the data affected by these disturbances to improve the accuracy of wind load assessments for long-span bridges, thus enhancing the reliability of bridge structural operation. Full article

The 589 nm three-frequency lidar systems are widely employed for detecting atmospheric parameters in the mesosphere and lower thermosphere (MLT). Recently, the single-peak atomic frequency discriminator (SPAFD) has enabled 589 nm three-frequency lidars to measure wind fields in the stratosphere and mesosphere. However, research on their application for near-surface aerosol measurements remains limited. This paper proposes a method for diurnal aerosol detection using the 589 nm three-frequency lidar integrated with SPAFD. The specific configuration of the lidar system is described in detail, along with the retrieval algorithm for aerosol optical parameters derived from the three-frequency backscatter signals. Continuous 69-h observation results of the aerosol backscatter ratio are provided, followed by an analysis of the diurnal evolution of the planetary boundary layer (PBL) height. This approach enables existing 589 nm lidar systems to acquire aerosol diurnal detection capabilities without additional hardware costs or operational expenses. At present, the retrieval of aerosol extinction coefficients is constrained to altitudes above 10 km due to geometric overlap factor limitations. To overcome this, a dedicated low-altitude detection channel will be integrated in future iterations to enable full-altitude measurements. This advancement will establish the 589 nm lidar as a highly efficient tool for full-altitude, diurnal atmospheric detection. Full article

The vertical distribution of atmospheric aerosol layers plays a fundamental role in understanding their climatic and environmental effects. Using one year of lidar observations in Jinhua, together with ground-based meteorological measurements and ERA5 reanalysis data, this study develops an integrated analytical framework to investigate the structural characteristics of aerosol layers in Mie-scattering profiles and their meteorological driving factors. K-means clustering identifies three representative aerosol layer structure types: single-layer concave, single-layer convex, and multi-layer profiles. By combining the Boruta algorithm with a random forest model, the dominant meteorological factors associated with each structure type are quantified across four boundary-layer stages (00–06, 06–12, 12–18, 18–24 LT). Temperature, humidity, wind speed, wind direction, divergence, and vertical velocity exhibit distinct influences across different boundary-layer conditions, revealing differentiated regulatory mechanisms governing aerosol layer structure change. The proposed framework establishes a coupled perspective between atmospheric dynamic/thermodynamic processes and aerosol layer structure formation, providing a basis for refined modeling of aerosol evolution and improved understanding of aerosol–meteorology interactions. Full article

This study evaluates the applicability of ERA5 wind speed (WS) and wind direction (WD) in the East China Sea, using high-resolution vertical wind profiles measured by a floating LiDAR at the Shanghai Nanhui Offshore Wind Farm from 15 January 2022 to 15 January 2023. Key findings are as follows: (1) Strong positive correlations exist between LiDAR-measured and ERA5 WS across all evaluated heights, with correlation coefficients of 0.76 (ground level), 0.86 (50 m), 0.88 (100 m), and 0.90 (200 m), respectively, and corresponding root mean square errors (RMSEs) of 2.33 m/s, 1.78 m/s, 1.73 m/s, and 1.77 m/s. This systematic improvement in correlation and modest reduction in RMSE with increasing height indicate that ERA5 captures vertical wind structure with progressively higher fidelity above the surface layer. (2) Both the ERA5 dataset and LiDAR measurements consistently show dominant wind frequencies in the NNE and SSE directions, with peaks at approximately 1000 occurrences. The minimal differences in the two datasets demonstrate the ERA5’s robust representation of near-surface offshore WD climatology. (3) The ERA5 reanalysis data of typhoon Muifa can better illustrate the increase in the initial WS and its subsequent decreases. However, the peak WS lags behind measurements by 2 h, and the extreme WS is significantly lower than that measured. Evaluations of the multi-year return period WS demonstrate an underestimation of extreme WS by 16.06–16.51% for the ERA5 data. Regarding the WD, the measured direction is clockwise, while that of the ERA5 is counterclockwise, revealing a fundamental deficiency in its representation of mesoscale cyclonic wind structure. Therefore, ERA5 reanalysis data provides reliable characterization of typical offshore WS and WD within the operational wind turbine hub-height range (100–200 m). For typhoon-related wind engineering assessments, the applicability of ERA5 data necessitates caution and potentially bias correction. Full article

Accurate inflow wind conditions are essential for operational wind farms. However, wind conditions from the Supervisory Control and Data Acquisition (SCADA) system are significantly affected by rotor-induced disturbances and thus cannot reliably represent the true inflow. Although LiDAR can directly measure inflow wind conditions, its data availability is highly sensitive to environmental conditions, frequently leading to insufficient valid samples. Existing studies generally apply the Nacelle Transfer Function (NTF) to empirically correct SCADA wind speed, yet its accuracy remains limited. Consequently, this study proposes a deep learning model for LiDAR-referenced inflow wind condition estimation from SCADA data. First, variations in LiDAR data availability and their influencing factors are systematically analyzed. The deviations and correlations between SCADA data and LiDAR measurements are quantitatively characterized. Subsequently, a deep learning model is developed, employing a time–frequency dual-branch residual network to extract features from SCADA data, while incorporating the Gram matrix as an additional input to provide auxiliary information. Finally, the proposed method is validated using measurements from two offshore turbines with different rated capacities. The results demonstrate that the proposed approach outperforms comparative methods, enabling more accurate estimation of inflow wind speed and direction. Full article

The scarcity of global wind field data limits the accuracy of numerical weather prediction. The currently operational spaceborne Doppler lidar (the European Space Agency’s Aeolus) measures only a single line-of-sight (LOS) wind component, which leads to discrepancies between the measured results and the real wind field. The systems of the United States and Japan have provided additional LOS wind measurements. Yet residual errors in correcting for the satellite’s own velocity can still degrade the accuracy of the retrieved wind vectors. To enhance the accuracy and timeliness of global wind observations, we propose a dual-spaceborne Doppler lidar wind measurement system. Two satellite orbits with different inclinations each provide a LOS wind; combining these components at each crossover yields the horizontal wind vector. Thereby, within 12 h, the crossovers blanket the globe, yielding a global horizontal wind-vector field. Orbital simulations show that inclinations summing to 180° produce the most uniform crossover-point distribution. As Satellite-1’s inclination (prograde orbit) increases, the latitudinal coverage of crossover points expands accordingly. The preferred configuration is when the two satellites have inclinations of 70° and 110°, respectively. Their ground tracks cover nearly all major global landmasses, with a symmetrical distribution of intersection points and a balanced grid resolution. As satellite technology further matures, this dual-spaceborne approach is expected to supplement global horizontal wind-field data. Full article

This study evaluates the impact of measurement campaign duration on wind resource characterization using three MCP (Measure–Correlate–Predict) models: Total Least Squares (TLS), Multiple Linear Regression (LR), and Quantile Gradient Boosting (GB). The analysis is based on data from 30 meteorological masts (nine primary and twenty-one secondary masts) installed worldwide across different terrains, with up to twenty-seven months of concurrent wind measurements between primary and secondary masts. Fixed campaign durations of 3, 4, 5, 6, 9, and 12 months were simulated using moving intervals to quantify the effect of measurement length on mean wind speed estimation. This working framework also serves to represent conditions typical of campaigns where LIDAR systems are used to complement meteorological mast deployments, as LIDAR units generally operate for shorter periods due to frequent relocation as part of broader measurement strategies. Wind speed estimation was assessed through metrics such as Mean Absolute Error (MAE), relative uncertainty, and monthly uncertainty reduction, taking into account terrain complexity and correlation coefficient ($R^2$) between masts. Results indicate that extending the measurement period improves the accuracy and consistency of wind speed estimates, with significant reductions in uncertainty observed after six months. Across sites, the average monthly uncertainty reduction ranges from 0.13% to 0.41% of the mean wind speed per additional month of measurements, depending on terrain complexity and inter-mast correlation. Linear models (TLS and LR) consistently show better performance in terms of error and uncertainty reduction compared to GB. Based on an extensive and diverse MCP dataset covering multiple terrains and locations, this study provides empirically derived monthly uncertainty-reduction benchmarks for campaign-length optimisation under different site conditions, contributing to more reliable wind resource assessments and, consequently, energy yield estimates. Full article

Accurate wind resource assessment is critical for the effective planning of wind farms, as well as for forecasting production values to ensure grid stability, yet it remains a complex challenge. This study evaluates the robustness of the Norwegian reanalysis model (NORA3) as a wind assessment tool specifically for the Baltic Sea region. The NORA3 model was validated by comparing it to observation data from four onshore locations in Latvia, collected from meteorological masts and a lidar wind measurement device. The evaluation applied correlation analysis, wind distribution and wind rose comparisons, and annual energy production (AEP) estimates. Results reveal high similarity between NORA3 and observation datasets in terms of wind speed correlation and distribution, while wind roses feature significant differences, especially for short-term observations. AEP estimates based on the NORA3 dataset are more optimistic compared to the actual observations for all investigated locations. Full article