Wind Velocity Measurements Research Articles

Research on optimizing the optical local-oscillator power of Coherent Doppler LiDAR to enhance wind velocity measurements

Due to weak echo signals that become progressively overwhelmed by noise, measurement accuracy and effective detection range of the Coherent Doppler wind LiDAR (CDL) are often compromised. While increasing the optical local-oscillator power (OLP) can amplify the echo signal, it is constrained by the nonlinear effects of the detector. This paper introduces a method for optimizing the OLP in CDL systems. Theoretical analysis has been proposed to explore the amplification effect of OLP on echo signals, and the nonlinear effects of detectors have been studied. Simulations are performed to explore the influence of varying OLP on the signal-to-noise ratio (SNR) across different detector α (quadratic nonlinear coefficient) values. The spectral analysis method is used to directly compute the SNR of actual atmospheric wind field signals under various OLP settings. Results demonstrate consistency between calculated and simulated values, enabling determination of optimal OLP and the detector α values from fitted curves. Comparative experiments confirm significant enhancement in effective detection range (> 1.5 km) with ± 0.5 m/s accuracy. The innovation of this study lies in combining the OLP optimization method with real atmospheric wind field echo signal experiments. It addresses the challenges of directly measuring the nonlinear parameters of the detector and determining the optimal OLP. This study offers valuable theoretical and experimental insights for enhancing the wind measurement performance of CDL.

Exploration of moiré tomography in spatial distribution measurement of wind velocity

In this paper, moiré tomography is applied to measure the spatial velocity distribution of thermal wind field. To verify the feasibility of our method, three sets of experiments are carried out, each lasting for 5 min. And then, its spatial distribution is obtained based on the theoretical relation between the refractive index extracted from moiré fringes and the wind velocity. Finally, some validation is performed for the reliability and accuracy of the method. The radial distribution of wind velocity matches well with those estimated results based on a reported theoretical model. Besides, the general trend of wind velocity values at different times of moiré tomography measurement is almost consistent with those recorded by the anemometer, and the relative error between them maintains within 3 %. In further, some reasonable analysis of the factors contributing to error is provided. In a word, this paper demonstrates that moiré tomography has the potential application to offer a feasible means for the spatial measurement of wind velocity.

Comparing triple and single Doppler lidar wind measurements with sonic anemometer data based on a new filter strategy for virtual tower measurements

Abstract. In this study, we compare the wind measurements of a virtual tower triple Doppler lidar setup to those of a sonic anemometer located at a height of 90 m above ground on an instrumented tower and with those of two single Doppler lidars to evaluate the effect of the horizontal homogeneity assumption used for single Doppler lidar applications on the measurement accuracy. The triple lidar setup was operated in a 90 m stare and a step–stare mode at six heights between 90 and 500 m above ground, while the single lidars were operated in a continuous scan velocity–azimuth display (VAD) mode where one of them had a zenith angle of 54.7° and the other one of 28.0°. The instruments were set up at the boundary-layer field site of the German Meteorological Service (DWD) in July and August of 2020 during the FESST@MOL (Field Experiment on sub-mesoscale spatiotemporal variability at the Meteorological Observatory Lindenberg) 2020 campaign. Overall, we found good agreement of the lidar methods for the whole study period for different averaging times and scan modes compared to the sonic anemometer. For the step–stare mode wind speed measurements, the comparability between the triple lidar and the sonic anemometer was 0.47 m s−1 at an averaging time of 30 min with a bias value of −0.34 m s−1. For wind speed measured by one single lidar setup for the same period with an averaging time of 30 min, we found a comparability of 0.32 m s−1 at an averaging time of 30 min and a bias value of −0.07 m s−1 as well as values of 0.47 and −0.34 m s−1 for the other one, respectively. We also compared the wind velocity measurements of the single and triple lidars at different heights and found decreasing agreement between them with increasing measurement height up to 495 m above ground for the single lidar systems. We found that the single Doppler lidar with the increased zenith angle produced poorer agreement with the triple Doppler lidar setup than the one with the lower zenith angle, especially at higher altitudes. At a height of 495 m above ground and with an averaging time of 30 min the comparability and bias for the larger zenith angle were 0.71 and −0.50 m s−1, respectively, compared to values of 0.57 and −0.28 m s−1 for the smaller zenith angle. Our results confirm that a single Doppler lidar provides reliable wind speed and direction data over heterogeneous but basically flat terrain in different scan configurations. For the virtual tower scanning strategies, we developed a new filtering approach based on a median absolute deviation (MAD) filter combined with a relatively relaxed filtering criterion for the signal-to-noise ratio output by the instrument.

Toward on-demand measurements of greenhouse gas emissions using an uncrewed aircraft AirCore system

Abstract. This paper evaluates the performance of a multirotor uncrewed aircraft and AirCore system (UAAS) for measuring vertical profiles of wind velocity (speed and direction) and the mole fractions of methane (CH4) and carbon dioxide (CO2), and it presents a use case that combines UAAS measurements and dispersion modeling to quantify CH4 emissions from a dairy farm. To evaluate the atmospheric sensing performance of the UAAS, four field deployments were performed at three locations in the San Joaquin Valley of California where CH4 hotspots were observed downwind of dairy farms. A comparison of the observations collected on board the UAAS and an 11 m meteorological tower show that the UAAS can measure wind velocity trends with a root mean squared error varying between 0.4 and 1.1 m s−1 when the wind magnitude is less than 3.5 m s−1. Findings from UAAS flight deployments and a calibration experiment also show that the UAAS can reliably resolve temporal variations in the mole fractions of CH4 and CO2 occurring over periods of 10 s or longer. Results from the UAAS and dispersion modeling use case further demonstrate that UAASs have great potential as low-cost tools for detecting and quantifying CH4 emissions in near real time.

Experimental investigation into the effects of endplate designs for a Savonius turbine

Abstract Wind energy is experiencing a trend of exponential growth in both literature and industrial employment, emphasizing its current pivotal role in achieving carbon neutrality by replacing fossil fuels. Vertical axis wind turbines (VAWTs) particularly Savonius rotors operate at a lower tip-speed-ratio (TSR) range compared to horizontal axis wind turbines (HAWTs). The Savonius rotor has good starting characteristics, making it suitable to be placed in urban areas with lower wind speeds. Due to its low efficiency, studies on augmentation and optimization have been conducted to improve its blade and deflector designs. Extensive research has established a common consensus that the use of endplates significantly improves the aerodynamic performance of the Savonius rotor. However, limited research has been undertaken to explore the endplate design further. In this study, wind tunnel tests were conducted on different endplate ratios, to investigate the effects of endplate design on the aerodynamic performance of a conventional Savonius turbine. Three different Savonius rotor with no endplate, semi-circular endplate and circular endplate were designed and manufactured. Experiments were conducted in an open-type suction wind tunnel at a constant measured wind velocity of 5.89 m/s. The turbine’s performance for different endplate ratios was evaluated across a TSR range of 0.2 to 1.0. The performance was assessed based on the maximum power generated and its self-starting ability. From the experimental results, the circular endplate ratio of 1.1 shows a significantly high coefficient of power compared to both semi-circular and without endplates. This is due to reduced spanwise spillage and enhanced airflow capture by the rotor. The endplates direct incoming air to the advancing blade and overlap region, resulting in the increased pressure difference between the concave and convex sides of both the advancing and returning blade, thereby improving the power efficiency of the turbine with 1.1D diameter endplate and semi-circular endplate by 296.6% and 6.9% compared to no endplates case.

Wind farm power curve characterization under different atmospheric stability regimes

Wind farm power curves are particularly relevant in different applications. A better understanding of wind farm power curves under different atmospheric regimes is sought. In the present work, the atmospheric stability regime was identified by the vertical gradient of the potential temperature. Vertical profile of the wind velocity presented a clearly different pattern during the stable and unstable regimes. Analysis was performed considering 5 years of data from three wind farms, each corresponding to different mesoscale wind. Power curves are presented as a function of atmospheric stability. For each wind farm, we utilized wind velocity and temperature data measurements of a correspondent near the tower location. Clearly different patterns of wind farm power curves were observed as a function of the stability regime, with clear differences in the dependence of mesoscale wind regimes. For the same mean velocity, wind power production is greater for higher values of turbulence intensity TI.

Rotary-wing drone-induced flow – comparison of simulations with lidar measurements

Abstract. Ultrasonic anemometers mounted on rotary-wing drones have the potential to provide a cost-efficient alternative to the classical meteorological mast-mounted counterpart for atmospheric boundary layer research. However, the propeller-induced flow may degrade the accuracy of free-stream wind velocity measurements by wind sensors mounted on drones – a fact that needs to be investigated for optimal sensor placement. Computational fluid dynamics (CFD) simulations are an alternative to experiments for studying characteristics of the propeller-induced flow but require validation. Therefore, we performed an experiment using three short-range continuous-wave Doppler lidars (light detection and ranging; DTU WindScanners) to measure the complex and turbulent three-dimensional wind field around a hovering drone at low ambient wind speeds. Good agreement is found between experimental results and those obtained using CFD simulations under similar conditions. Both methods conclude that the disturbance zone (defined as a relative deviation from the mean free-stream velocity by more than 1 %) on a horizontal plane located at 1 D (rotor diameter D of 0.71 m) below the drone extends about 2.8 D upstream from the drone center for the horizontal wind velocity and more than 7 D for the vertical wind velocity. By comparing wind velocities along horizontal lines in the upstream direction, we find that the velocity difference between the two methods is ≤ 0.1 m s−1 (less than a 4 % difference relative to the free-stream velocity) in most cases. Both the plane and line scan results validate the reliability of the simulations. Furthermore, simulations of flow patterns in a vertical plane at the ambient speed of 1.3 m s−1 indicate that it is difficult to accurately measure the vertical wind component with less than a 1 % distortion using drone-mounted sonic anemometers.

Demonstration on the performance enhancement of the 1.645 µm coherent Doppler lidar for long-range wind measurements with modification of laser transmitter and optical antenna

We have developed and experimentally investigated a long-range 1.645 µm coherent Doppler wind lidar (CDWL) system. A compact 1.645 µm single-frequency Er:YAG laser is utilized as the laser transmitter. The impact of laser transmitter parameters on wind detection was assessed using the figure of merit (FOM) concept. To enhance the measurement efficiency, the influence of wave aberrations on the heterodyne efficiency was analyzed. A Galilean telescope with an optical aperture of 100 mm is designed as the optical antenna based on the analysis. The line of sight (LOS) detection range exceeds 30.42 km with a data rate of 1 Hz at an elevation angle of 3.5°. To evaluate the effectiveness of the CDWL, comparison experiments were conducted between the 1.645 µm CDWL and a calibrated 1.55 µm CDWL, revealing a correlation coefficient of 0.9816 for the whole detection path in the wind velocity measurement.

Enhancing wildfire propagation model predictions using aerial swarm-based real-time wind measurements: A conceptual framework

The dynamic behaviour of wildfires is mainly influenced by weather, fuel, and topography. Based on fundamental conservation laws involving numerous physical processes and large scales, atmospheric models require substantial computational resources. Therefore, coupling wildfire and atmospheric models is impractical for high resolutions. Instead, a static atmospheric wind field is typically input into the wildfire model, either as boundary conditions on the control surface or distributed over the control volume. Wildfire propagation models may be (i) data-driven; (ii)theoretical; or (iii)mechanistic surrogates. Data-driven models are beyond the scope of this paper. Theoretical models are based on conservation laws (species, energy, mass, momentum) and are, therefore, computationally intensive; e.g. the Fire Dynamics Simulator (FDS). Mechanistic surrogate models do not closely follow fire dynamics laws, but related laws observed to make predictions more efficiently with sufficient accuracy; e.g. FARSITE, and FDS with the Level Set model (FDS-LS). Whether theoretical or mechanistic surrogate, these wildfire models may be coupled with or decoupled from wind models (e.g. Navier-Stokes equations). Only coupled models account for the effect of the fire on the wind field. In this paper, a series of simulations of wildfire propagation on grassland are performed using FDS-LS to study the impact of the fire-induced wind on the fire propagation dynamics. Results show that coupling leads to higher Rates of Spread (RoS), closer to those reported from field experiments, with increasing wind speeds and higher terrain slopes strengthening this trend. Aiming to capture the fire–wind interaction without the hefty cost of solving Navier-Stokes equations, a conceptual framework is proposed: 1) a swarm of unmanned aerial vehicles measure wind velocities at flight height; 2) the wind field is constructed with the acquired data; 3) the high-altitude wind field is mapped to near-surface, and 4) the near-surface wind field is fed into the wildfire model periodically. A series of simulations are performed using an in-house decoupled physics-based reduced-order fire propagation model (FireProM-F) enhanced by wind field “measurements”. In this proof of concept, wind velocities are not measured but extracted from physics-based Large Eddy Simulations taken as ground truth. Unsurprisingly, higher measurement frequencies lead to more accurate and realistic predictions of the propagating fire front. An initial attempt is made to study the effect of wind measurement uncertainty on the model predictions by adding Gaussian noise. Preliminary results show that higher noise leads to the fire front displaying more irregular shapes and slower propagation.

Pulse Accumulation Approach Based on Signal Phase Estimation for Doppler Wind Lidar.

Coherent Doppler wind lidar (CDWL) uses transmitted laser pulses to measure wind velocity distribution. However, the echo signal of CDWL is easily affected by atmospheric turbulence, which can decrease the signal-to-noise ratio (SNR) of lidar. To improve the SNR, this paper proposes a pulse accumulation method based on the cross-correlation function to estimate the phase of the signal. Compared with incoherent pulse accumulation, the proposed method significantly enhances the correlation between signals from different periods to obtain high SNR gains that arise from pulse accumulation. Using simulation, the study evaluates the effectiveness of this phase estimation method and its robustness against noise in algorithms which analyze Doppler frequency shifts. Furthermore, a CDWL is developed for measuring the speed of an indoor motor turntable and the outdoor atmospheric wind field. The phase estimation method yielded SNR gains of 28.18 dB and 32.03 dB for accumulation numbers of 500 and 1500, respectively. The implementation of this method in motor turntable speed measurements demonstrated a significant reduction in speed error-averaging 9.18% lower than that of incoherent accumulation lidar systems. In experiments that measure atmospheric wind fields, the linear fit curve slope between the measured wind speed and the wind speed measured via a commercial wind-measuring lidar can be reduced from 1.146 to 1.093.

The Impacts of Deformed Fabry–Perot Interferometer Transmission Spectrum on Wind Lidar Measurements

The Fabry–Perot interferometer (FPI) plays a crucial role as the frequency discriminator in the incoherent Doppler wind lidar. However, in the practical receiver system, reflections occurring between optical elements introduce non-normal incident components in the light beams passing through the FPI. This phenomenon results in the deformation of the FPI transmission spectral lines. Based on that, a theoretical model has been developed to describe the transmission spectrum of the FPI when subjected to obliquely incident light beams with a divergence angle. By appropriately adjusting the model parameters, the simulated transmission spectrum of the FPI edge channels can coincide with the experimentally measured FPI spectral line. Subsequently, the impact of deformations in the transmission spectrum of the two edge channels on wind measurements is evaluated. The first implication is a systematic shift of 30.7 m/s in line-of-sight (LOS) wind velocities. This shift is based on the assumption that the lidar echo is solely backscattered from atmospheric molecules. The second consequence is the inconsistency in the response sensitivities of Doppler frequency shift between Rayleigh signals and Mie signals. As a result, the lidar system fails to fully achieve its initial design objectives, particularly in effectively suppressing interference from Mie signals. The presence of aerosols can introduce a significant error of several meters per second in the measurement of LOS wind velocity.

Wind power forecasting based on a machine learning model: considering a coastal wind farm in Zhejiang as an example

ABSTRACT The unpredictability and instability of wind have hindered the development and utilization of wind power. To harness wind energy and ensure a secure and stable power grid after wind power integration, precise predictions of wind power generation are imperative. Here, we apply one-year data from a coastal wind farm in Zhejiang to train a Random Forest (RF) model for predicting wind power generation. The results indicate that the RF model (mean bias (MB) 1.33) outperforms traditional linear models (MB 87.70). An evaluation of prediction-measurement bias shows a strong agreement between the RF model’s predictions and actual measurements (MB 1.33), especially when wind speeds exceed 5.7 m/s. While the Weather Research and Forecasting (WRF) model yields less accurate results (R2 0.65) compared to using measured wind velocity data (R2 0.97) for wind power prediction, it remains acceptable as it can capture and forecast peak generating power, meeting daily management requirements. Therefore, our machine learning-based approach offers practical guidance for reducing wind power generation uncertainties. Enhancing wind velocity forecasting through the WRF model could further improve the RF model’s accuracy in predicting wind power generation. Our study also verified the future application of RF model in coastal areas of China.

Research on attitude correction algorithm for mobile wind lidars

The laser wind measurement technology is remarkable for detecting clear-sky wind fields. The Doppler beam swinging algorithm for wind lidars has been developed to obtain vertical wind profiles based on fixed observation methods. However, the Doppler frequencies are superposed due to the self-motions of lidars caused by carrier motions when lidars are used on motion carrier platforms. Meanwhile, the emission directions of laser beams are uncertain due to changes in carriers’ motion directions and tilts. Thus, a new wind measurement correction model must be studied with lidar attitudes. This study considers the influences of the motion velocities, the carrier’s tilt angles, and the laser beams’ yaw angles at the 0° azimuth angle on the measured results under lidar motions, a correction model of motion attitudes for mobile wind lidars was designed. Sensitivity simulation tests for motion attitude parameters were carried out, and the influences of different attitude parameters of the carrier on the measured results were investigated to evaluate and verify the effects of the correction model. Results indicated that the wind measurement correction model could correct data errors caused by the carrier’s motion and tilts. The motion velocities, carrier directions, and the yaw angles of the laser beams at the 0° azimuth angle had an essential influence on the wind velocity measurements. Besides, the carrier’s pitch angles and the roll angles, which did not influence the wind velocity measurements, only affected the altitudes of the wind field data. Furthermore, the pitch angles exerted more significant influences on the data altitudes than the roll angles.

Reference Evapotranspiration Evaluation Using Solar Radiation Estimated by ANN and Empirical Models

Aims: The reference evapotranspiration (ETo) estimation with Penman-Monteith or Priestley-Taylor methods requires measurements of temperature, radiation, humidity, and wind velocity. In this study, we evaluated the estimations of ETo by Penman-Monteith (ETo-PM) and Priestley-Taylor (ETo-PrT) methods using indirect methods of calculating solar radiation (Rs). Place and Duration of Study: Daily meteorological measurements from two stations in northern Greece were used for the development of solar radiation models and ETo calculation. Methodology: The indirect methods of calculating solar radiation (Rs) are based on Artificial Neural networks (ANN) technology and models using the multi-linear regression method (MLR). Three different ANN and MLR models were derived. The Hargreaves method is also used. The evaluation of the indirect Rs derived models and the ETo estimation by the two methods was performed with the use of correlation coefficients (r), root mean square error (RMSE), and efficiency (EF) indexes. Results: The statistics of ETo estimation at the two stations showed that the r and EF values, between the estimated ETo using the indirect Rs models and estimated ETo using Rs measured, were greater than 0.963 and 0.918, respectively, while the RMSE values were lower than 0.646 mm d-1. The statistics of Rs models, showed that the r and EF values were greater than 0.860 and 0.605, respectively, while the RMSE values were lower than 4.47 MJ m-2d-1. Conclusion: The results of ANN models in comparison to MLR models, when using the same input variables, are consistent between them. These findings indicate that the Penman-Monteith and Priestley-Taylor methods can accurately predict ETo using Rs values estimated indirectly through the examined methods and models.

Seasonal and spatial variability of CO2 emissions in a large tropical mangrove‐dominated delta

AbstractThis study quantified the seasonal and spatial variability of partial pressure of CO2 (pCO2) and water‐atmosphere CO2 fluxes in the Parnaíba River Delta, the largest delta in the Americas. It is a pristine equatorial, mangrove‐dominated environment located in a transitional between humid and semi‐arid climates, with marked seasonality in rainfall and river discharge. Major channels and bays were sampled during dry and wet seasons, with continuous measurements of pCO2, temperature, salinity, and wind velocity. Subsurface water samples were collected in discrete stations for pH, total alkalinity (TA), dissolved inorganic carbon (DIC), dissolved oxygen and chlorophyll a quantification. A significant positive correlation between carbonate system parameters with salinity was found in both periods, with salinity significantly higher in the dry season. Strong deviations of pCO2, TA, and DIC from two endmembers conservative mixing were found, particularly in mangrove‐dominated waters, due to organic matter degradation. The Delta showed high spatial variability of pCO2, with the highest values in mangrove‐dominated waters, moderate in the river‐dominated regions, and lowest in the high salinity areas, suggesting that pCO2 variability is likely controlled by a combination of river‐ocean mixing and biological processes (respiration and photosynthesis). The Delta outgasses about 20 times less CO2 in the dry season (9.06 ± 11.09 mmol m−2.d−1) than in the rainy season (209.68 ± 250.87 mmol m−2 d−1). Our results indicate this large mangrove‐dominated tropical delta is an important source of CO2 to the atmosphere, but a sharp decrease was observed during dry periods.

Improving the urban wind flow prediction efficiency of target area by considering its surrounding buildings impact

Rapid urbanization has caused various urban environmental challenges, including the emergence of urban heat island and air pollution. Urban wind flow is a crucial factor shaping the urban environment, which needs effective prediction and design. Computational fluid dynamics is commonly used for prediction and design of urban wind environment. However, high-accuracy simulation of wind flow, especially considering the layouts of full-scale and complex buildings, requires intensive computational resources. Typically, simulations simplify the surrounding building layouts (e.g., building layers) of target areas, potentially impacting simulation accuracy. It is imperative to determine appropriate building layers around different-scale target areas for effective wind flow simulation. This work explored the simulated wind flow characteristics of multi-scale (local, micro, and building-scale) target areas with different building layers defined as H, L, H/L, and L/H (H and L representing the vertical and horizontal maximum sizes of target area). The recommended building layers for local-scale, micro-scale, and building-scale target areas were at least 3L, 4H, and 5H, respectively, ensuing the relative errors between simulated and measured wind velocities remained below 20 %. This method largely reduced the calculation time by 73 % compared with full-scale simulation. This work can provide guidelines for fast prediction and design of urban wind environment, further promoting the sustainable development of urban cities.

Reconfigurable ultra-sparse ventilated metamaterial absorber

The impetus for examining ventilated acoustic metamaterials stems from the demand for increasingly efficient and adaptable noise reduction and sound wave manipulation techniques. However, for the present reconfigurable metamaterial absorbers, their absorption band and ventilation rate have not yet attained practical application at low frequencies (<1000 Hz). A theoretical and experimental demonstration of a reconfigurable ultra-sparse ventilated metamaterial absorber for low frequencies with highly sparse ventilation and tunability is presented. The acoustic absorber is based on triadic cylindrical Helmholtz resonators, and a coupled mode theory for three resonators is implemented to elucidate the underlying physics. The absorber achieves a sparsity level of more than 80%, with a measured wind velocity ratio higher than 95%. Furthermore, tunability is accomplished in the frequency range of 600 to 950 Hz through reconfiguration. Due to its simple structure, this metamaterial absorber can be easily fabricated in mass in the future, providing promising solutions for various acoustic engineering applications.

Performance-based wind engineering assessment of a telecommunication mast utilizing Bayesian extreme wind velocity model

A promising design concept, the performance-based wind engineering methodology is adopted, implementing a finite element model (FEM) to analyse the structural behaviour of a telecommunication steel lattice mast. The integration of site-specific wind loading models into the standardized FEM-based design can be a powerful tool for design engineers. The tower being investigated, located in Central Hungary, is equipped with sensors to measure wind velocity and strains in chord members. The stochastic modelling of the extreme wind velocity, i.e., the basic wind velocity is assessed using the peak-over-threshold approach with an automated threshold selection method. To enhance the accuracy of the analysis, Bayesian inference is employed, combining the information from a relatively short duration of measurements collected at the tower site with a more extensive dataset from measurement locations across the country. Numerical model of the telecommunication mast subjected to wind loading is developed in a general-purpose finite element software. The probabilistic design system module is applied for performing probabilistic calculations of both serviceability criterion and load-bearing capacity of the structure using Monte Carlo simulation with Latin hypercube method. Numerical results are compared and validated with measurement data. The accuracy of wind load assessment is examined in this study, considering both the standardized design method according to EN 1993-3-1 and wind tunnel testing. The main objective is to demonstrate the potential of integrating the Performance-based Wind Design framework with a monitoring system and a finite element model, highlighting their combined benefits.

Aircraft wake vortices affecting airport wind measurements

The influence of wake vortices on other aircraft has been extensively studied and is well understood. However, to date, it has not been investigated how wake vortices can affect wind velocity measurements at airports. This study investigates this previously overlooked issue, focusing on departures from runway 34 at Zurich Airport. These departures are suspected to affect a wind sensor situated at the runway’s end. Through a combination of visual identification and application of a wake vortex model, instances where wakes affected the said anemometer were identified for a fifteen-month period. Analysis of the resulting data shows that approximately 5% of all departures generated such occurrences. In addition, specific wind conditions and aircraft types were identified as being necessary for such events to occur. The observed cases of wake hits have a significant effect on the wind measurements, altering even averaged values used by air traffic control for clearance by several knots and up to 50 degrees. That, in turn, can result in cases where aircraft performance does not allow take-off based on altered wind readings, even though the actual wind conditions would not prevent the departure. Such cases have the potential to cause significant disruption to flight operations. Even though this paper focuses on this phenomenon at Zurich Airport, similar issues are likely to occur at other airports as well.

Tethered Balloon-Borne Turbulence Measurements in Winter and Spring during the MOSAiC Expedition

During the Multidisciplinary Drifting Observatory for the Study of Arctic Climate expedition, a tethered balloon system was operated with a turbulence probe attached to study the lower troposphere in the high Arctic. Overall, measurements were conducted on 34 days between December 2019 and May 2020, resulting in 47 quality-assured sampling records consisting of vertical profiles and constant-altitude measurements. The continuous profiles extend from the surface, i.e., the sea ice floe, to a height of several hundred meters typically. The high-resolution wind velocity measurements using a hot-wire anemometer and temperature measurements using a thermocouple provide a comprehensive basis for examining the dynamical processes and thermodynamic stratification in the Arctic atmospheric boundary layer under cloudless and cloudy conditions. This paper provides a detailed technical description of the turbulence payload, including calibration and quality assurance, and a general overview of the data. A particular focus of this work is the estimation of local energy dissipation rates. The data are freely available from the World Data Center PANGAEA.