Wind Lidar Research Articles

Wind lidars reveal turbulence transport mechanism in the wake of a tree

Abstract. Solitary trees are natural land surface elements found in almost all climates, yet their influence on the surrounding airflow is poorly known. Here we use state-of-the-art, laser-based, remote sensing instruments to study the turbulent wind field in the near-wake region of a mature, open-grown oak tree. Our measurements provide for the first time a full picture of the mixing layer of high turbulence that surrounds the mean wind speed deficit. In this layer, we investigate the validity of a two-dimensional vectorial relation derived from the eddy-viscosity hypothesis, a hypothesis commonly used in modelling the turbulence transport of momentum and scalars in the atmosphere. We find that the momentum fluxes of the streamwise wind component can be adequately predicted by the transverse gradient of the mean flow. Using the mixing-length hypothesis we find that for this tree the corresponding turbulence length scale in the mixing layer can be approximated by one height-independent value. Further, the laser-based scanning technology used here was able to accurately reveal three-dimensional turbulent and spatially varying atmospheric flows over a large plane without seeding or intruding the atmospheric flow. This capability points to a new and more exact way of exploring the complex earth–atmosphere interactions.

Alignment of scanning lidars in offshore wind farms

Abstract. Long-range Doppler wind lidars are applied more and more for high-resolution areal measurements in and around wind farms. Proper alignment, or at least knowledge on how the systems are aligned, is of great relevance here. The paper describes in detail two methods that allow a very accurate alignment of a long-range scanning lidar without the use of extra equipment or sensors. The well-known so-called hard targeting allows a very precise positioning and north alignment of the lidar using the known positions of the surrounding obstacles, e.g. wind turbine towers. Considering multiple hard targets instead of only one with a given position in an optimization algorithm allows us to increase the position information of the lidar device and minimizes the consequences of using erroneous input data. The method, referred to as sea surface levelling, determines the levelling of the device during offshore campaigns in terms of roll and pitch angles based on distance measurements to the water surface. This is particularly well-suited during the installation of the systems to minimize alignment error from the start, but it can also be used remotely during the measurement campaign for verification purposes. We applied and validated these methods to data of an offshore measurement campaign, where a commercial long-range scanning lidar was installed on the transition piece platform of a wind turbine. In addition, we present a model that estimates the quasi-static inclination of the device due to the thrust loading of the wind turbine at different operating conditions. The results show reliable outcomes with a very high accuracy in the range of 0.02∘ in determining the levelling. The importance of the exact alignment and the possible applications are discussed in this paper. In conclusion, these methods are useful tools that can be applied without extra effort and contribute significantly to the quality of successful measurement campaigns.

Coherent Doppler wind lidar with real-time wind processing and low signal-to-noise ratio reconstruction based on a convolutional neural network.

Multi-classification using a convolutional neural network (CNN) is proposed as a denoising method for coherent Doppler wind lidar (CDWL) data. The method is intended to enhance the usable range of a CDWL beyond the atmospheric boundary layer (ABL). The method is implemented and tested in an all-fiber pulsed CWDL system operating at 1550 nm wavelength with 20 kHz repetition rate, 300 ns pulse length and 180 µJ of laser energy. Real-time pre-processing using a field programmable gate array (FPGA) is implemented producing averaged lidar spectrograms. Real-world measurement data is labeled using conventional frequency estimators and mixed with simulated spectrograms for training of the CNN. First results of this methods show that the CNN can outperform conventional frequency estimations substantially in terms of maximum range and delivers reasonable output in very low signal-to-noise (SNR) situations while still delivering accurate results in the high-SNR regime. Comparing the CNN output with radiosonde data shows the feasibility of the proposed method.

Marine Mixed Layer Height Detection Using Ship-Borne Coherent Doppler Wind Lidar Based on Constant Turbulence Threshold

Marine mixed layer height (MLH) detection using a ship-borne coherent Doppler wind lidar (CDWL) based on a constant turbulent kinetic energy dissipation rate (TKEDR) threshold is realized and experimentally demonstrated. The MLH can be retrieved from the TKEDR estimated by the CDWL via setting an appropriate threshold. Here, the value of threshold is determined by a reference MLH retrieved from aerosol backscattered signal. The threshold of 10−4 m2 s−3 is found to be applicable in retrieving both inland and marine MLHs. In the experiments, to validate the reliability of the constant threshold, the MLH diurnal cycles at inland and marine sites are retrieved by using a ground-based CDWL. The MLH retrieval result at the marine site shows good agreement with radiosonde-derived MLH. After that, by using a ship-borne CDWL, the marine MLH along the ship’s route in South China Sea is successfully detected in real time.

Statistical validation of Aeolus L2A particle backscatter coefficient retrievals over ACTRIS/EARLINET stations on the Iberian Peninsula

Abstract. The Global Observing System (GOS) has encountered some limitations due to a lack of worldwide real-time wind measurements. In this context, the European Space Agency (ESA) has developed the Aeolus satellite mission, based on the ALADIN (Atmospheric Laser Doppler Instrument) Doppler wind lidar; this mission aims to obtain near-real-time wind retrievals at the global scale. As spin-off products, the instrument retrieves aerosol optical properties such as particle backscatter and extinction coefficients. In this work, a validation of Aeolus reprocessed (baseline 10) co-polar backscatter coefficients (βAeoluspart) is presented through an intercomparison with analogous ground-based measurements taken at the ACTRIS (Aerosols, Clouds and Trace gases Research InfraStructure Network)/EARLINET (European Aerosol Research Lidar Network) stations of Granada (Spain), Évora (Portugal) and Barcelona (Spain) over the period from July 2019 until October 2020. Case studies are first presented, followed by a statistical analysis. The stations are located in a hot spot between Africa and the rest of Europe, which guarantees a variety of aerosol types, from mineral dust layers to continental/anthropogenic aerosol, and allows us to test Aeolus performance under different scenarios. The so called Aeolus-like profiles (βAeoluslike,355part) are obtained from total particle backscatter coefficient and linear particle depolarization ratio (δlinearpart) profiles at 355 and 532 nm measured from the surface, through a thorough bibliographic review of dual-polarization measurements for relevant aerosol types. Finally, the study proposes a relation for the spectral conversion of δlinearpart, which is implemented in the Aeolus-like profile calculation. The statistical results show the ability of the satellite to detect and characterize significant aerosol layers under cloud-free conditions, along with the surface effect on the lowermost measurements, which causes the satellite to largely overestimate co-polar backscatter coefficients. Finally, the Aeolus standard correct algorithm middle bin (SCAmb) shows a better agreement with ground-based measurements than the standard correct algorithm (SCA), which tends to retrieve negative and meaningless coefficients in the clear troposphere. The implementation of Aeolus quality flags entails a vast reduction in the number of measurements available for comparison, which affects the statistical significance of the results.

Optimization of Aeolus' aerosol optical properties by maximum-likelihood estimation

Abstract. The European Space Agency (ESA) Earth Explorer Mission Aeolus was launched in August 2018, carrying the first Doppler wind lidar in space. Its primary payload, the Atmospheric LAser Doppler INstrument (ALADIN), is an ultraviolet (UV) high-spectral-resolution lidar (HSRL) measuring atmospheric backscatter from air molecules and particles in two separate channels. The primary mission product is globally distributed line-of-sight wind profile observations in the troposphere and lower stratosphere. Atmospheric optical properties are provided as a spin-off product. Being an HSRL, Aeolus is able to independently measure the particle extinction coefficients, co-polarized particle backscatter coefficients and the co-polarized lidar ratio (the cross-polarized return signal is not measured). This way, the retrieval is independent of a priori lidar ratio information. The optical properties are retrieved using the standard correct algorithm (SCA), which is an algebraic inversion scheme and therefore sensitive to measurement noise. In this work, we reformulate the SCA into a physically constrained maximum-likelihood estimation (MLE) problem and demonstrate a predominantly positive impact and considerable noise suppression capabilities. These improvements originate from the use of all available information by the MLE in conjunction with the expected physical bounds concerning positivity and the expected range of the lidar ratio. To consolidate and to illustrate the improvements, the new MLE algorithm is evaluated against the SCA on end-to-end simulations of two homogeneous scenes and for real Aeolus data collocated with measurements by a ground-based lidar and the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. The largest improvements were seen in the retrieval precision of the extinction coefficients and lidar ratio ranging up to 1 order of magnitude or more in some cases due to effective noise dampening. In real data cases, the increased precision of MLE with respect to the SCA is demonstrated by increased horizontal homogeneity and better agreement with the ground truth, though proper uncertainty estimation of MLE results is challenged by the constraints, and the accuracy of MLE and SCA retrievals can depend on calibration errors, which have not been considered.

Inter-comparison of wind measurements in the atmospheric boundary layer and the lower troposphere with Aeolus and a ground-based coherent Doppler lidar network over China

Abstract. After the successful launch of Aeolus, which is the first spaceborne wind lidar developed by the European Space Agency (ESA), on 22 August 2018, we deployed several ground-based coherent Doppler wind lidars (CDLs) to verify the wind observations from Aeolus. By the simultaneous wind measurements with CDLs at 17 stations over China, the Rayleigh-clear and Mie-cloudy horizontal-line-of-sight (HLOS) wind velocities from Aeolus in the atmospheric boundary layer and the lower troposphere are compared with those from CDLs. To ensure the quality of the measurement data from CDLs and Aeolus, strict quality controls are applied in this study. Overall, 52 simultaneous Mie-cloudy comparison pairs and 387 Rayleigh-clear comparison pairs from this campaign are acquired. All of the Aeolus-produced Level 2B (L2B) Mie-cloudy HLOS wind and Rayleigh-clear HLOS wind and CDL-produced HLOS wind are compared individually. For the inter-comparison result of Mie-cloudy HLOS wind and CDL-produced HLOS wind, the correlation coefficient, the standard deviation, the scaled mean absolute deviation (MAD) and the bias are 0.83, 3.15 m s−1, 2.64 m s−1 and −0.25 m s−1, respectively, while the y=ax slope, the y=ax+b slope and the y=ax+b intercept are 0.93, 0.92 and −0.33 m s−1. For the Rayleigh-clear HLOS wind, the correlation coefficient, the standard deviation, the scaled MAD and the bias are 0.62, 7.07 m s−1, 5.77 m s−1 and −1.15 m s−1, respectively, while the y=ax slope, the y=ax+b slope and the y=ax+b intercept are 1.00, 0.96 and −1.2 m s−1. It is found that the standard deviation, the scaled MAD and the bias on ascending tracks are lower than those on descending tracks. Moreover, to evaluate the accuracy of Aeolus HLOS wind measurements under different product baselines, the Aeolus L2B Mie-cloudy HLOS wind data and L2B Rayleigh-clear HLOS wind data under Baselines 07 and 08, Baselines 09 and 10, and Baseline 11 are compared against the CDL-retrieved HLOS wind data separately. From the comparison results, marked misfits between the wind data from Aeolus Baselines 07 and 08 and wind data from CDLs in the atmospheric boundary layer and the lower troposphere are found. With the continuous calibration and validation and product processor updates, the performances of Aeolus wind measurements under Baselines 09 and 10 and Baseline 11 are improved significantly. Considering the influence of turbulence and convection in the atmospheric boundary layers and the lower troposphere, higher values for the vertical velocity are common in this region. Hence, as a special note, the vertical velocity could impact the HLOS wind velocity retrieval from Aeolus.

A Genetic Algorithm for Optimizing Yaw Operation Control in Wind Power Plants

A genetic algorithm is proposed to optimize the yaw control system used for the stable and efficient operation of turbines in wind power plants. In particular, the factors that produce yaw static deviation are analyzed. Then, the sought optimization method for the yaw static deviation of the wind turbine is implemented by using a lidar wind meter in the engine room in order to solve the low accuracy problem caused by yaw static deviation. It is shown that fuzzy control can overcome problematic factors such as the randomness of wind direction and track the change of wind direction accurately. Power control implementation is simple, as only the voltage and current of the generator need to be measured.

NWP calibration applied to Aeolus Mie channel winds

AbstractAeolus is the first Doppler wind lidar (DWL) to measure wind profiles from space. Aeolus is an ESA (European Space Agency) explorer mission with the objective to retrieve winds from the collected atmospheric return signal which is the result of Mie and Rayleigh scattering of laser‐emitted light by atmospheric molecules and particulates. The focus of this paper is on winds retrieved from instrument Mie channel collected data, that is, originating from Mie scattering by atmospheric aerosols and clouds. The use of simulated data from numerical weather prediction (NWP) models is a widely accepted and proven concept for the monitoring of the performance of many meteorological instruments, including Aeolus. Continuous monitoring of Aeolus Mie channel winds against model winds from the European Centre for Medium‐Range Weather Forecasts (ECMWF) has revealed systematic errors in retrieved Mie winds. Following a reverse engineering approach, the systematic errors could be traced back to imperfections of the data in the calibration tables which serve as input for the on‐ground wind processing algorithms. A new algorithm, denoted NWP calibration, makes use of NWP model winds to generate an updated calibration table. It is shown that Mie winds retrieved by making use of the NWP‐based calibration tables show reduced systematic errors, not only when compared to NWP model winds but also when compared to an independent dataset of very‐high‐resolution aircraft wind data. The latter gives high confidence that the NWP‐based calibration algorithm does not introduce model‐related errors into retrieved Aeolus Mie winds. Based on the presented results in this paper, the NWP‐based calibration table, as part of the level‐2B wind processing, has become part of the operational processing chain since 01 July 2021.

A review of the technical system of spaceborne Doppler wind lidar and its assessment method

A review of the technical system of spaceborne Doppler wind lidar and its assessment method

Comparative Study of the Near-Surface Typhoon Wind Profile Fitting between Offshore and Onshore Areas

The study of typhoon wind profiles, especially offshore typhoon wind profiles, has been constrained by the scarcity of observational data. In this study, the Doppler wind lidar was used to observe the offshore wind profiles during Super Typhoon Mangkhut and onshore wind profiles during Super Typhoon Lekima. Four wind profile models, including the power law, logarithmic law, Deaves–Harris (D-H), and Gryning, were selected in the height range of 0–300 m to fit the wind profile. The variations in the power exponent with the mean wind speed and roughness length were also analyzed. The results showed that the wind profiles fitted by the four models were generally in good agreement with the observed wind profiles with correlation coefficients greater than 0.98 and root mean square deviations less than 0.5 m s−1. For the offshore case, the fitting degree of all wind profile models improved with increasing mean wind speed. Specifically, the D-H model had the highest fitting degree when the horizontal mean wind speed at 40 m was in the range of 8–25 m s−1, while the log-law model had the highest fitting degree when the wind speed exceeded 30 m s−1. For the onshore case, the fitting degree of the four wind profile models deteriorated with increasing mean wind speed, and the log-law model had the highest fitting degree in all wind speed intervals from 8 to 30 m s−1. For both offshore and onshore cases, the power exponent was less affected by mean wind speed and increased with increasing roughness length, and the logarithmic empirical model proposed in this study could well characterize the relationship between the power exponent and roughness length.

Correction of various environmental influences on Doppler wind lidar based on multiple linear regression model

Doppler wind lidar (DWL) is being increasingly employed in various areas, such as wind energy, meteorology, aviation, and so on. Extensive studies have been conducted to validate its accuracy and reliability compared with anemometers mounted on meteorological towers. However, previous examinations mainly focused on a range up to 100 m because of the limited heights of meteorological towers. To further validate the DWL performance, especially above a height of 100 m, experimental tests were carried out at two national meteorological observatories in China (Shenzhen and Xilinhaote). The meteorological tower at Shenzhen Observatory is 356 m high, which enables validation of DWLs above 100 m. Different environmental variables, including humidity, precipitation, wind characteristics, and surface roughness length, were investigated to quantify their effects on the measurement errors of DWLs. Moreover, a correction methodology based on multiple linear regression model was proposed to eliminate the measurement error induced by environmental conditions. The corrected DWL data can be improved by up to 9.6% regarding the slope of the linear regression between the DWL and tower data, and the associated root mean square errors can be reduced by up to 37%.

Wind lidar signal denoising method based on singular value decomposition and variational mode decomposition.

A denoising method based on singular value decomposition (SVD) and variational mode decomposition (VMD) is proposed for wind lidar. Utilizing the covariance matrix based lidar signal simulation model, the performance of VMD, SVD, and VMD-SVD is evaluated. The results show that the VMD-SVD method is of better performance, and the output signal-to-noise ratio (SNR) is about 12 dB at the input SNR of -9dB. The actual lidar signals processing is performed with this combined denoising method, and the detection range and wind speed at pulse accumulation numbers of 50,100, and 300 are compared. We set the wind speed resulting from noisy signal with pulse accumulation number of 300 as the reference wind speed, and the mean value and standard deviation of wind differences are analyzed. The results show that the denoising method can not only increase the detection range while ensuring the accuracy of wind speed estimation but also achieve the same detection distance with fewer pulse accumulations, thereby improving the temporal resolution. For the pulse accumulation number of 50, the detection range is extended to 24 km from 18.45 km, and the standard deviation of speed difference is 0.88 m/s; for the same detection range, the temporal resolution is increased by about 6 times.

Study of the Boundary Layer Structure of a Landfalling Typhoon Based on the Observation from Multiple Ground-Based Doppler Wind Lidars

The boundary layer structure is crucial to the formation and intensification of typhoons, but there is still a lack of high-precision turbulence observations in the typhoon boundary layer due to limitations of the observing instruments under typhoon conditions. Using joint observations from multiple ground-based Doppler wind lidars (DWL) collected by the Shanghai Typhoon Institute of China Meteorological Administration (CMA) during the transit of Typhoon Lekima (8–11 August 2019), the characteristics of the wind field and physical quantities (including turbulent kinetic energy (TKE) and typhoon boundary layer height (TBLH)) of the boundary layer of typhoon Lekima were analyzed. The magnitude of TKE was shown to be related not only to the horizontal wind speed but also to the presence of a strong downdraft, which leads to a rapid increase of TKE. The magnitudes of TKE in different quadrants of Typhoon Lekima were also found to differ. The TKE in the front right quadrant of the typhoon was 2.5–6.0 times that in the rear left quadrant and ~1.7 times that in the rear right quadrant. The TKE over the island was larger than that over the urban area. Before Typhoon Lekima made landfall, the TKE increased with decreasing distance to the typhoon center. After typhoon landfall, the TKE changes were different on the left and right sides of the typhoon center, with the TKE on the left decreasing rapidly, while that on the right changed little. The typhoon boundary layer height calculated by five methods was compared and was found to decrease gradually before typhoon landfall and increased gradually afterward. The trends of the TBLH calculated using helicity and TKE were consistent, and both determine the TBLH well, while the maximum tangential wind speed height (humax) was larger than the height calculated by other methods. This study confirms that DWL has a strong detecting capability for the finescale structure of the typhoon boundary layer and provides a powerful tool for the validation of numerical simulations of typhoon structure.

Vertical Profiles of Ozone Concentrations in the Lower Troposphere Downwind of New York City During LISTOS 2018–2019

AbstractWhile surface concentrations of ozone are routinely monitored, ozone aloft is infrequently measured, but critical for a full understanding of ozone production and transport. In this study, twenty‐six balloon‐borne ozonesondes were launched near the north shore of central Long Island in the summers of 2018 and 2019 as part of the Long Island Sound Tropospheric Ozone Study (LISTOS). The observed vertical ozone profiles are presented and analyzed with additional data sources and modeling tools, including lidar wind profiles from the New York State Mesonet, back trajectories based on 3 km resolution high‐resolution rapid refresh model data, and surface data, aircraft observations, sonde, and ozone lidar measurements from other LISTOS participants. Special attention is given to the region of interest for ozone pollution in the lower troposphere, from the surface to 2 km altitude. Cases analyzed in detail illustrate events with high ozone levels observed in the lower troposphere, often with pronounced vertical structure in the profile. Specifically, easily discernible layers are identified with ozone excursions of up to 40 ppb over short vertical distances. Analysis indicates that synoptic and local meteorological processes can combine to generate the observed vertical profiles. Hot, sunny days with high‐pressure systems are accompanied by high precursor emissions due to increased power demands, plentiful radiation for photochemistry, and stagnation of synoptic winds. Under these conditions, meso‐ and smaller‐scale flows like low‐level jets and sea/bay/land breeze circulations may dominate synoptic flow to produce shearing and the complex vertical layered structure observed.

Validation of Aeolus Level 2B wind products using wind profilers, ground-based Doppler wind lidars, and radiosondes in Japan

Abstract. The first space-based Doppler wind lidar (DWL) on board the Aeolus satellite was launched by the European Space Agency (ESA) on 22 August 2018 to obtain global profiles of horizontal line-of-sight (HLOS) wind speed. In this study, the Raleigh-clear and Mie-cloudy winds for periods of baseline 2B02 (from 1 October to 18 December 2018) and 2B10 (from 28 June to 31 December 2019 and from 20 April to 8 October 2020) were validated using 33 wind profilers (WPRs) installed all over Japan, two ground-based coherent Doppler wind lidars (CDWLs), and 18 GPS radiosondes (GPS-RSs). In particular, vertical and seasonal analyses were performed and discussed using WPR data. During the baseline 2B02 period, a positive bias was found to be in the ranges of 0.5 to 1.7 m s−1 for Rayleigh-clear winds and 1.6 to 2.4 m s−1 for Mie-cloudy winds using the three independent reference instruments. The statistical comparisons for the baseline 2B10 period showed smaller biases, −0.8 to 0.5 m s−1 for the Rayleigh-clear and −0.7 to 0.2 m s−1 for the Mie-cloudy winds. The vertical analysis using WPR data showed that the systematic error was slightly positive in all altitude ranges up to 11 km during the baseline 2B02 period. During the baseline 2B10 period, the systematic errors of Rayleigh-clear and Mie-cloudy winds were improved in all altitude ranges up to 11 km as compared with the baseline 2B02. Immediately after the launch of Aeolus, both Rayleigh-clear and Mie-cloudy biases were small. Within the baseline 2B02, the Rayleigh-clear and Mie-cloudy biases showed a positive trend. For the baseline 2B10, the Rayleigh-clear wind bias was generally negative for all months except August 2020, and Mie-cloudy wind bias gradually fluctuated. Both Rayleigh-clear and Mie-cloudy biases did not show a marked seasonal trend and approached zero towards September 2020. The dependence of the Rayleigh-clear wind bias on the scattering ratio was investigated, showing that there was no significant bias dependence on the scattering ratio during the baseline 2B02 and 2B10 periods. Without the estimated representativeness error associated with the comparisons using WPR observations, the Aeolus random error was determined to be 6.7 (5.1) and 6.4 (4.8) m s−1 for Rayleigh-clear (Mie-cloudy) winds during the baseline 2B02 and 2B10 periods, respectively. The main reason for the large Aeolus random errors is the lower laser energy compared to the anticipated 80 mJ. Additionally, the large representativeness error of the WPRs is probably related to the larger Aeolus random error. Using the CDWLs, the Aeolus random error estimates were in the range of 4.5 to 5.3 (2.9 to 3.2) and 4.8 to 5.2 (3.3 to 3.4) m s−1 for Rayleigh-clear (Mie-cloudy) winds during the baseline 2B02 and 2B10 periods, respectively. By taking the GPS-RS representativeness error into account, the Aeolus random error was determined to be 4.0 (3.2) and 3.0 (2.9) m s−1 for Rayleigh-clear (Mie-cloudy) winds during the baseline 2B02 and 2B10 periods, respectively.

Spatial resolution enhancement of coherent Doppler wind lidar using differential correlation pair technique.

A high spatial resolution coherent Doppler wind lidar (CDWL) incorporating the differential correlation pair (DCP) technique is proposed and demonstrated. By employing pulse pair with appropriate window functions, the spatial resolution can be enhanced, as the common parts of the correlation pair can be eliminated in the differential data processing. The performance of the new method is validated in the comparison experiment with the CDWLs adopting conventional schemes. Under a given peak power, the DCP technique provides higher wind velocity accuracy compared with a conventional pulsed CDWL where the laser spectral broadening caused by short pulses can be avoided and the carrier-to-noise ratio is improved. At a laser peak power of 250 W, with a spatial and temporal resolution of 3.3 m and 1 s, continuous radial wind profiling over 700 m is realized with a maximum error of 0.1 m/s.

Wind speed reconstruction from mono-static wind lidar eliminating the effect of turbulence

Light detection and ranging (lidar) is an attractive technique to measure wind speed via the Doppler effect, thanks to its flexibility. Although stand-alone deployment is growing, current standards for wind energy applications often require wind lidars to be verified against an in situ met mast with cup anemometers, which deliver point-like measurements. Analyzing 38 lidar-to-cup verification campaigns at eight accredited sites, we investigate the difference between these techniques during turbulence influenced atmospheric conditions. We demonstrate that strong turbulence results in the lidar 10-min scalar average wind speed overestimate the cup value, whereas the lidar vector average results in an underestimate. Although scalar and vector averaging provide acceptable measurements, we show that a linear combination, with appropriate weighting, reduces the lidar turbulence sensitivity by more than one order of magnitude. The resulting overall lidar-to-cup bias is within the one-standard-deviation comparison uncertainty at all sites. The resulting ensemble mean bias is [Formula: see text], where the uncertainty stems from variations between sites. We explain the experimental observations by a model derived from first principles. The developed hybrid averaging is implemented in the Leosphere WindCube v2.1 wind lidar.

Aerodynamic study of a suspension bridge deck by CFD simulations, wind tunnel tests and full-scale observations

Assessing the aerodynamic characteristics of long-span bridges is fundamental for their design. Depending on the terrain complexity and local wind conditions, episodes of large angles of attack (AoA) of 15° may be observed. However, such large AoAs ( above 10°) are often overlooked in the design process. This paper studies the aerodynamics properties of a flow around a single-box girder for a wide range of AoAs, from –20° to 20°, using numerical simulations. The simulations are based on a 2D unsteady Reynolds-averaged Navier–Stokes (URANS) approach using the k − ω SST turbulence model with a Reynolds number of 1.6 × 105. Numerically obtained aerodynamic static coefficients were compared to wind tunnel test data. The CFD results were generally in good agreement with the wind tunnel tests, especially for small AoAs and positive AoAs. More discrepancies were observed for large negative AoA, likely due to the limitation of modelling 3D railings with 2D simulations. The simulated velocity deficit downstream of the deck was consistent with the one measured in full-scale using short-range Doppler wind lidar instruments. Finally, the Strouhal number from the CFD simulations were in agreement with the value obtained from the full-scale data.

Dual lidar wind measurements along an upstream horizontal line perpendicular to a suspension bridge

Remote wind sensing can complement traditional anemometry at a bridge site and contribute to an improved wind-resistant design of long-span bridges. This study examines wind lidar measurement data recorded along a 168-meter-long horizontal line perpendicular to the main span of a suspension bridge in complex terrain. The velocity data records are obtained by a pair of continuous-wave Doppler lidars (short-range WindScanners) installed on the bridge deck. The measurement data are explored in terms of the mean wind speed and mean wind direction upstream of the bridge. The spectral characteristics of turbulence along the line are also investigated in relation to the increasing sampling volumes of a continuous-wave lidar system at increasing distances from the monitored area. Wind characteristics observed by the lidars are compared to those derived from sonic anemometer data recorded above the bridge deck at midspan. The results provide new insight into the wind flow characteristics in a fjord and demonstrate the potential of lidar measurements in charting the wind flow around a bridge. A slight monotonic increase of the wind speed, as well as a decrease in the yaw angle, is observed as the distance to the bridge reduces from 160 m to 20 m, while lower wind velocities are accompanied by a more stable wind direction. Within 15 m from the bridge deck, the adopted lidar setup gives unreliable information due to the large angle between the lidar beams.