Explore the vast range of titles available.

To address the measurement instability of reflective ultrasonic anemometers in complex wind fields, this study systematically investigates the mechanisms by which shadow effects caused by transducers and reflector support pillars affect measurement accuracy under varying wind speeds and directions. By integrating particle image velocimetry (PIV) experiments with computational fluid dynamics (CFD) simulations, 1:1 and 1:2 scale models are employed to reveal the flow field characteristics and error mechanisms. The results indicate that at a wind direction of 0°, wall-following vortices and turbulent wakes generated by transducer structures cause systematic wind speed deviations along the measurement paths. At a 45° wind direction, flow disturbances around the support pillars become the dominant source of shadow effects. The 1:1 scale model exhibits insufficient decay of large-scale, low-frequency turbulent energy, resulting in the accumulation of turbulent kinetic energy and significant wind speed errors at 0°. In contrast, the 1:2 scale model enables efficient energy transfer through high-frequency, small-scale vortices, enhances vortex intensity uniformity, and achieves improved spatial homogeneity in cross-wind measurement errors. These findings provide an important theoretical foundation for improving the high-precision measurement performance of reflective ultrasonic anemometers in complex wind environments.

Wind-profiling lidars are now regularly used in boundary-layer meteorology and in applications such as wind energy and air quality. Lidar wind profilers exploit the Doppler shift of laser light backscattered from particulates carried by the wind to measure a line-of-sight (LOS) velocity. The Doppler beam swinging (DBS) technique, used by many commercial systems, considers measurements of this LOS velocity in multiple radial directions in order to estimate horizontal and vertical winds. The method relies on the assumption of homogeneous flow across the region sampled by the beams. Using such a system in inhomogeneous flow, such as wind turbine wakes or complex terrain, will result in errors. To quantify the errors expected from such violation of the assumption of horizontal homogeneity, we simulate inhomogeneous flow in the atmospheric boundary layer, notably stably stratified flow past a wind turbine, with a mean wind speed of 6.5 m s−1 at the turbine hub-height of 80 m. This slightly stable case results in 15° of wind direction change across the turbine rotor disk. The resulting flow field is sampled in the same fashion that a lidar samples the atmosphere with the DBS approach, including the lidar range weighting function, enabling quantification of the error in the DBS observations. The observations from the instruments located upwind have small errors, which are ameliorated with time averaging. However, the downwind observations, particularly within the first two rotor diameters downwind from the wind turbine, suffer from errors due to the heterogeneity of the wind turbine wake. Errors in the stream-wise component of the flow approach 30% of the hub-height inflow wind speed close to the rotor disk. Errors in the cross-stream and vertical velocity components are also significant: cross-stream component errors are on the order of 15% of the hub-height inflow wind speed (1.0 m s−1) and errors in the vertical velocity measurement exceed the actual vertical velocity. By three rotor diameters downwind, DBS-based assessments of wake wind speed deficits based on the stream-wise velocity can be relied on even within the near wake within 1.0 m s−1 (or 15% of the hub-height inflow wind speed), and the cross-stream velocity error is reduced to 8% while vertical velocity estimates are compromised. Measurements of inhomogeneous flow such as wind turbine wakes are susceptible to these errors, and interpretations of field observations should account for this uncertainty.

This paper analyzes the differences between ERA-Interim and ERA5 surface winds fields relative to Advanced Scatterometer (ASCAT) ocean vector wind observations, after adjustment for the effects of atmospheric stability and density, using stress-equivalent winds (U10S) and air–sea relative motion using ocean current velocities. In terms of instantaneous root mean square (rms) wind speed agreement, ERA5 winds show a 20 % improvement relative to ERA-Interim and a performance similar to that of currently operational ECMWF forecasts. ERA5 also performs better than ERA-Interim in terms of mean and transient wind errors, wind divergence and wind stress curl biases. Yet, both ERA products show systematic errors in the partition of the wind kinetic energy into zonal and meridional, mean and transient components. ERA winds are characterized by excessive mean zonal winds (westerlies) with too-weak mean poleward flows in the midlatitudes and too-weak mean meridional winds (trades) in the tropics. ERA stress curl is too cyclonic in midlatitudes and high latitudes, with implications for Ekman upwelling estimates, and lacks detail in the representation of sea surface temperature (SST) gradient effects (along the equatorial cold tongues and Western Boundary Current (WBC) jets) and mesoscale convective airflows (along the Intertropical Convergence Zone and the warm flanks for the WBC jets). It is conjectured that large-scale mean wind biases in ERA are related to their lack of high-frequency (transient wind) variability, which should be promoting residual meridional circulations in the Ferrel and Hadley cells.

Results from a recent field campaign are used to assess the accuracy of wind speed and direction precision estimates produced by a Doppler lidar wind retrieval algorithm. The algorithm, which is based on the traditional velocity-azimuth-display (VAD) technique, estimates the wind speed and direction measurement precision using standard error propagation techniques, assuming the input data (i.e., radial velocities) to be contaminated by random, zero-mean, errors. For this study, the lidar was configured to execute an 8-beam plan-position-indicator (PPI) scan once every 12 min during the 6-week deployment period. Several wind retrieval trials were conducted using different schemes for estimating the precision in the radial velocity measurements. The resulting wind speed and direction precision estimates were compared to differences in wind speed and direction between the VAD algorithm and sonic anemometer measurements taken on a nearby 300 m tower.All trials produced qualitatively similar wind fields with negligible bias but substantially different wind speed and direction precision fields. The most accurate wind speed and direction precisions were obtained when the radial velocity precision was determined by direct calculation of radial velocity standard deviation along each pointing direction and range gate of the PPI scan. By contrast, when the instrumental measurement precision is assumed to be the only contribution to the radial velocity precision, the retrievals resulted in wind speed and direction precisions that were biased far too low and were poor indicators of data quality.

Approximate equations for slope Λ and shape μ parameters of the gamma raindrop size distribution (DSD) are derived from theoretical considerations on the basis of the reflectivity-weighted mean terminal velocity V T and Doppler spectral width σ p of the raindrop echoes. These approximate equations are then corrected to reduce the error by using the long-term ground-based disdrometer measurements. We applied these equations on the VHF-radar measurements (i.e., reflectivity-weighted mean fall velocity and Doppler spectral width) to estimate μ and Λ aloft. The effects of the vertical wind velocity on the VHF radar-estimated μ –Λ and σ p – V T values are investigated. The result shows that there is a tendency for the μ and Λ values to increase with an increase of the upward wind velocity. In addition, the terminal velocity and the spread of the DSD estimated from the precipitation echoes are strongly related to the vertical air velocity. The stronger the upward vertical air velocity is, the larger the terminal velocity and the smaller the spread of the DSD will be. The dependence of the estimated μ and Λ values on the vertical wind velocity is very likely caused by the updraft that can support and carry away the smaller raindrops in the original drop size distribution in the radar volume. Consequently, the DSDs aloft are truncated and the corresponding μ and Λ values estimated from the radar returns tend to be larger. To confirm this assertion, we intentionally truncate disdrometer-measured DSDs at small drop size end and calculate corresponding μ and Λ values. The results show that the distribution patterns of the μ –Λ scatter diagrams of the truncated DSDs bear strong resemblance to those of the radar observations, both of which are very different from those obtained on the ground.

Shipborne wind observations by a coherent Doppler lidar (CDL) have been conducted to study the structure of the marine atmospheric boundary layer (MABL) during the 2014 Yellow Sea campaign. This paper evaluates uncertainties associated with the ship motion and presents the correction methodology regarding lidar velocity measurement based on modified 4-Doppler beam swing (DBS) solution. The errors of calibrated measurement, both for the anchored and the cruising shipborne observations, are comparable to those of ground-based measurements. The comparison between the lidar and radiosonde results in a bias of −0.23 ms−1 and a standard deviation of 0.87 ms−1 for the wind speed measurement, and 2.48, 8.84∘ for the wind direction. The biases of horizontal wind speed and random errors of vertical velocity are also estimated using the error propagation theory and frequency spectrum analysis, respectively. The results show that the biases are mainly related to the measuring error of the ship velocity and lidar pointing error, and the random errors are mainly determined by the signal-to-noise ratio (SNR) of the lidar backscattering spectrum signal. It allows for the retrieval of vertical wind, based on one measurement, with random error below 0.15 ms−1 for an appropriate SNR threshold and bias below 0.02 ms−1. The combination of the CDL attitude correction system and the accurate motion correction process has the potential of continuous long-term high temporal and spatial resolution measurement for the MABL thermodynamic and turbulence process.

Wind lidars are widespread and important tools in atmospheric observations. An intrinsic part of lidar measurement error is due to atmospheric variability in the remote-sensing scan volume. This study describes and quantifies the distribution of measurement error due to turbulence in varying atmospheric stability. While the lidar error model is general, we demonstrate the approach using large ensembles of virtual WindCube V2 lidar performing a profiling Doppler-beam-swinging scan in quasi-stationary large-eddy simulations (LESs) of convective and stable boundary layers. Error trends vary with the stability regime, time averaging of results, and observation height. A systematic analysis of the observation error explains dominant mechanisms and supports the findings of the empirical results. Treating the error under a random variable framework allows for informed predictions about the effect of different configurations or conditions on lidar performance. Convective conditions are most prone to large errors (up to 1.5 m s−1 in 1 Hz wind speed in strong convection), driven by the large vertical velocity variances in convective conditions and the high elevation angle of the scanning beams (62∘). Range-gate weighting induces a negative bias into the horizontal wind speeds near the surface shear layer (−0.2 m s−1 in the stable test case). Errors in the horizontal wind speed and direction computed from the wind components are sensitive to the background wind speed but have negligible dependence on the relative orientation of the instrument. Especially during low winds and in the presence of large errors in the horizontal velocity estimates, the reported wind speed is subject to a systematic positive bias (up to 0.4 m s−1 in 1 Hz measurements in strong convection). Vector time-averaged measurements can improve the behavior of the error distributions (reducing the 10 min wind speed error standard deviation to <0.3 m s−1 and the bias to <0.1 m s−1 in strong convection) with a predictable effectiveness related to the number of decorrelated samples in the time window. Hybrid schemes weighting the 10 min scalar- and vector-averaged lidar measurements are shown to be effective at reducing the wind speed biases compared to cup measurements in most of the simulated conditions, with time averages longer than 10 min recommended for best use in some unstable conditions. The approach in decomposing the error mechanisms with the help of the LES flow field could be extended to more complex measurement scenarios and scans.

Pressure gradient over topography will significantly affect wind-farm flow. However, knowledge gaps still exist on how to superpose wind-turbine wakes in the wind-farm flow analytical model to account for this effect, leading to systematic errors in evaluating wind-farm wake effects. To this end, we derive an implicit momentum-conserving wake superposition method under pressure gradient (PG-IMCM) based on the total momentum deficit equation, which is linearised by the convection velocity introduced by Zong & Porté-Agel (J. Fluid Mech., vol. 889, 2020, A8). The PG-IMCM method consists of the linear-weighted sum of individual velocity deficits, the sum of the individual pressure correction terms and the total pressure correction term. Based on a sensitivity analysis, we demonstrate that the last two terms nearly cancel out and, thus, can be neglected, resulting in a simplified form, which has the same form as its counterpart under zero pressure gradient but with the single-wake quantities redefined based on the wake model under pressure gradient. This motivates us to further examine the performance of the combination of five empirical superposition methods and the stand-alone wake model under pressure gradient. Validation results based on large-eddy simulation show that PG-IMCM has an overall satisfactory performance in both the magnitude and shape of the velocity-deficit profiles, provided that the stand-alone turbine wake can be modelled accurately, which is virtually identical with its simplified form. Further comparison with empirical superposition methods shows that local linear and wind product superposition methods based on the updated base flow also have comparable performance, with only discernable differences with the PG-IMCM method in the near-wake region of downstream turbines. Therefore, they are two attractive methods for engineering applications.

The wind direction is closely linked to the power performance and structural loads of wind turbines. Conventional nacelle‐mounted vanes or sonic anemometers face errors associated with airflow distortions caused by turbine blades. Nacelle‐mounted lidar systems offer line‐of‐sight speed measurements from multiple positions ahead of the rotor and rely on wind field reconstruction methods to predict the wind direction. This work considers three methods: the matrix inverse, the velocity azimuth display, and the physics‐informed neural network (PINN)–based methods. The first two are industrialized techniques that assume homogeneous flow. For flat terrain and offshore sites, the inhomogeneity of the mean flow is influenced by time‐averaging windows and turbine wakes. To illustrate the limitations and potential bias of wind direction estimates with homogeneous flow assumptions, we first present the bias using site measurement data. We then formulate a theoretical bias for a typical two‐beam lidar system. Next, we use openly available large eddy simulation data to evaluate the minute‐averaged wind direction estimates produced by the three methods. The first two methods are found to be unreliable, with maximum errors reaching close to 25° in the unwaked scenario and exceeding 30° in the waked case. As for the PINN‐based method, the errors remain within 10° across unwaked, waked, nonyawed, and yawed scenarios, even when only a 2D nonlinear convection equation is used as the physical constraint.

Global measurements of horizontal winds in cloud and precipitation systems represent a gap in the global observation system. The Wind Velocity Radar Nephoscope (WIVERN) mission, one of the two candidates to be the ESA's Earth Explorer 11 mission, aims at filling this gap based on a conically scanning W-band Doppler radar instrument. The determination of the antenna boresight mispointing angles and the impact of their uncertainty on the line of sight Doppler velocities is critical to achieve the mission requirements. While substantial industrial efforts are on their way to achieving accurate determination of the pointing, alternative (external) calibration approaches are currently under scrutiny. The correction of the line of sight Doppler velocity error introduced by the mispointing only needs knowledge of such mispointing angles and does not need the correction of the mispointing itself. Thus, this work discusses four methods applicable to the WIVERN radar that can be used at different timescales to characterize the antenna mispointing both in the azimuthal and in the elevation directions and to correct the error in the Doppler velocity induced by such mispointing. Results show that elevation mispointing is well corrected at very short timescales by monitoring the range at which the surface peak occurs. Azimuthal mispointing is harder but can be tackled by using the expected profiles of the non-moving surface Doppler velocity. Biases in pointing at longer timescales can be monitored by using a well-established reference database (e.g. ECMWF reanalysis) or ad-hoc ground-based calibrators. Although tailored to the WIVERN mission, the proposed methodologies can be extended to other Doppler concepts featuring conically scanning or slant viewing Doppler systems.