Novel strategy for wide-range wind vector measurement using the hybrid CP/CTD heating mode and sequential measuring and correcting

To improve the performance of wind sensors in the high velocity range, this paper proposes a wind measurement strategy for thermal wind velocity sensors that combines the constant power and constant temperature difference driving modes of the heating element. Based on the airflow distribution characteristics from fluid dynamics, sequential measurement and correction is proposed as a method of measuring wind direction. In addition, a wind velocity and direction measurement instrument was developed using the above-mentioned approaches. The test results showed that the proposed instrument can obtain large dynamic wind velocity measurements from 0 to 60 m/s. The wind velocity measurement accuracy was ±0.5 m/s in the common velocity range of 0–20 m/s and ±1 m/s in the high velocity range of 20–60 m/s. The wind direction accuracy was ±3° throughout the 360° range. The proposed approaches and instrument are not only practical but also capable of meeting the requirements of wide-range and large dynamic wind vector measurement applications.

New flexible retrieval for gusts and mean winds from Doppler wind lidars, tested for various scanning configurations

<p>In the Field Experiment on Sub-Mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL, www.fesstval.de) various phenomena in the atmospheric boundary layer are investigated. One goal is to detect wind gusts from measurements of a Doppler wind lidar (DWL). DWL’s allow the determination of wind vector profiles with a high vertical resolution (∼ 30 m) and therefore are an attractive alternative to metorological towers.</p><p>However, obtaining wind gusts from DWL measurements is not trivial because a monostatic lidar provides only a radial velocity, i.e., only one component of a three-dimensional vector per individual beam. Measurements in at least three linearly independent directions are therefore necessary to derive the wind vector. These must be performed sequentially, which prolongs the time interval for determining the wind vector and therefore limits the time resolution of the derived wind vector. In order to retrieve wind gusts, wind maxima of a few seconds, one needs to operate the instrument in a quick scanning mode. In this presentation, we show results from different scanning modes and discuss the method for retrieving wind gusts. We tested various configurations with respect to their ability to detect gusts and mean winds at the Boundary Layer Field Site in Falkenberg in autumn 2019. The DWL configurations that measure different lines-of-sight with rapid temporal repetitions have a lower signal-to-noise ratio (SNR) but the highest chance of detecting gusts.</p><p>We have developed a new retrieval method that skips prior SNR filtering and instead iteratively removes a fixed number of measurements that do not match a least-squares-fit. The least-squares-fit is then recalculated on the reduced set of measurements, and if necessary, this step is repeated. With appropriate retrieval steps and iteration criteria, our results suggest that prior filtering can be omitted.</p><p><br>We present the results of our new retrieval for eight different DWL configurations consisting of double-beam swinging, step-stare modes, and continuous-scanning modes. The evaluation is done by a comparison of the minimum, maximum and mean wind speed at 90 m a.g.l. against the reference measurements of a sonic anemometer that is located nearby. Ongoing work is addressing further comparison of our retrieved wind variables with unmanned aerial vehicles from the FESSTVaL campaign in summer 2020.</p>

Investigation of the turbulent three-dimensional wind field in the near-wake of a full-scale wind turbine using synchronized scanning wind lidars

<p>The objective of this work is to enhance the understanding of the mean wind and turbulence characteristics in the wake of a full-scale wind turbine.  Here, we present observations of the three-dimensional wind vector in the near wake of a wind turbine, a Vestas V52 with a 52-m rotor diameter.  The test turbine is located at the Risø campus of the Technical University of Denmark (DTU). The measurements were acquired using three, state-of-the-art, scanning, continuous-wave wind lidars, developed in DTU Wind Energy (<em>Short-Range WindScanner</em>). In our study, the area of focus was a vertical, two-dimensional plane at a distance of two rotor diameters from the wind turbine, in the downwind direction. Using the scanning lidars it was possible to derive spatially distributed estimations of the first and second-order moments of the wind vector within the vertical plane. The plane was within an area equal to 2.6 x 1.8 rotor diameters, towards the transverse and vertical direction respectively, covering a measuring range that included both the wake and free flow. The field test took place during a period of almost two weeks (July 2 - July 14, 2019). Approximately half of the time, the wind direction was favourable such that the measuring plane was covering a cross-section of the mean flow, which included the entire area where the wind speed deficit occurred. This data set enables the quantification of the wind speed deficit and the corresponding momentum deficit in the wake and reveals the turbulent layer that surrounds the mean wind speed deficit. Thus, allowing the investigation of the relation between the momentum fluxes and the local wind speed gradients, which is important for the understanding of the physical properties of the flow behind a wind turbine. Furthermore, we investigate the effect that wakes have on the vertical shear close to the ground, which can have a direct impact on the wind-surface interaction on the downwind side. Since the measuring plane was extended also to areas of the free flow, we compare the wind characteristics within the mean wake flow to the ones of the free flow. The knowledge of the features of the wake and the physical connection between the mean and turbulent flow provides a new detailed input for improved wake modelling. This is necessary for a more accurate prediction of the wake characteristics and can enable a more realistic quantification of the interaction between wind turbines in a wind farm, as well as the impact of the wake flow on the surrounding microclimate.</p>

Validation of the High-Altitude Wind and Rain Airborne Profiler during the Tampa Bay Rain Experiment

This paper deals with the validation of rain rate and wind speed measurements from the High-Altitude Wind and Rain Airborne Profiler (HIWRAP), which occurred in September 2013 when the NASA Global Hawk unmanned aerial vehicle passed over an ocean rain squall line in the Gulf of Mexico near the North Florida coast. The three-dimensional atmospheric rain distribution and the associated ocean surface wind vector field were simultaneously measured by two independent remote sensing and two in situ systems, namely the ground-based National Weather Service Next-Generation Weather Radar (NEXRAD); the European Space Agency satellite Advanced Scatterometer (ASCAT), and two instrumented weather buoys. These independent measurements provided the necessary data to calibrate the HIWRAP radar using the measured ocean radar backscatter and to validate the HIWRAP rain and wind vector retrievals against NEXRAD, ASCAT and ocean buoys observations. In addition, this paper presents data processing procedures for the HIWRAP instrument, including the development of a geometric model to collocate time-morphed rain rates from the NEXRAD radar with HIWRAP atmospheric rain profiles. Results of the rain rate intercomparison are presented, and they demonstrate excellent agreement with the NEXRAD time-interpolated rain volume scans. In our analysis, we find that HIWRAP produces wind and rain rates that are consistent with the supporting ground and satellite estimates, thereby providing validation of the geolocation, the calibration, and the geophysical retrieval algorithms for the HIWRAP instrument.

Automated Probabilistic Analysis and Parametric Modelling of the Seasonal-Diurnal Wind Vector

A refined and extended version of the Offset Elliptical Normal mixture model has been developed to parameterise the seasonal diurnal wind vector automatically. Automated using R scripts, the method eliminates any potential risk of confirmation bias posed by the manual supervision in the original method. Refinements to the method include the latest algorithms for clustering of Gaussian mixtures, with Bayesian regularisation to set the number of components and to limit the predisposition to overfit. A new extension uses fuzzy logic to evaluate the probability distributions, autocovariances and spectra of the random perturbations around the mean seasonal-diurnal variations for each component of the mixture. These additional parameters allow the predictions of the OEN model to be validated and its automated application demonstrated using the hourly METAR reports of mean wind speeds at Adelaide, South Australia, showing significant improvements over the previously published analysis. The OEN mixture model is directly applicable to a wide range of wind engineering applications where seasonal and diurnal variation is of importance.