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1
Heard, Maurice C. "Wind speed measurement." Journal of the Acoustical Society of America 81, no. 6 (1987): 2001. http://dx.doi.org/10.1121/1.394734.
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Zhong, Juhua, Zhongqi Cheng, and Wenchuan Guan. "Wind speed measurement by paper anemometer." Physics Education 46, no. 5 (2011): 578–82. http://dx.doi.org/10.1088/0031-9120/46/5/010.
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Song, YongDuan, ZhenYu Zhang, Peng Li, WenLiang Wang, and Ming Qin. "Robust adaptive variable speed control of wind power systems without wind speed measurement." Journal of Renewable and Sustainable Energy 5, no. 6 (2013): 063115. http://dx.doi.org/10.1063/1.4840035.
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Vasiljević, Nikola, Michael Harris, Anders Tegtmeier Pedersen, et al. "Wind sensing with drone-mounted wind lidars: proof of concept." Atmospheric Measurement Techniques 13, no. 2 (2020): 521–36. http://dx.doi.org/10.5194/amt-13-521-2020.
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Abstract. The fusion of drone and wind lidar technology introduces the exciting possibility of performing high-quality wind measurements virtually anywhere. We present a proof-of-concept (POC) drone–lidar system and report results from several test campaigns that demonstrate its ability to measure accurate wind speeds. The POC system is based on a dual-telescope continuous-wave (CW) lidar, with drone-borne telescopes and ground-based optoelectronics. Commercially available drone and gimbal units are employed. The demonstration campaigns started with a series of comparisons of the wind speed measurements acquired by the POC system to simultaneous measurements performed by nearby mast-based sensors. On average, an agreement down to about 0.1 m s−1 between mast- and drone-based measurements of the horizontal wind speed is found. Subsequently, the extent of the flow disturbance caused by the drone downwash was investigated. These tests vindicated the somewhat conservative choice of lidar measurement ranges made for the initial wind speed comparisons. Overall, the excellent results obtained without any drone motion correction and with fairly primitive drone position control indicate the potential of drone–lidar systems in terms of accuracy and applications. The next steps in the development are outlined and several potential applications are discussed.
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Lipecki, Tomasz, Paulina Jamińska-Gadomska, and Andrzej Sumorek. "Influence of Ultrasonic Wind Sensor Position on Measurement Accuracy under Full-Scale Conditions." Sensors 20, no. 19 (2020): 5640. http://dx.doi.org/10.3390/s20195640.
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A system designed for making field measurements of wind action on engineering structures is described. The system is composed of sonic anemometers, differential pressure sensors, a barometer, and a thermohygrometer. The focus of this study is to determine the indications of sonic anemometers; to accomplish this goal, wind tunnel tests were performed. The tests did not involve checking the accuracy of the devices themselves, but determining their indications under field measurement conditions where certain unavoidable errors resulting from their installation can appear. The anemometer measurement uncertainty with respect to wind speed and angle was determined. The devices were rotated in a horizontal plane and inclined against and with the mean wind speed direction in a wind tunnel. Different tunnel wind speeds were tested. The results indicate stable device readings at different horizontal plane positions at different wind speeds and a low sensitivity to changes in inclination against the inflow.
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Řeháček, D., T. Khel, J. Kučera, J. Vopravil, and M. Petera. "Effect of windbreaks on wind speed reduction and soil protection against wind erosion." Soil and Water Research 12, No. 2 (2017): 128–35. http://dx.doi.org/10.17221/45/2016-swr.
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Windbreaks form efficient soil protection against wind erosion particularly at the time when soil cover is not protected by the cultivated plant vegetation cover. The objective of this research was to evaluate windbreaks efficiency in terms of wind speed reduction. Wind speed along the windbreaks was measured in the cadastral areas of Dobrovíz and Středokluky (Czech Republic, Central Europe). The measurement was carried out by 4 stations placed at windward side (1 station at the distance of 3 times the height of the windbreak) and at leeward side of the windbreak (3 stations at the distance of 3, 6, and 9 times the height of the windbreak). Each station contained 2 anemometers situated 0.5 and 1 m above surface. The character of windbreak was described by terrestrial photogrammetry method as the value of optical porosity from the photo documentation of the windbreak at the time of field measurement. A significant dependence between the value of optical porosity and efficiency of windbreak emerged from the results. The correlation coefficient between optical porosity and wind speed reduction was in the range of 0.842 to 0.936 (statistical significance more than 95%). A significant effect of windbreak on airflow reduction was proven on the leeward side of windbreak in a belt corresponding to approximately six times the height of the windbreaks depending on the optical porosity and it was expressed by a polynomial equation.
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Meng, Wenchao, Qinmin Yang, and Youxian Sun. "Adaptive control of variable-speed wind energy conversion systems with inaccurate wind speed measurement." Transactions of the Institute of Measurement and Control 37, no. 1 (2014): 63–72. http://dx.doi.org/10.1177/0142331214531008.
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Shimada, Susumu, Jay Prakash Goit, Teruo Ohsawa, Tetsuya Kogaki, and Satoshi Nakamura. "Coastal Wind Measurements Using a Single Scanning LiDAR." Remote Sensing 12, no. 8 (2020): 1347. http://dx.doi.org/10.3390/rs12081347.
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A wind measurement campaign using a single scanning light detection and ranging (LiDAR) device was conducted at the Hazaki Oceanographical Research Station (HORS) on the Hazaki coast of Japan to evaluate the performance of the device for coastal wind measurements. The scanning LiDAR was deployed on the landward end of the HORS pier. We compared the wind speed and direction data recorded by the scanning LiDAR to the observations obtained from a vertical profiling LiDAR installed at the opposite end of the pier, 400 m from the scanning LiDAR. The best practice for offshore wind measurements using a single scanning LiDAR was evaluated by comparing results from a total of nine experiments using several different scanning settings. A two-parameter velocity volume processing (VVP) method was employed to retrieve the horizontal wind speed and direction from the radial wind speed. Our experiment showed that, at the current offshore site with a negligibly small vertical wind speed component, the accuracy of the scanning LiDAR wind speeds and directions was sensitive to the azimuth angle setting, but not to the elevation angle setting. In addition to the validations for the 10-minute mean wind speeds and directions, the application of LiDARs for the measurement of the turbulence intensity (TI) was also discussed by comparing the results with observations obtained from a sonic anemometer, mounted at the seaward end of the HORS pier, 400 m from the scanning LiDAR. The standard deviation obtained from the scanning LiDAR measurement showed a greater fluctuation than that obtained from the sonic anemometer measurement. However, the difference between the scanning LiDAR and sonic measurements appeared to be within an acceptable range for the wind turbine design. We discuss the variations in data availability and accuracy based on an analysis of the carrier-to-noise ratio (CNR) distribution and the goodness of fit for curve fitting via the VVP method.
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Uhlhorn, Eric W., Peter G. Black, James L. Franklin, Mark Goodberlet, James Carswell, and Alan S. Goldstein. "Hurricane Surface Wind Measurements from an Operational Stepped Frequency Microwave Radiometer." Monthly Weather Review 135, no. 9 (2007): 3070–85. http://dx.doi.org/10.1175/mwr3454.1.
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Abstract For the first time, the NOAA/Aircraft Operations Center (AOC) flew stepped frequency microwave radiometers (SFMRs) on both WP-3D research aircraft for operational hurricane surface wind speed measurement in 2005. An unprecedented number of major hurricanes provided ample data to evaluate both instrument performance and surface wind speed retrieval quality up to 70 m s−1 (Saffir–Simpson category 5). To this end, a new microwave emissivity–wind speed model function based on estimates of near-surface winds in hurricanes by global positioning system (GPS) dropwindsondes is proposed. For practical purposes, utilizing this function removes a previously documented high bias in moderate SFMR-measured wind speeds (10–50 m s−1), and additionally corrects an extreme wind speed (>60 m s−1) underestimate. The AOC operational SFMRs yield retrievals that are precise to within ∼2% at 30 m s−1, which is a factor of 2 improvement over the NOAA Hurricane Research Division’s SFMR, and comparable to the precision found here for GPS dropwindsonde near-surface wind speeds. A small (1.6 m s−1), but statistically significant, overall high bias was found for independent SFMR measurements utilizing emissivity data not used for model function development. Across the range of measured wind speeds (10–70 m s−1), SFMR 10-s averaged wind speeds are within 4 m s−1 (rms) of the dropwindsonde near-surface estimate, or 5%–25% depending on speed. However, an analysis of eyewall peak wind speeds indicates an overall 2.6 m s−1 GPS low bias relative to the peak SFMR estimate on the same flight leg, suggesting a real increase in the maximum wind speed estimate due to SFMR’s high-density sampling. Through a series of statistical tests, the SFMR is shown to reduce the overall bias in the peak surface wind speed estimate by ∼50% over the current flight-level wind reduction method and is comparable at extreme wind speeds. The updated model function is demonstrated to behave differently below and above the hurricane wind speed threshold (∼32 m s−1), which may have implications for air–sea momentum and kinetic energy exchange. The change in behavior is at least qualitatively consistent with recent laboratory and field results concerning the drag coefficient in high wind speed conditions, which show a fairly clear “leveling off” of the drag coefficient with increased wind speed above ∼30 m s−1. Finally, a composite analysis of historical data indicates that the earth-relative SFMR peak wind speed is typically located in the hurricane’s right-front quadrant, which is consistent with previous observational and theoretical studies of surface wind structure.
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Allers, Katelyn N., Johanna M. Vos, Beth A. Biller, and Peter K. G. Williams. "A measurement of the wind speed on a brown dwarf." Science 368, no. 6487 (2020): 169–72. http://dx.doi.org/10.1126/science.aaz2856.
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Zonal (latitudinal) winds dominate the bulk flow of planetary atmospheres. For gas giant planets such as Jupiter, the motion of clouds can be compared with radio emissions from the magnetosphere, which is connected to the planet’s interior, to determine the wind speed. In principle, this technique can be applied to brown dwarfs and/or directly imaged exoplanets if periods can be determined for both the infrared and radio emissions. We apply this method to measure the wind speeds on the brown dwarf 2MASS J10475385+2124234. The difference between the radio period of 1.751 to 1.765 hours and infrared period of 1.741 ± 0.007 hours implies a strong wind (+650 ± 310 meters per second) proceeding eastward. This could be due to atmospheric jet streams and/or low frictional drag at the bottom of the atmosphere.
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Yonehara, Yoshinari, Yusuke Goto, Ken Yoda, et al. "Flight paths of seabirds soaring over the ocean surface enable measurement of fine-scale wind speed and direction." Proceedings of the National Academy of Sciences 113, no. 32 (2016): 9039–44. http://dx.doi.org/10.1073/pnas.1523853113.
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Ocean surface winds are an essential factor in understanding the physical interactions between the atmosphere and the ocean. Surface winds measured by satellite scatterometers and buoys cover most of the global ocean; however, there are still spatial and temporal gaps and finer-scale variations of wind that may be overlooked, particularly in coastal areas. Here, we show that flight paths of soaring seabirds can be used to estimate fine-scale (every 5 min, ∼5 km) ocean surface winds. Fine-scale global positioning system (GPS) positional data revealed that soaring seabirds flew tortuously and ground speed fluctuated presumably due to tail winds and head winds. Taking advantage of the ground speed difference in relation to flight direction, we reliably estimated wind speed and direction experienced by the birds. These bird-based wind velocities were significantly correlated with wind velocities estimated by satellite-borne scatterometers. Furthermore, extensive travel distances and flight duration of the seabirds enabled a wide range of high-resolution wind observations, especially in coastal areas. Our study suggests that seabirds provide a platform from which to measure ocean surface winds, potentially complementing conventional wind measurements by covering spatial and temporal measurement gaps.
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Rettenmeier, Andreas, David Schlipf, Ines Würth, and Po Wen Cheng. "Power Performance Measurements of the NREL CART-2 Wind Turbine Using a Nacelle-Based Lidar Scanner." Journal of Atmospheric and Oceanic Technology 31, no. 10 (2014): 2029–34. http://dx.doi.org/10.1175/jtech-d-13-00154.1.
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Abstract Different certification procedures in wind energy, such as power performance testing or load estimation, require measurements of the wind speed, which is set in relation to the electrical power output or the turbine loading. The wind shear affects the behavior of the turbine as hub heights and rotor diameters of modern wind turbines increase. Different measurement methods have been developed to take the wind shear into account. In this paper an approach is presented where the wind speed is measured from the nacelle of a wind turbine using a scanning lidar system. The measurement campaign was performed on the two-bladed Controls Advanced Research Turbine (CART-2) at the National Wind Technology Center in Colorado. The wind speed of the turbine inflow was measured and recalculated in three different ways: using an anemometer installed on a meteorological mast, using the nacelle-based lidar scanner, and using the wind turbine itself. Here, the wind speed was recalculated from turbine data using the wind turbine as a big horizontal anemometer. Despite the small number of useful data, the correlation between this so-called rotor effective wind speed and the wind speed measured by the scanning nacelle-based lidar is high. It could be demonstrated that a nacelle-based scanning lidar system provides accurate measurements of the wind speed converted by a wind turbine. This is a first step, and it provides evidence to support further investigations using a much more extensive dataset and refines the parameters in the measurement process.
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Eltamaly, Ali Mohamed, Mamdooh Al-Saud, Khairy Sayed, and Ahmed G. Abo-Khalil. "Sensorless Active and Reactive Control for DFIG Wind Turbines Using Opposition-Based Learning Technique." Sustainability 12, no. 9 (2020): 3583. http://dx.doi.org/10.3390/su12093583.
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In this paper, a wind speed sensorless control method for doubly-fed induction generator (DFIG) control in wind energy systems is proposed. This method is based on using opposition-based learning (OBL) in optimizing the parameters of the support vector regression (SVR) algorithm. These parameters are tuned by applying particle swarm optimization (PSO) method. As a general rule, wind speed measurements are usually done using an anemometer. The measured wind speed by the anemometer is taken at the level of the blades. In a high-power wind turbine, the blade diameter is very large which makes the measurement of the wind speed at a single point inaccurate. Moreover, using anemometers also increases the maintenance cost, complexity and the system cost. Therefore, estimating the wind speed in variable speed wind power systems gives a precise amount of wind speed which is then used in the generator control. The proposed method uses the generator characteristics in mapping a relationship between the generated power, rotational speed and wind speed. This process is carried on off-line and the relationship is then used online to deduce the wind speed based on the obtained relationship. Using OBL with PSO-SVR to tune the SVR parameters accelerates the process to get the optimum parameters in different wind speeds.
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Fitch, Kyle E., Chaoxun Hang, Ahmad Talaei, and Timothy J. Garrett. "Arctic observations and numerical simulations of surface wind effects on Multi-Angle Snowflake Camera measurements." Atmospheric Measurement Techniques 14, no. 2 (2021): 1127–42. http://dx.doi.org/10.5194/amt-14-1127-2021.
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Abstract. Ground-based measurements of frozen precipitation are heavily influenced by interactions of surface winds with gauge-shield geometry. The Multi-Angle Snowflake Camera (MASC), which photographs hydrometeors in free-fall from three different angles while simultaneously measuring their fall speed, has been used in the field at multiple midlatitude and polar locations both with and without wind shielding. Here, we present an analysis of Arctic field observations – with and without a Belfort double Alter shield – and compare the results to computational fluid dynamics (CFD) simulations of the airflow and corresponding particle trajectories around the unshielded MASC. MASC-measured fall speeds compare well with Ka-band Atmospheric Radiation Measurement (ARM) Zenith Radar (KAZR) mean Doppler velocities only when winds are light (≤5ms-1) and the MASC is shielded. MASC-measured fall speeds that do not match KAZR-measured velocities tend to fall below a threshold value that increases approximately linearly with wind speed but is generally <0.5ms-1. For those events with wind speeds ≤1.5ms-1, hydrometeors fall with an orientation angle mode of 12∘ from the horizontal plane, and large, low-density aggregates are as much as 5 times more likely to be observed. Simulations in the absence of a wind shield show a separation of flow at the upstream side of the instrument, with an upward velocity component just above the aperture, which decreases the mean particle fall speed by 55 % (74 %) for a wind speed of 5 m s−1 (10 m s−1). We conclude that accurate MASC observations of the microphysical, orientation, and fall speed characteristics of snow particles require shielding by a double wind fence and restriction of analysis to events where winds are light (≤5ms-1). Hydrometeors do not generally fall in still air, so adjustments to these properties' distributions within natural turbulence remain to be determined.
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Lillibridge, John, Remko Scharroo, Saleh Abdalla, and Doug Vandemark. "One- and Two-Dimensional Wind Speed Models for Ka-Band Altimetry." Journal of Atmospheric and Oceanic Technology 31, no. 3 (2014): 630–38. http://dx.doi.org/10.1175/jtech-d-13-00167.1.
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abstract SARAL—the Satellite with ARgos and ALtiKa—is the first satellite radar altimetry mission to fly a Ka-band instrument (AltiKa). Ocean backscatter measurements in the Ka band suffer larger signal attenuation due to water vapor and atmospheric liquid water than those from Ku-band altimeters. An attenuation algorithm is provided, based on radar propagation theory, which is a function of atmospheric pressure, temperature, water vapor, and liquid water content. Because of the nature of the air–sea interactions between wind and surface gravity waves, the shorter wavelength Ka-band backscatter exhibits a different relationship with wind speed than at Ku band, particularly at moderate to high wind speeds. This paper presents a new one-dimensional wind speed model, as a function of backscatter only, and a two-dimensional model, as a function of backscatter and significant wave height, tuned to AltiKa’s backscatter measurements. The performance of these new Ka-band altimeter wind speed models is assessed through validation with independent ocean buoy wind speeds. The results indicate wind measurement accuracy comparable to that observed at Ku band with only slightly elevated noise in the wind estimates.
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Rautenberg, Alexander, Martin Graf, Norman Wildmann, Andreas Platis, and Jens Bange. "Reviewing Wind Measurement Approaches for Fixed-Wing Unmanned Aircraft." Atmosphere 9, no. 11 (2018): 422. http://dx.doi.org/10.3390/atmos9110422.
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One of the biggest challenges in probing the atmospheric boundary layer with small unmanned aerial vehicles is the turbulent 3D wind vector measurement. Several approaches have been developed to estimate the wind vector without using multi-hole flow probes. This study compares commonly used wind speed and direction estimation algorithms with the direct 3D wind vector measurement using multi-hole probes. This was done using the data of a fully equipped system and by applying several algorithms to the same data set. To cover as many aspects as possible, a wide range of meteorological conditions and common flight patterns were considered in this comparison. The results from the five-hole probe measurements were compared to the pitot tube algorithm, which only requires a pitot-static tube and a standard inertial navigation system measuring aircraft attitude (Euler angles), while the position is measured with global navigation satellite systems. Even less complex is the so-called no-flow-sensor algorithm, which only requires a global navigation satellite system to estimate wind speed and wind direction. These algorithms require temporal averaging. Two averaging periods were applied in order to see the influence and show the limitations of each algorithm. For a window of 4 min, both simplifications work well, especially with the pitot-static tube measurement. When reducing the averaging period to 1 min and thereby increasing the temporal resolution, it becomes evident that only circular flight patterns with full racetracks inside the averaging window are applicable for the no-flow-sensor algorithm and that the additional flow information from the pitot-static tube improves precision significantly.
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Hanlon, Tara, and David Risk. "Using computational fluid dynamics and field experiments to improve vehicle-based wind measurements for environmental monitoring." Atmospheric Measurement Techniques 13, no. 1 (2020): 191–203. http://dx.doi.org/10.5194/amt-13-191-2020.
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Abstract. Vehicle-based measurements of wind speed and direction are presently used for a range of applications, including gas plume detection. Many applications use mobile wind measurements without knowledge of the limitations and accuracy of the mobile measurement system. Our research objective for this field-simulation study was to understand how anemometer placement and the vehicle's external airflow field affect measurement accuracy of vehicle-mounted anemometers. Computational fluid dynamic (CFD) simulations were generated in ANSYS Fluent to model the external flow field of a research truck under varying vehicle speed and wind yaw angle. The CFD simulations provided a quantitative description of fluid flow surrounding the vehicle and demonstrated that the change in wind speed magnitude from the inlet increased as the wind yaw angle between the inlet and the vehicle's longitudinal axis increased. The CFD results were used to develop empirical speed correction factors at specified yaw angles and to derive an aerodynamics-based correction function calibrated for wind yaw angle and anemometer placement. For comparison with CFD, we designed field tests on a square, 12.8 km route in flat, treeless terrain with stationary sonic anemometers positioned at each corner. The route was driven in replicate under varying wind conditions and vehicle speeds. The vehicle-based anemometer measurements were corrected to remove the vehicle speed and course vector. From the field trials, we observed that vehicle-based wind speed measurements differed in average magnitude in each of the upwind, downwind, and crosswind directions. The difference from stationary anemometers increased as the yaw angle between the wind direction and the truck's longitudinal axis increased, confirming the vehicle's impact on the surrounding flow field and validating the trends in CFD. To further explore the accuracy of CFD, we applied the function derived from the simulations to the field data and again compared these with stationary measurements. From this study, we were able to make recommendations for anemometer placement, demonstrate the importance of applying aerodynamics-based correction factors to vehicle-based wind measurements, and identify ways to improve the empirical aerodynamic-based correction factors.
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Shimura, Tomoya, Minoru Inoue, Hirofumi Tsujimoto, Kansuke Sasaki, and Masato Iguchi. "Estimation of Wind Vector Profile Using a Hexarotor Unmanned Aerial Vehicle and Its Application to Meteorological Observation up to 1000 m above Surface." Journal of Atmospheric and Oceanic Technology 35, no. 8 (2018): 1621–31. http://dx.doi.org/10.1175/jtech-d-17-0186.1.
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AbstractSmall unmanned aerial vehicles (UAVs), also known as drones, have recently become promising tools in various fields. We investigated the feasibility of wind vector profile measurement using an ultrasonic anemometer installed on a 1-m-wide hexarotor UAV. Wind vectors measured by the UAV were compared to observations by a 55-m-high meteorological tower, over a wide range of wind speed conditions up to 11 m s−1, which is a higher wind speed range than those used in previous studies. The wind speeds and directions measured by the UAV and the tower were in good agreement, with a root-mean-square error of 0.6 m s−1 and 12° for wind speed and direction, respectively. The developed method was applied to field meteorological observations near a volcano, and the wind vector profiles, along with temperature and humidity, were measured by the UAV for up to an altitude of 1000 m, which is a higher altitude range than those used in previous studies. The wind vector profile measured by the UAV was compared with Doppler lidar measurements (collected several kilometers away from the UAV measurements) and was found to be qualitatively similar to that captured by the Doppler lidar, and it adequately represented the features of the atmospheric boundary layer. The feasibility of wind profile measurement up to 1000 m by a small rotor-based UAV was clarified over a wide range of wind speed conditions.
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Yan, Qiushuang, Chenqing Fan, Jie Zhang, and Junmin Meng. "Understanding Ku-Band Ocean Radar Backscatter at Low Incidence Angles under Weak to Severe Wind Conditions by Comparison of Measurements and Models." Remote Sensing 12, no. 20 (2020): 3445. http://dx.doi.org/10.3390/rs12203445.
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The rain-free normalized radar cross-section (NRCS) measurements from the Ku-band precipitation radars (PRs) aboard the tropical rainfall measuring mission (TRMM) and the global precipitation measurement (GPM) mission, along with simultaneous sea surface wind truth from buoy observations, stepped-frequency microwave radiometer (SFMR) measurements, and H*Wind analyses, are used to investigate the abilities of the quasi-specular scattering models, i.e., the physical optics model (PO) and the classical and improved geometrical optics models (GO and GO4), to reproduce the Ku-band NRCS at low incidence angles of 0–18° over the wind speed range of 0–45 m/s. On this basis, the limitations of the quasi-specular scattering theory and the effects of wave breaking are discussed. The results show that the return caused by quasi-specular reflection is affected significantly by the presence of background swell waves at low winds. At moderate wind speeds of 5–15 m/s, the NRCS is still dominated by the quasi-specular reflection, and the wave breaking starts to work but its contribution is very small, thus, the models are found in excellent agreement with the measurements. With wind speed increasing, the impact of wave breaking increases, whereas the role of standard quasi-specular reflection decreases. The wave breaking impact on NRCS is first visible at incidence angles near 18° as wind speed exceeds about 20 m/s, then it becomes dominant when wind speed exceeds about 37 m/s where the NRCS is insensitive to wind speed and depends linearly on incidence angle, which cannot be explained by the standard quasi-specular scattering theory.
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Yang, D. K., Y. Q. Zhang, Y. Lu, and Q. S. Zhang. "GPS Reflections for Sea Surface Wind Speed Measurement." IEEE Geoscience and Remote Sensing Letters 5, no. 4 (2008): 569–72. http://dx.doi.org/10.1109/lgrs.2008.2000620.
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Garrison, J. L., A. Komjathy, V. U. Zavorotny, and S. J. Katzberg. "Wind speed measurement using forward scattered GPS signals." IEEE Transactions on Geoscience and Remote Sensing 40, no. 1 (2002): 50–65. http://dx.doi.org/10.1109/36.981349.
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Ren, Guorui, Jinfu Liu, Jie Wan, Yufeng Guo, Daren Yu, and Jizhen Liu. "Measurement and statistical analysis of wind speed intermittency." Energy 118 (January 2017): 632–43. http://dx.doi.org/10.1016/j.energy.2016.10.096.
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Rokhmanila, Gunastuti, Rosiana, and Kifni. "The Design For Wind Speed Measurement Using Labview." Journal of Physics: Conference Series 1772, no. 1 (2021): 012005. http://dx.doi.org/10.1088/1742-6596/1772/1/012005.
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Balasubramaniam, Rajeswari, and Christopher Ruf. "Neural Network Based Quality Control of CYGNSS Wind Retrieval." Remote Sensing 12, no. 17 (2020): 2859. http://dx.doi.org/10.3390/rs12172859.
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Global Navigation Satellite System – Reflectometry (GNSS-R) is a relatively new field in remote sensing that uses reflected GPS signals from the Earth’s surface to study the state of the surface geophysical parameters under observation. The CYGNSS is a first of its kind GNSS-R constellation mission launched in December 2016. It aims at providing high quality global scale GNSS-R measurements that can reliably be used for ocean science applications such as the study of ocean wind speed dynamics, tropical cyclone genesis, coupled ocean wave modelling, and assimilation into Numerical Weather Prediction models. To achieve this goal, strong quality control filters are needed to detect and remove outlier measurements. Currently, quality control of CYGNSS data products are based on fixed thresholds on various engineering, instrument, and measurement conditions. In this work we develop a Neural Network based quality control filter for automated outlier detection of CYGNSS retrieved winds. The primary merit of the proposed ML filter is its ability to better account for interactions between the individual engineering, instrument and measurement conditions than can separate thresholded flags for each one. Use of Machine Learning capabilities to capture inherent patterns in the data can create an efficient and effective mechanism to detect and remove outlier measurements. The resulting filter has a probability of outlier detection (PD) >75% and False Alarm Rate (FAR) < 20% for a wind speed range of 5 to 18 m/s. At least 75% of the outliers with wind speed errors of at least 5 m/s are removed while ~100% of the outliers with wind speed errors of at least 10 m/s are removed. This filter significantly improves data quality. The standard deviation of wind speed retrieval error is reduced from 2.6 m/s without the filter to 1.7 m/s with it over a wind speed range of 0 to 25 m/s. The design space for this filter is also analyzed in this work to characterize trade-offs between PD and FAR. Currently the filter performance is applicable only up to moderate wind speeds, as sufficient data is available only in this range to train the filter, as a way forward, more data over time can help expand the usability of this filter to higher wind speed ranges as well.
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Aniskevich, S., V. Bezrukovs, U. Zandovskis, and D. Bezrukovs. "Modelling the Spatial Distribution of Wind Energy Resources in Latvia." Latvian Journal of Physics and Technical Sciences 54, no. 6 (2017): 10–20. http://dx.doi.org/10.1515/lpts-2017-0037.
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AbstractThe paper studies spatial wind energy flow distribution in Latvia based on wind speed measurements carried out at an altitude of 10mover a period of two years, from 2015 to 2016. The measurements, with 1minincrements, were carried out using certified measuring instruments installed at 22 observation stations of the Latvian National Hydrometeorological and Climatological Service of the Latvian Environment, Geology and Meteorology Centre (LEGMC). The models of the spatial distribution of averaged wind speed and wind energy density were developed using the method of spatial interpolation based on the historical measurement results and presented in the form of colour contour maps with a 1×1kmresolution. The paper also provides the results of wind speed spatial distribution modelling using a climatological reanalysis ERA5 at the altitudes of 10, 54, 100 and 136mwith a 31×31kmresolution. The analysis includes the comparison of actual wind speed measurement results with the outcomes of ERA5 modelling for meteorological observation stations in Ainazi, Daugavpils, Priekuli, Saldus and Ventspils.
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Natili, Francesco, Francesco Castellani, Davide Astolfi, and Matteo Becchetti. "Video-Tachometer Methodology for Wind Turbine Rotor Speed Measurement." Sensors 20, no. 24 (2020): 7314. http://dx.doi.org/10.3390/s20247314.
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The measurement of the rotational speed of rotating machinery is typically performed based on mechanical adherence; for example, in encoders. Nevertheless, it can be of interest in various types of applications to develop contactless vision-based methodologies to measure the speed of rotating machinery. In particular, contactless rotor speed measurement methods have several potential applications for wind turbine technology, in the context of non-intrusive condition monitoring approaches. The present study is devoted exactly to this problem: a ground level video-tachometer measurement technique and an image analysis algorithm for wind turbine rotor speed estimation are proposed. The methodology is based on the comparison between a reference frame and each frame of the video through the covariance matrix: a covariance time series is thus obtained, from which the rotational speed is estimated by passing to the frequency domain through the spectrogram. This procedure guarantees the robustness of the rotational speed estimation, despite the intrinsic non-stationarity of the system and the possible signal disturbances. The method is tested and discussed based on two experimental environments with different characteristics: the former is a small wind turbine model (with a 0.45 m rotor diameter) in the wind tunnel facility of the University of Perugia, whose critical aspect is the high rotational speed (up to the order of 1500 RPM). The latter test case is a wind turbine with a 44 m rotor diameter which is part of an industrial wind farm: in this case, the critical point regards the fact that measurements are acquired in uncontrolled conditions. It is shown that the method is robust enough to overcome the critical aspects of both test cases and to provide reliable rotational speed estimates.
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Coquilla, Rachael V., John Obermeier, and Bruce R. White. "Calibration Procedures and Uncertainty in Wind Power Anemometers." Wind Engineering 31, no. 5 (2007): 303–16. http://dx.doi.org/10.1260/030952407783418720.
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Accurate wind measurements are critical in evaluating wind turbine power performance and site assessment. In a turbine power performance evaluation, wind speed readings are matched with corresponding turbine power measurements to produce a power curve for the turbine. For site assessment, the distribution of measured wind speed is used to determine the predicted annual energy production from the wind. Since wind power is proportional to the cube of the wind speed, a small error in the wind measurement could translate to a much greater error in the predicted wind power, which emphasizes the importance of having accurate wind speed readings. To acquire such precision in wind data, it is recommended that individually calibrated anemometers be employed. With these calibrations, it is also recommended that the uncertainty in the calibration be reported so that it may be used not only in the overall uncertainty for turbine power curves and site assessments, but also in improving the performance of an anemometer. A method of presenting calibration uncertainty is defined in the standard IEC 61400-12-1. However, the standard only refers to the measurement uncertainty of the reference wind speed from the particular test facility. It does not include the uncertainty in the anemometer linear transfer function and the errors directly made by the anemometer signal. This paper will discuss: 1) the details of uncertainty reporting as defined by IEC 61400-12-1, 2) a method of extending the uncertainty to include the errors when using the linear transfer function, and 3) a qualitative description of how to determine the uncertainty in a wind speed measurement in the field.
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Lucio-Eceiza, Etor E., J. Fidel González-Rouco, Jorge Navarro, Hugo Beltrami, and Jorge Conte. "Quality Control of Surface Wind Observations in Northeastern North America. Part II: Measurement Errors." Journal of Atmospheric and Oceanic Technology 35, no. 1 (2018): 183–205. http://dx.doi.org/10.1175/jtech-d-16-0205.1.
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AbstractA quality control (QC) process has been developed and applied to an observational database of surface wind speed and wind direction in northeastern North America. The database combines data from three datasets of different initial quality, including a total of 526 land stations and buoys distributed over the provinces of eastern Canada and five adjacent northeastern U.S. states. The data span from 1953 to 2010. The first part of the QC deals with data management issues and is developed in a companion paper. Part II, presented herein, is focused on the detection of measurement errors and deals with low-variability errors, like the occurrence of unrealistically long calms, and high-variability problems, like rapid changes in wind speed; some types of biases in wind speed and wind direction are also considered. About 0.5% (0.16%) of wind speed (wind direction) records have been flagged. Additionally, 15.87% (1.73%) of wind speed (wind direction) data have been corrected. The most pervasive error type in terms of affected sites and erased data corresponds to unrealistic low wind speeds (89% of sites affected with 0.35% records removed). The amount of detected and corrected/removed records in Part II (~9%) is approximately two orders of magnitude higher than that of Part I. Both management and measurement errors are shown to have a discernible impact on the statistics of the database.
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Basse, Alexander, Lukas Pauscher, and Doron Callies. "Improving Vertical Wind Speed Extrapolation Using Short-Term Lidar Measurements." Remote Sensing 12, no. 7 (2020): 1091. http://dx.doi.org/10.3390/rs12071091.
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This study investigates how short-term lidar measurements can be used in combination with a mast measurement to improve vertical extrapolation of wind speed. Several methods are developed and analyzed for their performance in estimating the mean wind speed, the wind speed distribution, and the energy yield of an idealized wind turbine at the target height of the extrapolation. These methods range from directly using the wind shear of the short-term measurement to a classification approach based on commonly available environmental parameters using linear regression. The extrapolation strategies are assessed using data of ten wind profiles up to 200 m measured at different sites in Germany. Different mast heights and extrapolation distances are investigated. The results show that, using an appropriate extrapolation strategy, even a very short-term lidar measurement can significantly reduce the uncertainty in the vertical extrapolation of wind speed. This observation was made for short as well as for very large extrapolation distances. Among the investigated methods, the linear regression approach yielded better results than the other methods. Integrating environmental variables into the extrapolation procedure further increased the performance of the linear regression approach. Overall, the extrapolation error in (theoretical) energy yield was decreased by around 50% to 70% on average for a lidar measurement of approximately one to two months depending on the extrapolation height and distance. The analysis of seasonal patterns revealed that appropriate extrapolation strategies can also significantly reduce the seasonal bias that is connected to the season during which the short-term measurement is performed.
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Chen, R., J. Liu, E. Kang, et al. "Precipitation measurement intercomparison in the Qilian Mountains, Northeastern Tibetan Plateau." Cryosphere Discussions 9, no. 2 (2015): 2201–30. http://dx.doi.org/10.5194/tcd-9-2201-2015.
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Abstract. Systematic errors in gauge-measured precipitation are well-known but no reports have come from the Tibet Plateau. An intercomparison experiment was carried out from September 2010 to September 2014 in the Hulu watershed, northeastern Tibet Plateau. Precipitation gauges included a Chinese standard precipitation gauge (CSPG), a CSPG with Alter shelter (Alter), a Pit type gauge with the CSPG (Pit) and a Double-Fence International Reference with Tretyakov shelter and CSPG (DFIR). The intercomparison experiments show that the Pit gauge caught 1% more rainfall, 2% more mixed precipitation, 4% less snowfall and 0.8% more precipitation (all types) than the DFIR from September 2012 to September 2014. The Pit caught 4% more rainfall, 21% more snow and 16% more mixed precipitation than the CSPG. The DFIR caught 3% more rainfall, 27% more snowfall, and 13% more mixed precipitation than the CSPG, respectively. For rain and mixed precipitation, the catch ratios (CRs) for the gauges are ranked as follows: CRPit > CRDFIR > CRAlter > CRCSPG. For snowfall, the CRs are ranked as follows: CRDFIR > CRPit > CRAlter > CRCSPG. Catch ratio vs. 10 m wind speed indicates that with increasing wind speed from 0 to 4.5 m s−1, the CRCSPG or CRAlter decreased slightly. For mixed precipitation, the ratios of DFIR/Alter or DFIR/Pit vs. wind speed show that wind speed has no significant effect on catch ratio below 3.5 m s−1. For snowfall, the ratio of CSPG/DFIR or Alter/DFIR vs. wind speed shows that catch ratio decreases with increasing wind speed. The calibration equations for three different precipitation types for the CSPG and Alter were established with 10 m wind speeds based on the CR vs. wind speed analysis. Results indicate that combined use of the DFIR and the Pit as reference gauges for snow and rainfall, respectively, could enhance precipitation observation precision. Applicable regions for the Pit gauge or the DFIR as representative gauges for all precipitation types are present in China.
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Zhao, Yuefeng, Xiaojie Zhang, Yurong Zhang, et al. "Data Processing and Analysis of Eight-Beam Wind Profile Coherent Wind Measurement Lidar." Remote Sensing 13, no. 18 (2021): 3549. http://dx.doi.org/10.3390/rs13183549.
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Real-time measurement of atmospheric wind field parameters plays an important role in weather analysis and forecasting, including improving the efficiency of wind energy, particle tracking, boundary layer measurements, and airport security. In this study, a wind profile coherent wind Light Detection and Ranging (Lidar) measurement with a wavelength of 1.55 µm was developed and demonstrated based on the principle of eight-beam velocimetry. The wind speed information was retrieved, and vertical and horizontal profiles were calculated via power spectrum estimation of sampled echo signals through the measurement of the atmospheric wind field in Hefei for several consecutive days. The experimental results show that the wind profiles produced using different techniques are quite consistent and the standard error is less than 0.42 m/s compared with three-beam and five-beam wind measurements.
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Silva, Murilo T., Eric W. Gill, and Weimin Huang. "An Improved Estimation and Gap-Filling Technique for Sea Surface Wind Speeds Using NARX Neural Networks." Journal of Atmospheric and Oceanic Technology 35, no. 7 (2018): 1521–32. http://dx.doi.org/10.1175/jtech-d-18-0001.1.
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AbstractThis work presents the use of a nonlinear autoregressive neural network to obtain an improved estimate of sea surface winds, taking Placentia Bay, Newfoundland and Labrador, Canada, as a study case. The network inputs and delays were chosen through cross correlation with the target variable. The proposed method was compared with five other wind speed estimation techniques, outperforming them in correlation, precision, accuracy, and bias levels. As an extension, the temporal gap filling of missing wind speed data during a storm has been considered. Data containing a measurement gap from a 40-yr windstorm that hit the same location has been used. The proposed method filled the gaps in the dataset with a high degree of correlation with measurements obtained by surrounding stations. The method presented in this work showed promising results that could be extended to estimate wind speeds in other locations and filling gaps in other datasets.
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Newsom, Rob K., W. Alan Brewer, James M. Wilczak, Daniel E. Wolfe, Steven P. Oncley, and Julie K. Lundquist. "Validating precision estimates in horizontal wind measurements from a Doppler lidar." Atmospheric Measurement Techniques 10, no. 3 (2017): 1229–40. http://dx.doi.org/10.5194/amt-10-1229-2017.
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Abstract. 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.
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Yang, Ke, Wen Hai Shi, and Zheng Nong Li. "Whole Process Wind Characteristics Field Measurements of a Strong Wind." Advanced Materials Research 243-249 (May 2011): 5094–100. http://dx.doi.org/10.4028/www.scientific.net/amr.243-249.5094.
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This paper presents field measurement results of boundary layer wind characteristics over typical open country during the passages of typhoon Fung-wong passed by Wenzhou in July 2008. The field data such as wind speed and wind direction were measured from two propeller anemometers placed at the height of about 30m. The measured wind data are analyzed to obtain the information on mean wind speed and direction, turbulence intensity, gust factor, turbulence integral length scale and spectra of wind speed fluctuations. The results clearly demonstrate that the turbulence intensity and gust factor of typhoon Fung-wong are larger than normal, and there is a tendency for the turbulence intensities to decrease with the increase of the mean wind speed, however, there is another tendency for the turbulence integral length scale to increase with the increase of the mean wind speed. The power spectral densities of fluctuating wind speed in longitudinal and lateral directions obtained from the measured wind speed data roughly fit with Von Karman spectra. The results presented in this paper are expected to be of use to researchers and engineers involved in design of low-rise buildings.
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Plant, William J., William C. Keller, and Kenneth Hayes. "Simultaneous Measurement of Ocean Winds and Waves with an Airborne Coherent Real Aperture Radar." Journal of Atmospheric and Oceanic Technology 22, no. 7 (2005): 832–46. http://dx.doi.org/10.1175/jtech1724.1.
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Abstract A coherent, X-band airborne radar has been developed to measure wind speed and direction simultaneously with directional wave spectra on the ocean. The coherent real aperture radar (CORAR) measures received power, mean Doppler shifts, and mean Doppler bandwidths from small-resolution cells on the ocean surface and converts them into measurements of winds and waves. The system operates with two sets of antennas, one rotating and one looking to the side of the airplane. The rotating antennas yield neutral wind vectors at a height of 10 m above the ocean surface using a scatterometer model function to relate measured cross sections to wind speed and direction. The side-looking antennas produce maps of normalized radar cross section and line-of-sight velocity from which directional ocean wave spectra may be obtained. Capabilities of CORAR for wind and wave measurement are illustrated using data taken during the Shoaling Waves Experiment (SHOWEX) sponsored by the Office of Naval Research. Wind vectors measured by CORAR agree well with those measured by nearby buoys. Directional wave spectra obtained by CORAR also agree with buoy measurements and illustrate that offshore winds can produce dominant waves at an angle to the wind vector that are in good agreement with the measurements. The best agreement is produced using the Joint North Sea Wave Project (JONSWAP) parameterizations of the development of wave height and period with fetch.
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Zhou, Shu Dao, Yong Qi Jin, Ying Qiang Wang, and Min Wang. "Improvement of Wind Speed Measurement Model by Meteorological UAV and System Design." Advanced Materials Research 542-543 (June 2012): 591–94. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.591.
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At present, the error of wind speed measurement by meteorological UAV is great, in order to solve this problem, this paper identified the major sources of error by the analysis of the wind measurement model. Found that the airspeed error caused by the aircraft acceleration for the airspeed is very small, so classic airspeed measurement calculations often ignore the impact of aircraft acceleration, but the value of wind speed is smaller, so that the error caused by aircraft acceleration relative to the wind speed cannot be ignored. Therefore, this article added aircraft acceleration to re-establish the airspeed measurement model, and got an improved wind speed of calculation model. In order to verify the model, the wind speed measurement system was been designed, the system design diagram and related data processing, solver method were presented.
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Kurbatova, Maria, Konstantin Rubinstein, Inna Gubenko, and Grigory Kurbatov. "Comparison of seven wind gust parameterizations over the European part of Russia." Advances in Science and Research 15 (November 19, 2018): 251–55. http://dx.doi.org/10.5194/asr-15-251-2018.
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Abstract. Wind gusts are extreme events which can cause severe damage. Gusts can reach significant values even during medium winds. However, numerical atmospheric models are designed to reproduce average wind speed, not gusts. There are several approaches to estimating wind gusts. Seven different methods are applied to WRF-ARW model output. Results are compared to high-frequency wind speed measurements using ultrasonic anemometers and temperature profiler measurement at the same point in Moscow. Data gathered from synoptic station network over the European part of Russia were also included in the analysis to increase the statistics. None of the wind gust estimation methods shows best results at every skill score. The proposed hybrid method shows good balance between the probability of detection and the false alarm ratio estimates.
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Zhang, Biao, and William Perrie. "Cross-Polarized Synthetic Aperture Radar: A New Potential Measurement Technique for Hurricanes." Bulletin of the American Meteorological Society 93, no. 4 (2012): 531–41. http://dx.doi.org/10.1175/bams-d-11-00001.1.
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We present an empirical C-band Cross-Polarization Ocean (C-2PO) model for wind retrievals from synthetic aperture radar (SAR) data collected by the RADARSAT-2 satellite. The C-2PO model relates normalized radar cross section (NRCS) in cross polarization to wind speed at 10-m height. This wind retrieval model has the characteristic that it is independent of wind direction and radar incidence angle but is quite linear with respect to wind speed. To evaluate the accuracy of the proposed model, winds with a resolution on the scale of 1 km were retrieved from a dual-polarization SAR image of Hurricane Earl on 2 September 2010, using the C-2PO model and compared with CMOD5.N, the newest available C-band geophysical model function (GMF), and validated with collocated airborne stepped-frequency microwave radiometer measurements and National Data Buoy Center data. Results suggest that for winds up to 38 m s−1, C-2PO has a bias of −0.89 m s−1 and a root-meansquare error of 3.23 m s−1 compared to CMOD5.N, which has a bias of −4.14 m s−1 and an rms difference of 6.24 m s−1. Similar results are obtained from Hurricane Ike, comparing wind retrievals from C-2PO and CMOD5.N with H*Wind data. The advantage of C-2PO over CMOD5.N and other GMFs is that it does not need any external wind direction and radar incidence angle inputs. Moreover, in the presently available quad-polarization dataset, C-2PO has the feature that the cross-polarized NRCS linearly increases even for wind speeds up to 26 m s−1 and reproduces the hurricane eye structure well, thereby providing a potential technique for hurricane observations from space.
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Corscadden, Kenneth W., Allan Thomson, Behrang Yoonesi, and Josiah McNutt. "The Impact of Variable Wind Shear Coefficients on Risk Reduction of Wind Energy Projects." International Scholarly Research Notices 2016 (October 30, 2016): 1–12. http://dx.doi.org/10.1155/2016/5790464.
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Estimation of wind speed at proposed hub heights is typically achieved using a wind shear exponent or wind shear coefficient (WSC), variation in wind speed as a function of height. The WSC is subject to temporal variation at low and high frequencies, ranging from diurnal and seasonal variations to disturbance caused by weather patterns; however, in many cases, it is assumed that the WSC remains constant. This assumption creates significant error in resource assessment, increasing uncertainty in projects and potentially significantly impacting the ability to control gird connected wind generators. This paper contributes to the body of knowledge relating to the evaluation and assessment of wind speed, with particular emphasis on the development of techniques to improve the accuracy of estimated wind speed above measurement height. It presents an evaluation of the use of a variable wind shear coefficient methodology based on a distribution of wind shear coefficients which have been implemented in real time. The results indicate that a VWSC provides a more accurate estimate of wind at hub height, ranging from 41% to 4% reduction in root mean squared error (RMSE) between predicted and actual wind speeds when using a variable wind shear coefficient at heights ranging from 33% to 100% above the highest actual wind measurement.
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Pichugina, Yelena L., Robert M. Banta, W. Alan Brewer, Scott P. Sandberg, and R. Michael Hardesty. "Doppler Lidar–Based Wind-Profile Measurement System for Offshore Wind-Energy and Other Marine Boundary Layer Applications." Journal of Applied Meteorology and Climatology 51, no. 2 (2012): 327–49. http://dx.doi.org/10.1175/jamc-d-11-040.1.
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AbstractAccurate measurement of wind speed profiles aloft in the marine boundary layer is a difficult challenge. The development of offshore wind energy requires accurate information on wind speeds above the surface at least at the levels occupied by turbine blades. Few measured data are available at these heights, and the temporal and spatial behavior of near-surface winds is often unrepresentative of that at the required heights. As a consequence, numerical model data, another potential source of information, are essentially unverified at these levels of the atmosphere. In this paper, a motion-compensated, high-resolution Doppler lidar–based wind measurement system that is capable of providing needed information on offshore winds at several heights is described. The system has been evaluated and verified in several ways. A sampling of data from the 2004 New England Air Quality Study shows the kind of analyses and information available. Examples include time–height cross sections, time series, profiles, and distributions of quantities such as winds and shear. These analyses show that there is strong spatial and temporal variability associated with the wind field in the marine boundary layer. Winds near the coast show diurnal variations, and frequent occurrences of low-level jets are evident, especially during nocturnal periods. Persistent patterns of spatial variability in the flow field that are due to coastal irregularities should be of particular concern for wind-energy planning, because they affect the representativeness of fixed-location measurements and imply that some areas would be favored for wind-energy production whereas others would not.
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Petersen, Guðrún Nína, and Trausti Jónsson. "The climate of Surtsey." Surtsey research 14 (June 2020): 9–16. http://dx.doi.org/10.33112/surtsey.14.1.
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The first meteorological measurement in Surtsey were conducted before the eruption ended in 1967 and since 2009 there have been continous automatic measurements on the island. Here we give the first comprehensive analysis of the climate of Surtsey, based on these observations, and compare it to the climate at the two other stations in the Vestmannaeyjar archipelago, Vestmannaeyjabær and Stórhöfði. Surtsey experiences a relatively mild but windy climate, with monthly mean temperature above freezing during all calendar months and wind speed exceeding 20 m/s on average 30 days a year. Precipitation measurements are challenging but show, as expected, the summer months to be the driest and October to be both on average the wettest month but also the most variable month. The measurements show the climate of Surtsey to be similar to the climate of the other two stations in the archipelago with the largest difference in wind speed, where Vestamannaeyjabær is sheltered while at Stórhöfði strong winds are enhanced by the orography.
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Thielicke, William, Waldemar Hübert, Ulrich Müller, Michael Eggert, and Paul Wilhelm. "Towards accurate and practical drone-based wind measurements with an ultrasonic anemometer." Atmospheric Measurement Techniques 14, no. 2 (2021): 1303–18. http://dx.doi.org/10.5194/amt-14-1303-2021.
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Abstract. Wind data collection in the atmospheric boundary layer benefits from short-term wind speed measurements using unmanned aerial vehicles. Fixed-wing and rotary-wing devices with diverse anemometer technology have been used in the past to provide such data, but the accuracy still has the potential to be increased. A lightweight drone for carrying an industry-standard precision sonic anemometer was developed. Accuracy tests have been performed with the isolated anemometer at high tilt angles in a calibration wind tunnel, with the drone flying in a large wind tunnel and with the full system flying at different heights next to a bistatic lidar reference. The propeller-induced flow deflects the air to some extent, but this effect is compensated effectively. The data fusion shows a substantial reduction of crosstalk (factor of 13) between ground speed and wind speed. When compared with the bistatic lidar in very turbulent conditions, with a 10 s averaging interval and with the unmanned aerial vehicle (UAV) constantly circling around the measurement volume of the lidar reference, wind speed measurements have a bias between −2.0 % and 4.2 % (root-mean-square error (RMSE) of 4.3 % to 15.5 %), vertical wind speed bias is between −0.05 and 0.07 m s−1 (RMSE of 0.15 to 0.4 m s−1), elevation bias is between −1 and 0.7∘ (RMSE of 1.2 to 6.3∘), and azimuth bias is between −2.6 and 7.2∘ (RMSE of 2.6 to 8.0∘). Key requirements for good accuracy under challenging and dynamic conditions are the use of a full-size sonic anemometer, a large distance between anemometer and propellers, and a suitable algorithm for reducing the effect of propeller-induced flow. The system was finally flown in the wake of a wind turbine, successfully measuring the spatial velocity deficit and downwash distribution during forward flight, yielding results that are in very close agreement to lidar measurements and the theoretical distribution. We believe that the results presented in this paper can provide important information for designing flying systems for precise air speed measurements either for short duration at multiple locations (battery powered) or for long duration at a single location (power supplied via cable). UAVs that are able to accurately measure three-dimensional wind might be used as a cost-effective and flexible addition to measurement masts and lidar scans.
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Bai, Wen Lei, Byun Gik Chang, Gerald Chen, Ken Starcher, David Carr, and Roy Issa. "Small Wind Turbine Power Performance Testing with Uncertainty Analysis." Advanced Materials Research 875-877 (February 2014): 1944–48. http://dx.doi.org/10.4028/www.scientific.net/amr.875-877.1944.
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Wind turbine power performance testing consists of power, temperature, air pressure and wind speed measurements collected for this study during which measuring uncertainties are involved. Due to the measurement uncertainties, the results of power performance testing are affected; therefore, it is necessary to consider the measurement uncertainties for evaluating the accuracy of turbine testing. For this purpose of this study, uncertainty analysis for one 5kW wind turbine power performance testing was conducted. The results of uncertainty analysis indicated that the uncertainty negatively affected the validity of conclusions drawn from power performance testing, and the uncertainty sources are various in different wind speed bins.
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Wildmann, Norman, Nikola Vasiljevic, and Thomas Gerz. "Wind turbine wake measurements with automatically adjusting scanning trajectories in a multi-Doppler lidar setup." Atmospheric Measurement Techniques 11, no. 6 (2018): 3801–14. http://dx.doi.org/10.5194/amt-11-3801-2018.
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Abstract. In the context of the Perdigão 2017 experiment, the German Aerospace Center (DLR) deployed three long-range scanning Doppler lidars with the dedicated purpose of investigating the wake of a single wind turbine at the experimental site. A novel method was tested for the first time to investigate wake properties with ground-based lidars over a wide range of wind directions. For this method, the three lidars, which were space- and time-synchronized using the WindScanner software, were programmed to measure with crossing beams at individual points up to 10 rotor diameters downstream of the wind turbine. Every half hour, the measurement points were adapted to the current wind direction to obtain a high availability of wake measurements in changing wind conditions. The linearly independent radial velocities where the lidar beams intersect allow the calculation of the wind vector at those points. Two approaches to estimating the prevailing wind direction were tested throughout the campaign. In the first approach, velocity azimuth display (VAD) scans of one of the lidars were used to calculate a 5 min average of wind speed and wind direction every half hour, whereas later in the experiment 5 min averages of sonic anemometer measurements of a meteorological mast close to the wind turbine became available in real time and were used for the scanning adjustment. Results of wind speed deficit measurements are presented for two measurement days with varying northwesterly winds, and it is evaluated how well the lidar beam intersection points match the actual wake location. The new method allowed wake measurements to be obtained over the whole measurement period, whereas a static scanning setup would only have captured short periods of wake occurrences.
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Zhou, Xuan, Xiao-min Ye, Yang Yu, Lian-jun Shao, Shu-ming Liu, and Zi-wei Li. "Sea Surface Wind Speed Measurement Using GNSS Reflection Signal." Journal of Electronics & Information Technology 35, no. 7 (2014): 1575–80. http://dx.doi.org/10.3724/sp.j.1146.2012.01396.
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SUTTON, S., and M. BENNETT. "Measurement of wind speed using a rapid-scanning lidar." International Journal of Remote Sensing 15, no. 2 (1994): 375–80. http://dx.doi.org/10.1080/01431169408954081.
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Goit, Jay Prakash, Atsushi Yamaguchi, and Takeshi Ishihara. "Measurement and Prediction of Wind Fields at an Offshore Site by Scanning Doppler LiDAR and WRF." Atmosphere 11, no. 5 (2020): 442. http://dx.doi.org/10.3390/atmos11050442.
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LiDAR-based wind speed measurements have seen a significant increase in interest in wind energy. However, reconstruction of wind speed vector from a LiDAR-measured radial wind speed is still a challenge. Furthermore, for extensive application of LiDAR technology, it can be used as a means to validate simulation and analytical models. To that end, this study employed scanning Doppler LiDAR for assessment of wind fields at an offshore site and compared Weather Research and Forecasting (WRF)-based mesoscale simulations and several wake models with the measurements. Firstly, the effect of carrier-to-noise-ratio (CNR) and data availability on the quality of scanning LiDAR measurements was evaluated. Analysis of vertical profiles show that the average wind speed is higher for wind blowing from the sea than that blowing from the land. Furthermore, profiles obtained from the WRF simulation also show a similar tendency in the LiDAR measurements in general, though it overestimates the wind speeds at higher altitudes. A method for reconstruction of wind fields from plan-position indicator (PPI) and range height indicator (RHI) scans of LiDAR-measured line of sight velocities was then proposed and first used to investigate the effect of coastal terrain. An internal boundary layer with strong shear could be observed to develop from the coastline. Finally, the flow field around wind turbine was measured using PPI scan and used to validate wake models.
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Kim, Hyun-Goo, and Jin-Young Kim. "Analysis of Wind Turbine Aging through Operation Data Calibrated by LiDAR Measurement." Energies 14, no. 8 (2021): 2319. http://dx.doi.org/10.3390/en14082319.
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This study analyzed the performance decline of wind turbine with age using the SCADA (Supervisory Control And Data Acquisition) data and the short-term in situ LiDAR (Light Detection and Ranging) measurements taken at the Shinan wind farm located on the coast of Bigeumdo Island in the southwestern sea of South Korea. Existing methods have generally attempted to estimate performance aging through long-term trend analysis of a normalized capacity factor in which wind speed variability is calibrated. However, this study proposes a new method using SCADA data for wind farms whose total operation period is short (less than a decade). That is, the trend of power output deficit between predicted and actual power generation was analyzed in order to estimate performance aging, wherein a theoretically predicted level of power generation was calculated by substituting a free stream wind speed projecting to a wind turbine into its power curve. To calibrate a distorted wind speed measurement in a nacelle anemometer caused by the wake effect resulting from the rotation of wind-turbine blades and the shape of the nacelle, the free stream wind speed was measured using LiDAR remote sensing as the reference data; and the nacelle transfer function, which converts nacelle wind speed into free stream wind speed, was derived. A four-year analysis of the Shinan wind farm showed that the rate of performance aging of the wind turbines was estimated to be −0.52%p/year.
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Redeker, K. R., A. J. Baird, and Y. A. Teh. "Quantifying wind and pressure effects on trace gas fluxes across the soil–atmosphere interface." Biogeosciences Discussions 12, no. 6 (2015): 4801–32. http://dx.doi.org/10.5194/bgd-12-4801-2015.
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Abstract. Large uncertainties persist in estimates of soil–atmosphere exchange of important trace gases. One significant source of uncertainty is the combined effect of wind and pressure on these fluxes. Wind and pressure effects are mediated by surface topography: few surfaces are uniform and over scales of tenths of a meter to tens of meters, air pressure and wind speed at the ground surface may be very variable. In this paper we consider how such spatial variability in air pressure and wind speed affects fluxes of trace gases. We used a novel nested wind tunnel design, comprising a toroidial wind tunnel in which wind speed and pressure may be controlled, set within a larger, linear wind tunnel. The effects of both wind speed and pressure differentials on fluxes of CO2 and CH4 within three different ecosystems (forest, grassland, peat bog) were quantified. We find that trace gas fluxes are positively correlated with both wind speed and pressure differential near the surface boundary. We argue that wind speed is the better proxy for trace gas fluxes because of its stronger correlation and because wind speed measurement is more easily accomplished and wind speed measurement methodology can be more easily standardized. Trace gas fluxes, whether into or out of the soil, increase with wind speed within the toroidal tunnel (+54% flux per m s−1), while faster, localized surface winds that are external to the toroidal wind tunnel reduce trace gas fluxes (−11% flux per m s−1). These results are consistent for both trace gases over all ecosystem soil types studied. Our findings support the need for a revised conceptualization of soil–atmosphere gas exchange. We propose a conceptual model of the soil profile that has a "mixed layer", with fluxes controlled by wind speed, wind duration, porosity, water table, and gas production and consumption.
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Ingenhorst, Christian, Georg Jacobs, Laura Stößel, Ralf Schelenz, and Björn Juretzki. "Method for airborne measurement of the spatial wind speed distribution above complex terrain." Wind Energy Science 6, no. 2 (2021): 427–40. http://dx.doi.org/10.5194/wes-6-427-2021.
Full textAbstract:
Abstract. Wind farm sites in complex terrain are subject to local wind phenomena, which have a relevant impact on a wind turbine's annual energy production. To reduce investment risk, an extensive site evaluation is therefore mandatory. Stationary long-term measurements are supplemented by computational fluid dynamics (CFD) simulations, which are a commonly used tool to analyse and understand the three-dimensional wind flow above complex terrain. Though under intensive research, such simulations still show a high sensitivity to various input parameters like terrain, atmosphere and numerical setup. In this paper, a different approach aims to measure instead of simulate wind speed deviations above complex terrain by using a flexible, airborne measurement system. An unmanned aerial vehicle is equipped with a standard ultrasonic anemometer. The uncertainty in the system is evaluated against stationary anemometer data at different heights and shows very good agreement, especially in mean wind speed (< 0.12 m s−1) and mean direction (< 2.4∘) estimation. A test measurement was conducted above a forested and hilly site to analyse the spatial and temporal variability in the wind situation. A position-dependent difference in wind speed increase of up to 30 % compared to a stationary anemometer is detected.