No Arabic abstract
A two-year measurement campaign of the ZephIR 300 vertical profiling continuous-wave (CW) focusing wind lidar has been carried out by the Royal Netherlands Meteorological Institute (KNMI) at the Cabauw site. We focus on the (height-dependent) data availability of the wind lidar under various meteorological conditions and the data quality through a comparison with in situ wind measurements at several levels in the 213-m tall meteorological mast. We find an overall availability of quality controlled wind lidar data of 97 % to 98 %, where the missing part is mainly due to precipitation events exceeding 1 mm/h or fog or low clouds below 100 m. The mean bias in the horizontal wind speed is within 0.1 m/s with a high correlation between the mast and wind lidar measurements, although under some specific conditions (very high wind speed, fog or low clouds) larger deviations are observed. The mean bias in the wind direction is within 2 degrees, which is on the same order as the combined uncertainty in the alignment of the wind lidars and the mast wind vanes. The well-known 180 degree error in the wind direction output for this type of instrument occurs about 9 % of the time. A correction scheme based on data of an auxiliary wind vane at a height of 10 m is applied, leading to a reduction of the 180 degree error below 2 %. This scheme can be applied in real-time applications in case a nearby, freely exposed, mast with wind direction measurements at a single height is available.
Ocean hazardous spills and search and rescue incidents are more prevalent as maritime activities increase across all sectors of society. However, emergency response time remains a factor due to a lack of information to accurately forecast the location of small objects. Existing drifting characterization techniques are limited to objects whose drifting properties are not affected by on-board wind and surface current sensors. To address this challenge, we study the application of multirotor unmanned aerial systems (UAS), and embedded navigation technology, for on-demand wind velocity and surface flow measurements to characterize drifting properties of small objects. An off-the-shelf quadrotor was used to measure wind velocity at 10 m above surface level near a drifting object. We also leveraged UAS-grade attitude and heading reference systems and GPS antennas to build water-proof tracking modules that record the position and orientation, as well of translational and rotational velocities, of objects drifting in water. The quadrotor and water-proof tracking modules were deployed during field experiments conducted in lake and ocean environments to characterize the leeway parameters of manikins simulating a person in water. Leeway parameters were found to be an order of magnitude within previous estimates derived using conventional wind and surface current observations. We also determined that multirotor UAS and water-proof tracking modules can provide accurate and high-resolution ambient information that is critical to understand how changes in orientation affect the downwind displacement and jibing characteristics of small objects floating in water. These findings support further development and application of multirotor UAS technology for leeway characterization and understanding the effect of an objects downwind-relative orientation on its drifting characteristics.
This work aims to characterize precisely and systematically the non-thermal characteristics of the electron Velocity Distribution Function (eVDF) in the solar wind at 1 au using data from the Wind spacecraft. We present a comprehensive statistical analysis of solar wind electrons at 1 au using the electron analyzers of the 3D-Plasma instrument on board Wind. This work uses a sophisticated algorithm developed to analyze and characterize separately the three populations - core, halo and strahl - of the eVDF up to 2 keV. The eVDF data are calibrated using independent electron parameters obtained from the quasi-thermal noise around the electron plasma frequency measured by the Thermal Noise Receiver. The code determines the respective set of total electron, core, halo and strahl parameters through non-linear least-square fits to the measured eVDF, taking properly into account spacecraft charging and other instrumental effects. We use four years, ~ 280000 independent measurements of core, halo and strahl parameters to investigate the statistical properties of these different populations in the solar wind. We discuss the distributions of their respective densities, drift velocities, temperature, and temperature anisotropies as functions of solar wind speed. We also show distributions with solar wind speed of the total density, temperature, temperature anisotropy and heat flux, as well as those of the proton temperature, proton-to-electron temperature ratio, proton and electron beta. Intercorrelations between some of these parameters are also discussed. The present dataset represents the largest, high-precision, collection of electron measurements in the pristine solar wind at 1~AU. It provides a new wealth of information on electron microphysics. Its large volume will enable future statistical studies of parameter combinations and their dependencies under different plasma conditions.
Velocity measurements of wind blowing near the North Sea border of Northern Germany and velocity measurements under local isotropic conditions of a turbulent wake behind a cylinder are compared. It is shown that wind gusts - measured by means of velocity increments - do show similar statistics to the laboratory data, if they are conditioned on an averaged wind speed value. Clear differences between the laboratory data and the atmospheric wind velocity measurement are found for the waiting time statistics between successive gusts above a certain threshold of interest.
We compare the available wind resources for conventional wind turbines and for airborne wind energy systems. Accessing higher altitudes and dynamically adjusting the harvesting operation to the wind resource substantially increases the potential energy yield. The study is based on the ERA5 reanalysis data which covers a period of 7 years with hourly estimates at a surface resolution of 31 x 31 km and a vertical resolution of 137 barometric altitude levels. We present detailed wind statistics for a location in the English Channel and then expand the analysis to a surface grid of Western and Central Europe with a resolution of 110 x 110 km. Over the land mass and coastal areas of Europe we find that compared to a fixed harvesting altitude at the approximate hub height of wind turbines, the energy yield which is available for 95% of the time increases by a factor of two.
Wind turbine wakes are the result of the extraction of kinetic energy from the incoming atmospheric wind exerted from a wind turbine rotor. Therefore, the reduced mean velocity and enhanced turbulence intensity within the wake are affected by the characteristics of the incoming wind, turbine blade aerodynamics, and the turbine control settings. In this work, LiDAR measurements of isolated wakes generated by wind turbines installed at an onshore wind farm are leveraged to characterize the variability of the wake mean velocity and turbulence intensity during typical operations encompassing a breadth of atmospheric stability regimes, levels of power capture, and, in turn, rotor thrust coefficients. For the statistical analysis of the wake velocity fields, the LiDAR measurements are clustered through a k-means algorithm, which enables to identify of the most representative realizations of the wind turbine wakes while avoiding the imposition of thresholds for the various wind and turbine parameters, which can be biased by preconceived, and potentially incorrect, notions. Considering the large number of LiDAR samples collected to probe the wake velocity field over the horizontal plane at hub height, the dimensionality of the experimental dataset is reduced by projecting the LiDAR data on an intelligently-truncated basis obtained with the proper orthogonal decomposition (POD). The coefficients of only five physics-informed POD modes, which are considered sufficient to reproduce the observed wake variability, are then injected in the k-means algorithm for clustering the LiDAR dataset. The analysis of the clustered LiDAR data, and the associated SCADA and meteorological data, enables the study of the variability of the wake velocity deficit, wake extent, and wake-added turbulence intensity for different thrust coefficients of the turbine rotor and regimes of atmospheric stability.