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Unmanned aerial vehicles are increasingly used to study atmospheric structure and dynamics. While much emphasis has been on the development of fixed-wing unmanned aircraft for atmospheric investigations, the use of multirotor aircraft is relatively unexplored, especially for capturing atmospheric winds. The purpose of this article is to demonstrate the efficacy of estimating wind speed and direction with 1) a direct approach using a sonic anemometer mounted on top of a hexacopter and 2) an indirect approach using attitude data from a quadcopter. The data are collected by the multirotor aircraft hovering 10 m above ground adjacent to one or more sonic anemometers. Wind speed and direction show good agreement with sonic anemometer measurements in the initial experiments. Typical errors in wind speed and direction are smaller than 0.5 and 30°, respectively. Multirotor aircraft provide a promising alternative to traditional platforms for vertical profiling in the atmospheric boundary layer, especially in conditions where a tethered balloon system is typically deployed.

We propose that flow distortion within a non-orthogonal CSAT3 sonic anemometer is primarily due to transducer shadowing, which is caused by wakes in the lee of the acoustic transducers impinging on their measurement paths. The dependence of transducer shadowing on sonic path geometry, wind direction and atmospheric stability is investigated with simulations that use surface-layer data from the Horizontal Array Turbulence Study (HATS) field program and canopy roughness-sublayer data from the CHATS (Canopy HATS) field program. We demonstrate the efficacy of correcting the CSAT3 for transducer shadowing with measurements of its flow distortion in the NCAR wind tunnel, combined with 6 months of data collected in the atmospheric surface layer with adjacent CSAT3 and orthogonal ATI-K sonic anemometers at the NCAR Marshall field site. CSAT3 and ATI-K measurements of the variance of vertical velocity σ w 2 and the vertical flux of sonic temperature agree within 1 % after correction of both sonics for transducer shadowing. Both the simulations of transducer shadowing and the comparison of CSAT3 and ATI-K field data suggest a simple, approximate correction of CSAT3 surface-layer scalar fluxes with an increase on the order of 4–5 %, independent of wind direction and atmospheric stability. We also find that σ w / u ∗ (where u ∗ is the friction velocity) and r u w (the correlation coefficient) calculated with corrected CSAT3 data are insensitive to wind direction and agree closely with known values of these dimensionless variables for neutral stratification, which is evidence for the efficacy of the correction of the horizontal wind components for transducer shadowing as well.

Non-orthogonal sonic anemometers are used extensively in flux networks and biomicrometeorological research. Previous studies have hypothesized potential underestimation of the vertical velocity turbulent perturbations, necessitating correction to increase flux measurements by approximately 10 %, while some studies have refuted that any correction is needed. Those studies have used cross-comparisons between sonic anemometers and numerical simulations. Here we propose a method that yields a correction factor for vertical velocity that requires only a single sonic anemometer in situ but requires some assumptions and adequate fetch at a sufficient distance above roughness elements where surface similarity is valid. Correction factors could be important in adjusting flux network and other flux data, as well as assessing the energy budget closure that is used as one of the flux data quality measures. The correction factor is confirmed in one field experiment and comparison between a CSAT3 and RMY 81000VRE, but it does not work well for the more complex form factors shown in a field comparison of an IRGAson and a CSAT3a.

This study introduces the SAMURAI-S, a novel measurement system that incorporates a state-of-the-art sonic anemometer combined with a multi-rotor drone in a sling load configuration, designed to overcome the limitations of traditional mast-based observations in terms of spatial flexibility. This system enables the direct measurement of 3D wind vectors while hovering, providing a significant advantage in manoeuvrability and positional accuracy over fixed mast setups. The capabilities of the system were quantified through a series of 10 to 28 min flights, conducting close comparisons of turbulence measurements at altitudes of 30 and 60 m against data from a 60 m tower equipped with research-grade sonic anemometers. The results demonstrate that SAMURAI-S matches the data quality of conventional setups for horizontal wind measurements while slightly overestimating vertical turbulence components. This overestimation increases with wind speed.

Wind shear (WS) phenomena are critical in many applications, especially in aviation, wind energy and urban planning. Microburst (MB) detection is important for ensuring safety during aircraft landing/takeoff, eliminating imbalances caused by shear from wind turbines, and for static calculations in urban planning. In this study, microburst events were detected using meteorological data. A new algorithm was applied to Light Detection and Ranging (LIDAR) data and 3 different cup anemometer data were available for 1-min and 10-min measurement periods. First, MB condition parameters using power law and basic wind shear analysis based on the scope of international criteria were defined, then checked in the algorithm. All results are compared with each other on behalf of detected microburst count, day, minute, and period. Detected events were matched at 66% and 85%, respectively, 10-min, and 1-min intervals. Validation studies were carried out for the same location by analysing the reflection values, reflection image and velocity product of the Doppler Weather Radar (DWR) with classical methods. However, when the radar results compared with 1- and 10-minute data sets, it was shown that 80% and 75% of daily events matched. The algorithm provided good continuity across LIDAR, different cup anemometers, and the weather radar. Consequently, the new algorithm will provide a great economic advantage.

Wind gusts are a key driver of aerodynamic loading, especially for tall structures such a bridges and wind turbines. However, gust characteristics in complex terrain are not well understood and common approximations used to describe wind gust behavior may not be appropriate at heights relevant to wind turbines and other structures. Data collected in the Perdigão experiment are analyzed herein to provide a foundation for improved wind gust characterization and process-level understanding of flow intermittency in complex terrain. High-resolution observations from sonic anemometers and vertically pointing Doppler lidars are used to conduct a detailed study of gust characteristics with a specific focus on the parent distributions of nine gust parameters (that describe velocity, time, and length scales), their joint distributions, height variation, and coherence in the vertical and horizontal planes. Best-fit distributional forms for varying gust properties show good agreement with those from previous experiments in moderately complex terrain but generate nonconservative estimates of the gust properties that are of key importance to structural loading. Probability distributions of gust magnitude derived from vertically pointing Doppler lidars exhibit good agreement with estimates from sonic anemometers despite differences arising from volumetric averaging and the terrain complexity. Wind speed coherence functions during gusty periods (which are important to structural wind loading) are similar to less complex sites for small vertical displacements (10 to 40 m), but do not exhibit an exponential form for larger horizontal displacements (800 to 1500 m).

To improve parameterizations of the turbulence dissipation rate (ϵ) in numerical weather prediction models, the temporal and spatial variability of ϵ must be assessed. In this study, we explore influences on the variability of ϵ at various scales in the Columbia River Gorge during the WFIP2 field experiment between 2015 and 2017. We calculate ϵ from five sonic anemometers all deployed in a ∼4 km2 area as well as from two scanning Doppler lidars and four profiling Doppler lidars, whose locations span a ∼300 km wide region. We retrieve ϵ from the sonic anemometers using the second-order structure function method, from the scanning lidars with the azimuth structure function approach, and from the profiling lidars with a novel technique using the variance of the line-of-sight velocity. The turbulence dissipation rate shows large spatial variability, even at the microscale, especially during nighttime stable conditions. Orographic features have a strong impact on the variability of ϵ, with the correlation between ϵ at different stations being highly influenced by terrain. ϵ shows larger values in sites located downwind of complex orographic structures or in wind farm wakes. A clear diurnal cycle in ϵ is found, with daytime convective conditions determining values over an order of magnitude higher than nighttime stable conditions. ϵ also shows a distinct seasonal cycle, with differences greater than an order of magnitude between average ϵ values in summer and winter.

The potential applications of three distinct photodetector types in laser Doppler anemometers for the measurement of hydrodynamic flow pulsations have been investigated. Laser Doppler anemometers with a classical vacuum photomultiplier tube, a multipixel silicon photomultiplier and a photomultiplier based on microchannel plates were used in the experiments. The main criteria for evaluating the quality of the Doppler signal were determined and analyzed for measuring the hydrodynamic flow velocity pulsations. The range of flow pulsations was from 1 to 50 Hz. In all experiments, laser Doppler anemometers demonstrated satisfactory performance when measuring pulsations up to 10 Hz. At frequencies from 10 to 50 Hz, the optimal results were achieved with laser Doppler anemometers employing photodetectors based on a silicon multipixel photomultiplier and a vacuum photomultiplier tube.

Extensive mean meteorological data and high frequency sonic anemometer data from two sites in Denmark, one coastal onshore and one offshore, have been used to study the full-scale spectrum of boundary-layer winds, over frequencies f from about [Formula: see text] to 10 Hz. 10-min cup anemometer data are used to estimate the spectrum from about [Formula: see text] to [Formula: see text]; in addition, using 20-Hz sonic anemometer data, an ensemble of 1-day spectra covering the range [Formula: see text] to 10 Hz has been calculated. The overlapping region in these two measured spectra is in good agreement. Classical topics regarding the various spectral ranges, including the spectral gap, are revisited. Following the seasonal peak at [Formula: see text], the frequency spectrum fS(f) increases with [Formula: see text] and gradually reaches a peak at about [Formula: see text]. From this peak to about [Formula: see text], the spectrum fS(f) decreases with frequency with a [Formula: see text] slope, followed by a [Formula: see text] slope, which can be described by [Formula: see text], ending in the frequency range for which the debate on the spectral gap is ongoing. It is shown here that the spectral gap exists and can be modelled. The linear composition of the horizontal wind variation from the mesoscale and microscale gives the observed spectrum in the gap range, leading to a suggestion that mesoscale and microscale processes are uncorrelated. Depending on the relative strength of the two processes, the gap may be deep or shallow, visible or invisible. Generally, the depth of the gap decreases with height. In the low frequency region of the gap, the mesoscale spectrum shows a two-dimensional isotropic nature; in the high frequency region, the classical three-dimensional boundary-layer turbulence is evident. We also provide the cospectrum of the horizontal and vertical components, and the power spectra of the three velocity components over a wide range from [Formula: see text] to 10 Hz, which is useful in determining the necessary sample duration when measuring turbulence statistics in the boundary layer.

This paper presents an open-source tool that can be used to simulate turbulence boxes constrained by measured data, which is useful for wind turbine model validation. The tool, called PyConTurb for \"Python Constrained Turbulence\", uses a novel algorithm based on the Kaimal Spectrum with Exponential Coherence method, and the algorithm can efficiently generate turbulence boxes under a wide variety of measurement constraints. The theoretical background for the technique is presented along with a few notes on its implementation in Python. The utility of PyConTurb is demonstrated using real data measured using three-dimensional sonic anemometers at the Denmark Technical University Risø campus. The presented results demonstrate that PyConTurb can successfully generate turbulence boxes from real measured data, including recreating the desired spatial coherence relationships between the simulated and measured time series. PyConTurb is shown to be a promising tool for investigating new spatial coherence models and for future one-to-one wind turbine validation studies.