the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Comparison of turbulence measurements by a CSAT3B sonic anemometer and a highresolution bistatic Doppler lidar
Michael Eggert
Christian Gutsmuths
Stefan Oertel
Paul Wilhelm
Ingo Voelksch
Luise Wanner
Jens Tambke
Ivan Bogoev
Accurate measurements of turbulence statistics in the atmosphere are important for eddycovariance measurements, wind energy research, and the validation of atmospheric numerical models. Sonic anemometers are widely used for these applications. However, these instruments are prone to probeinduced flow distortion effects, and the magnitude of the resulting errors has been debated due to the lack of an absolute reference instrument under field conditions. Here, we present the results of an intercomparison experiment between a CSAT3B sonic anemometer and a highresolution bistatic Doppler lidar, which is inherently free of any flow distortion. This novel remote sensing instrument has otherwise very similar spatial and temporal sampling characteristics to the sonic anemometer and hence served as a reference for this comparison. The presented measurements were carried out over flat homogeneous terrain at a measurement height of 30 m. We provide a comparative statistical analysis of the resulting mean wind velocities, the standard deviations of the vertical wind speed and the friction velocity and investigate the reasons for the observed deviations based on the turbulence spectra and cospectra. Our results show an agreement of the mean wind velocity measurements and the standard deviations of the vertical wind speed with a comparability of 0.082 and 0.020 m s^{−1}, respectively. Biases for these two quantities were 0.003 and 0.012 m s^{−1}, respectively. Slightly larger differences were observed for friction velocity. Analysis of the corresponding cospectra showed that the CSAT3B underestimates this quantity systematically by about 3 % on average as a result of cospectral losses in the frequency range between 0.1 and 5 s^{−1}. We also found that an angleofattackdependent transducershadowing correction does not improve the agreement between the CSAT3B and the PhysikalischTechnische Bundesanstalt (PTB) lidar effectively.
Accurate fastresponse measurements of the threedimensional wind vector are of great importance to fundamental research in micrometeorology for flux measurements using the eddycovariance methods in ecological studies (Aubinet et al., 2012). However, in recent years, several studies found that most, if not all, sonic anemometers may be afflicted by a systematic underestimation of turbulent fluctuations due to probeinduced flow distortion errors (Frank et al., 2013, 2016; Wyngaard, 1988). These errors can be further classified into errors due to transducer selfshadowing caused by crossshadowing and influences of the support structure. This has been demonstrated in field studies by means of specially modified reference instruments with a vertical measurement path, so that the measurement path is perfectly perpendicular to the horizontal flow, or by rotating an additional sonic anemometer 90^{∘} around the x axis for comparison. An intercomparison experiment between six different commercially available sonic anemometers showed that all participating instruments agreed very well (Mauder and Zeeman, 2018). Nevertheless, it is possible that all instruments measure vertical fluxes with similar inaccuracies, since no independent reference measurement was available. Consequently, the absolute magnitude of the potential bias remains unknown.
A particular sonic anemometer, the CSAT3 (Campbell Scientific Inc., Logan, Utah, USA) and its variant the CSAT3B, have been investigated intensively. It is one of the most widely used and highly reputed instruments, which has often served as a reference in past intercomparison studies (Foken and Oncley, 1995; Loescher et al., 2005; Mauder et al., 2007). Features such as its small transducer diameter, 30^{∘} tilt angle with respect to the vertical axis, short sonic path length, and symmetrical boom design, following the recommendations of Wyngaard (1988), increase confidence in its highfidelity vertical wind fluctuation measurements. Based on the results of a field comparison with an orthogonal sonic anemometer as reference, Horst et al. (2015, hereafter H15) propose a windtunnelderived correction for the CSAT3, which typically leads to an increase in vertical wind fluctuations and hence also vertical fluxes by 4 % to 5 %.
A numerical simulation of the flow around this instrument indicates that the H15 correction actually reduced the measurement error of common turbulence statistics, but a considerable uncertainty remained (Huq et al., 2017). This study found that the error is dependent on the azimuth angle, which can be explained by crossshadowing effects. A similar wind direction dependence of the CSAT3's flow distortion error was also found in a field experiment in comparison to another nonorthogonal sonic anemometer (Grare et al., 2016). Moreover, a spectral analysis based on theoretically derived ratios between the different wind components in the inertial subrange substantiates the earlier finding that the correction by H15 only partially compensates for the CSAT3's flow distortion error (Peña et al., 2019). Nevertheless, the main problem of all these past investigations has been the lack of an accurate standard reference for the measurement of turbulent flow statistics, since wind tunnel calibrations of sonic anemometers are conducted under quasilaminar conditions at much lower Reynolds numbers than in the free atmosphere and therefore their transferability to measurements in the field is questionable (Högström and Smedman, 2004). Further problems of past studies are the influence of shadowing between adjacent sensors and support structures, and lack of homogenous flat terrain. Our study seeks to overcome the limitations and uncertainties of previous experiments comparing sonic anemometers in the field.
As a reference instrument, we employ a highresolution bistatic Doppler lidar, which has been developed at the PhysikalischTechnische Bundesanstalt (PTB) in Braunschweig, Germany (Oertel et al., 2019). This optical remote sensing device is naturally free of any flow distortion errors and determines the 3D wind vector in a volume of less than 0.0005 m^{3}, for measurement heights up to 200 m at an output frequency of up to 10 s^{−1}, which is comparable to the sampling characteristics of a typical sonic anemometer. The very small sampling volume of this lidar system has the advantage that both data sets can be directly compared, without the need for extensive modelling of spatial averaging effects, which would lead to a large uncertainty of the resulting turbulence statistics (Brugger et al., 2016). Hence, our objectives for this study are as follows:

comparing the measurement of turbulence statistics of a CSAT3B sonic anemometer with the PTB lidar during a sidebyside field deployment,

investigating reasons for the observed deviations by means of (co)spectral analysis,

evaluating the correction proposed by H15 using the PTB lidar as a reference.
In this analysis, we will mainly focus on three statistics: (i) the mean wind velocity, as this quantity is of high relevance for a number of applications, especially in wind energy research; (ii) the standard deviation of the vertical velocity component, as errors in this variable directly translate into errors of fluxes between ecosystems and the atmosphere when using the eddycovariance method; and (iii) friction velocity, as this quantity is crucial for the validation of meteorological models (Tambke et al., 2005). To better understand the reasons for the differences between both instruments, we will analyse spectra and cospectra of the observed turbulent time series, including an analysis of spectral ratios of wind components in the inertial subrange as proposed by Peña et al. (2019).
2.1 Instruments
2.1.1 CSAT3B sonic anemometer
The CSAT3B sonic anemometer used in this study is the successor of the wellestablished CSAT3. The biggest difference to the CSAT3 is an improved placement of the control electronics inside the mounting block of the sensor head, whereas the sensor geometry, the measurement principle, etc., remained the same, so that findings of previous studies conducted with the CSAT3 are transferable to this study. The sensor geometry of the CSAT3B follows Zhang et al. (1986) and is optimized for low flow distortion due to transducer wakes designed for predominantly horizontal flow. In comparison to previous sonic anemometers with orthogonal sonic paths, where the horizontal velocity components are measured from a pair of axes located in the horizontal plane and the vertical velocity is measured by a single vertical pair of transducers, the flow distortion effects in the CSAT3B are reduced by positioning all six transducers and their supporting structures out of the horizontal plane. This is important because horizontal wind velocities are usually much larger than vertical wind velocities, and when using sonic anemometers with nonorthogonal paths a distorted measurement of the horizontal wind speed directly affects the vertical wind speed measurement. Each sonic path is tilted 30^{∘} from the vertical axis and spaced 120^{∘} apart in the horizontal plane. The length of the sonic path is 0.1154 m and the diameter of the ultrasonic transducers is 0.00635 m, giving a path length to diameter ratio of 18, which is larger than those of other commercially available instruments (Mauder and Zeeman, 2018). The higher this ratio and the steeper the angle between the sonic path and the vertical axis, the fewer selfshadowing effects are expected on the wind measurement because a smaller portion of the path is affected by the transducer wake (Kaimal, 1979; Wyngaard and Zhang, 1985).
As part of the calibration procedure, the sonic path length (the distance between the transducers) and the actual values of the angles of the sonic axes of each individual CSAT3B instrument are precisely determined with a coordinate measuring machine and stored in the internal nonvolatile memory. The wind speed along each sonic path is calculated from the sonic path distance between each pair of transducers and the difference of the reciprocal of the times of flight (TOF) of the ultrasonic pulses travelling along the sonic axes in opposite directions. Accurate and precise TOF measurements are achieved using advanced digital processing techniques. The wind components along the three nonorthogonal sonic axes are transformed into orthogonal components using a 3×3 coordinate transformation matrix unique for each CSAT3B and derived from the actual angles determined during the geometry measurement procedure. To determine accurate TOF estimates and to account for ultrasonic transducer delays associated with the conversion of the electricaltoacoustical signal, each CSAT3B is factory calibrated in a specially designed temperaturecontrolled zerowind chamber over the entire operating temperature range of −30 to +50 ^{∘}C. Any temperatureinduced changes in the sonic path length are also compensated for during this procedure.
The speed of sound can also be measured by the CSAT3B using the measured transducertotransducer distance and sum of the reciprocal of the TOF of the pulses along the acoustic path travelling in opposite direction. The quality and accuracy of the CSAT3B acoustic temperature measurements are evaluated during calibration by comparison with an air temperature standard. This procedure provides additional independent verification of the fidelity of the TOF measurements and the accuracy of the sonic path distance.
2.1.2 Bistatic Doppler lidar
The most widely used wind remote sensing devices are conventional monostatic Doppler lidar systems that have been established in wind energy applications in recent years (e.g. Pearson et al., 2009). Such systems utilize a common transmitting and receiving beam that measures the wind velocity component in beam direction via a Doppler shift of the received scattering light from aerosols travelling along the path of the transmitting laser beam (Drain, 1980). To measure the complete wind vector, the common beam is tilted in different directions (Eder et al., 2015; Newman et al., 2016). Provided that the wind field is almost homogeneous within the measurement volume, these systems deliver reliable measurement results (Gottschall et al., 2012; Peña et al., 2009). However, leaving flat terrain and having to consider the inhomogeneous wind conditions that predominate over complex terrain, significant errors for the wind speed measured arise (Bradley, 2008) and can be on the order of 10 % (Bingöl et al., 2009). Thus, in the case of unidentified and complex wind fields, the reliability of monostatic lidar measurements becomes questionable without considering any other reference measurements.
The novel threecomponent lidar system developed by the PTB aims to overcome the present limitation to almost homogeneous wind fields given by the monostatic working principle (Oertel et al., 2019). The basic idea of this system relies on utilizing a bistatic measurement setup (Harris et al., 2001), i.e. on the use of one transmitting laser beam and three detection beams (spatial separation), in order to determine all three components of the wind vector simultaneously in a small measurement volume by means of the same aerosols (Fig. 1). In contrast to monostatic systems, which typically use a common transmitting and receiving unit and an optical circulator to separate the received scattering light, the bistatic system is based on one transmitter and three discrete, spatially separated receivers.
The receivers are positioned at a radius of 1 m around the transmitter to ensure both sufficient particlescattering light intensity (quasibackward direction) and sufficient resolution for the determination of the horizontal velocity component. Each of the three heterodyne receivers converts the particlescattering light of its respective receiving beam into an optical beat signal, which is then converted into an electrical signal by a differential photodetector. The measurement volume calculated according to Gaussian beam optics has a diameter of 2 mm and a length of 50 mm for a measurement height of 30 m above ground. A timeofflight measurement of the overall optical path length is used to actively control the receiver optics in order to maintain the measurement volume at the desired wellknown height. To ensure a mobile operation with stable working conditions in the field, especially with respect to requirements on the mechanical setup and the optoelectronics, the bistatic lidar system has been enclosed in a temperaturecontrolled housing unit mounted on a trailer (Fig. 2). The accuracy of the bistatic PTB lidar was validated with the laser Doppler anemometer (LDA) reference standard in a wind tunnel erected on a platform at a height of 8 m. Longterm measurements, each lasting 1 h, were carried out. At seven velocities between 4 and 16 m s^{−1} and different orientations of the lidar system, an average deviation of less than 0.4 % was observed.
2.2 Experimental setup
The field intercomparison experiment was set up at the boundary of a recently harvested maize field on the compound of the Johann Heinrich von ThünenInstitut in Braunschweig, Germany (52.2943^{∘} N, 10.4461^{∘} E, 81 $\mathrm{m}\phantom{\rule{0.125em}{0ex}}\mathrm{a}.\mathrm{s}.\mathrm{l}.$), and measurements were carried out from 09:00 UTC, 14 September 2018, until 06:00 UTC, 27 September 2018. The CSAT3B was installed on top of a trailermounted pneumatic telescopic mast (Clark Masts Systems Ltd., Binstead, UK) at a height of 30.5 m (Fig. 3). Its measuring volume was 0.85 m from the centre of the mast; the mast's diameter at mounting height was 0.05 m. Since the prevailing wind direction expected for the measurement period was west, the PTB lidar was set up approximately 9 m west of the trailer mast and the CSAT3B was oriented at 270^{∘}. This setup was chosen, on the one hand, to minimize interference from the trailer mast with the PTB lidar measurements and interference from the anemometer's arms and the mast with the CSAT3B measurements on the other. Data acquisition from the CSAT3B was accomplished using a CR6 data logger (Campbell Scientific, Inc., Logan, Utah, USA) with SDM (Synchronous Device for Measurements) communications. The sampling rate was 10 s^{−1}, and the three orthogonal (referenced to the anemometer head) wind components u_{x}, u_{y}, and u_{z} (m s^{−1}); the ultrasonic air temperature T_{s} (^{∘}C); and the CSAT3B diagnostic flag were recorded. Measurement times were logged in UTC, and data acquisition systems were synchronized with a time server via the internet. The PTB lidar system recorded the measured (Doppler) frequency and amplitude of every detected scattered light signal. This raw data were also averaged to 10 s^{−1} velocity vectors afterwards.
2.3 Meteorological conditions
In a continuation of the previous months, the air temperature stayed relatively high for the first week of the measurement campaign, due to a series of highpressure systems. The remains of an Atlantic hurricane (“ExHelene”) pushed hot air up to the northern border of Germany, which culminated in air temperatures of more than 30 ^{∘}C on 18 September 2018 at the site of our experiment. The clear sky led to a strong diurnal variation in temperature with differences of up to 15 ^{∘}C between the nocturnal minimum and the daytime maximum. The wind was relatively weak, with 10 min mean wind speeds (in 10 m height) ranging from 1 to 6 m s^{−1} and between 1 and 10 m s^{−1} for wind gusts. Wind speed was correlated with the variation in air temperature, with higher speeds at noon, due to more intense convection and better mixing, and lower speeds during the night. The wind direction was mostly between south and west. At noon on 21 September 2018, the air temperature dropped abruptly from more than 25 ^{∘}C to less than 15 ^{∘}C, accompanied by wind speeds of up to 11 m s^{−1}, wind gusts of up to 20 m s^{−1}, and some rain. During this second week, the nocturnal temperature minimum was 5 ^{∘}C and wind speeds were generally higher than during the first week (Fig. 4).
2.4 Calculation of turbulence statistics
All turbulence statistics were calculated from the 10 s^{−1} raw data of both instruments using the eddycovariance software TK3 (Mauder and Foken, 2015) with an averaging time of 30 min. The same settings were applied in TK3 for both data sets, including a spike detection algorithm (Mauder et al., 2013). In addition, we used the diagnostic flag of the CSAT3B for filtering of the raw data and screened our data for rain in the last hour, which may have affected the optics of the lidar and the transducers of the sonic anemometer. After this preparation of the raw data, we discarded any 30 min statistics if more than 10 % of the highfrequency data were missing, including those data rejected by the spike test. These are commonly used settings for eddycovariance measurements (Fratini and Mauder, 2014; Mauder et al., 2013). For the CSAT3B, no spikes at all were detected for 92 % of the 618 30 min intervals, and for the PTB lidar, 73 % of the 618 30 min intervals were spikefree. This means application of the spike detection algorithm is important to ensure high data quality, but its impact on the comparison is limited. As a result of the data preparation described above, 615 30 min intervals remained for the CSAT3B and 458 remained for the PTB lidar. Subsequently, the raw turbulence statistics were corrected using the double rotation method (Kaimal and Finnigan, 1994), and a correction of lowpass filtering effects due to path length averaging (Moore, 1986) to allow for a direct comparison of both data sets. In an alternative processing stream, we applied the correction for transducershadowing effects by H15 in order to validate this method as part of this intercomparison experiment. To facilitate this, we implemented this method into the TK3 software based on a software script provided by Campbell Scientific Inc.
2.5 Statistical analysis of the comparison
For the statistical analysis of the intercomparison, an orthogonal Deming regression was applied in order to account for measurement errors in both x and y variables, using the R package mcr (Manuilova et al., 2014). In this regression analysis, we generally selected the PTB lidar data as the x variable and the sonic anemometer data as the y variable. In contrast to a traditional leastsquares method, the orthogonal regression provides deviations measured perpendicularly and not parallel to the y axis, which addresses problems when there is a measurement error in both x and y variables and implies that errors in x and y have equal variances. Pearson's correlation coefficient r is also determined by using the same R package. Furthermore, we calculated comparability, which is equivalent to the rootmeansquare error (RMSE), and bias, which is the mean error of a certain measurement quantity.
2.6 Spectral analysis
Based on dimensional analysis of energy distribution of turbulence, it has been deduced that spectra and cospectra of fully developed turbulence follow similarity laws (Kolmogorov, 1941). Comparing the theoretically derived and measured spectra can be a powerful tool to investigate the performance of measuring instruments. Here, we focus on two spectral characteristics in the inertial subrange: (i) the ratio between the spectra of transversal wind velocity components, i.e. S_{v}and S_{w}, and of the longitudinal component S_{u} is theoretically derived to be 4∕3, and (ii) the power law behaviour with a slope of $\mathrm{5}/\mathrm{3}$ for spectra and $\mathrm{7}/\mathrm{3}$ for cospectra (Kaimal and Finnigan, 1994). A ratio smaller than 4∕3 between S_{w} and S_{u} indicates a general underestimation of vertical wind velocity or overestimation of the horizontal velocity (Peña et al., 2019). In case of highfrequency dampening, the slope of a measured spectrum drops below $\mathrm{5}/\mathrm{3}$ at the highfrequency end of the spectrum (Aubinet et al., 2000). This allows for the determination of the cutoff frequency f_{c}, describing the associated sonic path averaging lowpass filter effect, by spectral analysis as proposed by, e.g. Ibrom et al. (2007). The halfhourly wind spectra are calculated using the TK3 software (Mauder and Foken, 2015), following the method of Stull (1988). Further processing is based on the method of Ibrom et al. (2007) for cutoff frequency determination. To investigate the ratio between S_{u}, S_{v}, and S_{w} within the inertial subrange, the halfhourly spectra were weighted by frequency and exponentially binned. All u, v, and w spectra from 30 min intervals with absolute values of sensible heat flux larger than 10 W m^{−2} and absolute values of the stability parameter $z/L<\mathrm{2}$ (z= measurement height; L= Obukhov length) were averaged to derive one ensemble spectrum. For the empirical determination of the cutoff frequency of the w measurements, halfhourly spectra S_{w,norm} were additionally normalized by the variance of w and inspected for blue noise.
We assume that the lowpass filtering of S_{w,norm}(f) can be described by the following function (Fratini et al., 2012),
where F_{n} is an additional normalization factor, which is intended to compensate for the reduction of the overall variance (Ibrom et al., 2007). We fitted the S_{w,norm} to Eq. (1) to determine the cutoff frequency, using the Levenberg–Marquardt nonlinear leastsquares algorithm as implemented in the R package minpack.lm (Elzhov et al., 2016). This fit was weighted by the number of frequencies in each bin. Instead of sonic temperature spectra, as proposed by Ibrom et al. (2007), we used the spectral models for vertical wind velocity S_{w,mod}(f) that are implemented in TK3 as universal reference spectra. These models are a corrected version of Moore (1986) for stable stratification and of Højstrup (1981) for unstable conditions. The model spectra were calculated for each 30 min interval and then averaged to one ensemble spectrum in order to determine the cutoff frequency. Please note that this does not apply to the ensemble spectra presented to determine the spectral ratios. These are purely based on measured spectra.
3.1 Comparison of turbulence statistics
Scatter plots and regression parameters for $\stackrel{\mathrm{\u203e}}{u}$, ${\stackrel{\mathrm{\u203e}}{{w}^{\prime}{w}^{\prime}}}^{\mathrm{0.5}}$, and u_{∗} generally show a good agreement between the CSAT3B and the PTB lidar measurements (Fig. 5, Table 1). Particularly, the measurements of the vertical velocity fluctuations are almost identical with a regression slope of 0.994, a correlation coefficient of 0.998, and a comparability of 0.017 m s^{−1}. This is somewhat unexpected because previous studies indicated an underestimation of ${\stackrel{\mathrm{\u203e}}{{w}^{\prime}{w}^{\prime}}}^{\mathrm{0.5}}$ by 3 %–5 % due to probeinduced flow distortion (H15, Frank et al., 2016). However, only a very small negative bias of −0.009 m s^{−1} was found in our analysis using the flowdistortionfree PTB lidar as reference. One might argue that perhaps both instruments underestimated ${\stackrel{\mathrm{\u203e}}{{w}^{\prime}{w}^{\prime}}}^{\mathrm{0.5}}$ in the same way. However, we regard this as implausible because the measurement principles are very different and therefore it is unlikely that the effect of potential errors is so similar under this broad range of atmospheric conditions. Based on the remote optical measurement principle and the lack of any physical structure, it can safely be assumed that flow distortion errors can be ruled out for the lidar. Any potential highfrequency dampening effects of the lidar signal should be small considering its short measurement path of 0.05 m and the sampling frequency of 10 s^{−1}. Furthermore, we have even compensated for those small lowpass filtering effects as part of the standard postprocessing routine using the TK3 software (Moore, 1986).
Our findings partially contradict the conclusions of earlier sonic anemometer intercomparison studies that proposed vertical wind underestimation by the CSAT3 as the source of error on the order of 5 %. The discrepancy of our findings with the results from previous experiments can be explained by the lack of a suitable and accurate reference instrument. For example, H15 used an Applied Technologies, Inc. (ATI) Kprobe sonic anemometer as a reference instrument, which they assumed to be more accurate because of its orthogonal transducer array. However, the measurements by this instrument are also corrected for flow distortion effects by a variable factor of 1.02, on average, for w measurements, and this windtunnelbased correction factor might not be applicable in the turbulent free atmosphere.
The mean wind velocity $\stackrel{\mathrm{\u203e}}{u}$ also compares very well on average. There are just a few data points at higher wind speeds between 5 and 6 m s^{−1} for which the CSAT3B reports slightly larger values than the PTB lidar. Nevertheless, this comparison, with a very small bias of 0.003 m s^{−1} and a RMSE of 0.082 m s^{−1} (Table 1), is still as good as or even better than between two adjacent CSAT3 sonic anemometers (Mauder and Zeeman, 2018).
Friction velocity u_{∗} is typically more difficult to measure due to the spectral separation between the peaks in the u and w spectra. Nevertheless, the comparability of these values is still good between the two instruments, with an RMSE of 0.042 m s^{−1}, which is again as good as between adjacent sonic anemometers (Mauder and Zeeman, 2018). However, the u_{∗} data measured by the CSAT3B are slightly too low compared to the PTB lidar, indicated by a regression slope of 0.973 and a bias of −0.009 m s^{−1} (Table 1). The differences in $\stackrel{\mathrm{\u203e}}{u}$ at larger wind speeds and the systematic differences in u_{∗} will be investigated further below.
As a first step, we assess whether the comparison of the CSAT3B data improves through application of the H15 method, which is intended to correct for flow distortion by transducer shadowing. However, as can be seen from Table 1, $\stackrel{\mathrm{\u203e}}{u}$ and ${\stackrel{\mathrm{\u203e}}{{w}^{\prime}{w}^{\prime}}}^{\mathrm{0.5}}$ show slightly larger differences from the PTB lidar after applying the H15 correction. The “corrected” mean wind velocity $\stackrel{\mathrm{\u203e}}{u}$ has a larger bias, 0.077 instead of 0.003 m s^{−1}, and a larger RMSE, 0.110 instead of 0.082 m s^{−1}, although intercept and slope are similar to before applying the H15 correction. H15 reported that ${\stackrel{\mathrm{\u203e}}{{w}^{\prime}{w}^{\prime}}}^{\mathrm{0.5}}$ is increased by 4–5 % through this correction. Our results are on the lower end of this range, as the regression slope is increased from 0.994 to 1.030 (Table 1). However, the slope is now clearly larger than unity and the regression intercept for ${\stackrel{\mathrm{\u203e}}{{w}^{\prime}{w}^{\prime}}}^{\mathrm{0.5}}$ slightly more negative, so that the comparability is similar before and after the correction. The agreement of the u_{∗} values improves slightly after applying the H15 correction, since the regression slope increases from 0.0973 to 1.007 and the correlation coefficient is marginally closer to unity than before (Table 1).
In order to investigate the reason for the remaining discrepancies in $\stackrel{\mathrm{\u203e}}{u}$, we analysed the relationship between the differences of the $\stackrel{\mathrm{\u203e}}{u}$ measurements from both instruments and potential driving variables, such as u_{∗}, sonic temperature, wind direction, and the standard deviations of the velocity components. We found the strongest relationship between $\mathrm{\Delta}\stackrel{\mathrm{\u203e}}{u}/\stackrel{\mathrm{\u203e}}{u}$ and the wind direction (Fig. 6). This could be explained by the horizontally symmetrical design of the CSAT3 structure, as recommended by Wyngaard and Zhang (1985). A very similar wind direction dependence of the error in $\stackrel{\mathrm{\u203e}}{u}$ has also been reported by Grare et al. (2016), when comparing a CSAT3 sonic anemometer against a Gill R350 sonic anemometer. Moreover, Horst et al. (2016) observed similar behaviour when they measured the flow distortion within the IRGASONintegrated sonic anemometer and CO_{2}∕H_{2}O gas analyser. They found good agreement for w but not for $\stackrel{\mathrm{\u203e}}{u}$ and u_{∗}. It is interesting to note that this wind direction dependence does not improve after application of the H15 flow distortion correction (Fig. 6), which only leads to larger wind speeds in general. Hence, these results confirm the finding of Huq et al. (2017) based on numerical simulations that the H15 correction does not account for the pronounced azimuth dependence of the CSAT3 velocity measurements. Moreover, we can now quite reliably attribute the observed differences in $\stackrel{\mathrm{\u203e}}{u}$ to a systematic winddirectiondependent error of the CSAT3B.
3.2 Spectral and cospectral analysis
In the following section, we investigate the ensemble turbulence spectra of the three wind components, with a special focus on the ratios between them in the inertial subrange. This may help to shed more light on the reasons for the very good agreement between the CSAT3B and the PTB lidar measurements of ${\stackrel{\mathrm{\u203e}}{{w}^{\prime}{w}^{\prime}}}^{\mathrm{0.5}}$. As can be seen in Fig. 7, all three wind components measured by the PTB lidar are afflicted by some noise at very high frequencies. In addition, the w spectra show a dampening of the signal at high frequencies. The CSAT3B spectra follow the theoretical $\mathrm{5}/\mathrm{3}$ power law very well across the entire inertial subrange in all three wind components. There are no signs of noise, aliasing, or highfrequency dampening in the spectra (Fig. 7).
In addition to the $\mathrm{5}/\mathrm{3}$ power law, a spectral ratio of 4∕3 has been theoretically derived for S_{v}∕S_{u} and S_{w}∕S_{u} in the inertial subrange (Kaimal and Finnigan, 1994). We generally find smaller ratios for both instruments, while the ratios measured by the PTB lidar are generally larger than for the CSAT3B by a few percent (Table 2). We also find that the S_{v}∕S_{u} ratios are generally larger than those for S_{w}∕S_{u}. The lower S_{w}∕S_{u} ratios have been interpreted as an indicator for probeinduced flow distortion (Peña et al., 2019), which is in line with our findings since the flowdistortionfree lidar measurements show larger values. However, even these flowdistortionfree data do not reach the theoretical value of 4∕3 for S_{v}∕S_{u} and even less so for S_{w}∕S_{u}. Hence, we suspect that this theoretical value was probably not fulfilled in reality for the ensemble spectrum, presumably because the turbulence was not quite isotropic under all atmospheric conditions during the measurement period, which can happen due to different reasons (Brugger et al., 2018; Stiperski and Calaf, 2018). In comparison with the uncorrected CSAT3 measurements of Peña et al. (2019), our CSAT3B data show slightly smaller S_{v}∕S_{u} ratios of 1.26 versus 1.32 and 1.34, while the S_{w}∕S_{u} ratios are slightly larger, being 1.16 versus 1.13 and 1.07 for their two data sets. It is interesting to note that after the application of the H15 correction, which is supposed to correct for flow distortion effects, the spectral ratio indeed agrees better with the theoretical value of 4∕3 and with the PTB lidar values than without the correction (Table 2).
As mentioned above, all the turbulence statistics of the PTB lidar are corrected for pathaveraging effects according to Moore (1986) using a length of 0.05 m. Since the underlying analytical transfer function might not necessarily be correct for this instrument, we also determined the lowpass filtering transfer function empirically based on the ensemble spectrum of w. We found a cutoff frequency of 4 s^{−1}, which results in an increase of ${\stackrel{\mathrm{\u203e}}{{w}^{\prime}{w}^{\prime}}}^{\mathrm{0.5}}$ by ca. 0.25 %, when applied as part of the Moore correction, compared to the value for the path averaging correction for 0.05 m measurement length. This small uncertainty adds confidence to the suitability of the PTB lidar for serving as absolute reference for ${\stackrel{\mathrm{\u203e}}{{w}^{\prime}{w}^{\prime}}}^{\mathrm{0.5}}$ in this comparison. Significant blue or white noise in S_{w} measured by the PTB lidar was not detected, either. In addition, we also calculated the turbulent statistics of the PTB lidar and the CSAT3B without any lowpass filtering correction whatsoever, and the results for ${\stackrel{\mathrm{\u203e}}{{w}^{\prime}{w}^{\prime}}}^{\mathrm{0.5}}$ show some small differences in comparison to the Moorecorrected data (Table 1), e.g. the bias is slightly smaller by 0.001 m s^{−1}, while the RMSE is slightly larger by 0.003 m s^{−1}. This shows that the effect of the lowpass correction is generally small because of the relatively large measurement height of 30 m.
Obviously, the results of this intercomparison partially contradict the findings of H15, Frank et al. (2016), and Huq et al. (2017), who advocate the need of a flow distortion correction on ${\stackrel{\mathrm{\u203e}}{{w}^{\prime}{w}^{\prime}}}^{\mathrm{0.5}}$ on the order of several percent. However, these previous field intercomparisons only compared different sonic anemometers with each other, partially with different sensor geometries, but none of them can be considered flow distortion free to the same extent as the bistatic Doppler lidar. It remains unclear why the numerical simulations of Huq et al. (2017) detect an underestimation of ${\stackrel{\mathrm{\u203e}}{{w}^{\prime}{w}^{\prime}}}^{\mathrm{0.5}}$ by 3 %–7 % for the CSAT3, when we see deviations of approximately 1 % in this field experiment. Perhaps the numerical simulations were not turbulent enough and thus the wake effects are stronger than under fully developed turbulent conditions in the field. Generally, wake effects depend on the Reynolds number and the wake extent is reduced suddenly at the transition from laminar to turbulent flow (e.g. Williamson, 1996). This is also the reason why it is problematic to transfer quasilaminar wind tunnel calibrations to realworld turbulence (Högström and Smedman, 2004). Therefore, we believe that this explains the differences between our field study and previous windtunnelbased and numerical experiments (Grare et al., 2016; Huq et al., 2017), and we expect that the field experiment has more validity in principle, since sonic anemometers are normally used in the field.
Nevertheless, our results show that an azimuthdependent flow distortion correction is indeed needed for obtaining more accurate measurements of the mean wind velocity of the CSAT3B (Sect. 3.1, Fig. 6). Further field comparisons with the PTB lidar or more realistic LES studies would be needed to this end. Moreover, it is generally preferable to minimize flow distortion errors to begin with through clever design of the instrument, e.g. by increasing the ratio between path length and transducer diameter, than relying on the transferability of windtunnelbased correction models to realworld conditions.
We found that the H15 flow distortion correction improves the u_{∗} comparison with the PTB lidar considerably, but why is only u_{∗} improved and not $\stackrel{\mathrm{\u203e}}{u}$ and ${\stackrel{\mathrm{\u203e}}{{w}^{\prime}{w}^{\prime}}}^{\mathrm{0.5}}$? An analysis of the Co_{uw} cospectra shows that the CSAT3B deviates from the expected $\mathrm{7}/\mathrm{3}$ power law behaviour in the inertial subrange at frequencies $f>\mathrm{0.1}\phantom{\rule{0.125em}{0ex}}{\mathrm{s}}^{\mathrm{1}}$ (Fig. 8). It can also be seen that the H15 correction slightly increases the cospectral energy across the entire range of frequencies. However, the too steep dropoff of the CSAT3B ensemble cospectrum is not improved effectively. Hence, our analysis shows that the H15 correction results in improved the comparison of the u_{∗} values, but the ensemble cospectrum shows that this improvement occurred for the wrong reasons. As a consequence, the observed behaviour of this correction for u_{∗} may potentially be sitespecific and not universally transferable. Nevertheless, we would like to recall here that the underestimation of u_{∗} measured by the CSAT3B is only by a few percent, so that the accuracy of these uncorrected measurements is still sufficient for many applications.
We presented the results of a field intercomparison experiment, comparing the measurements of turbulence statistics in the atmospheric surface layer of a CSAT3B sonic anemometer and a novel bistatic Doppler lidar, which has been recently developed by PTB. Spectral analysis of the highfrequency data shows that the PTB lidar has some minor noise at high frequencies in all three wind components. In addition, w is slightly dampened at high frequencies, probably due to path length averaging, which can be corrected by a lowpass filtering correction normally applied for sonic anemometers (Moore, 1986). Nevertheless, this newly developed instrument is well suited for serving as independent reference in measuring turbulent statistics in the atmospheric surface layer due to its traceability to laser Doppler anemometer measurements in a wind tunnel and its completely unobstructed measurement volume.
Our comparison shows a very good agreement between both instruments for the measurement of $\stackrel{\mathrm{\u203e}}{u}$ and ${\stackrel{\mathrm{\u203e}}{{w}^{\prime}{w}^{\prime}}}^{\mathrm{0.5}}$. Nevertheless, our results for spectral ratios between w and u confirm that the CSAT3B is somewhat affected by flow distortion in the measurement of ${\stackrel{\mathrm{\u203e}}{{w}^{\prime}{w}^{\prime}}}^{\mathrm{0.5}}$. Moreover, u_{∗} from the CSAT3B is about 3 % too low compared to the PTB lidar, which is explained by the too steep dropoff of the Co_{uw} cospectrum. We also evaluated whether the overall accuracy of the CSAT3B measurements can be improved by the H15 flow distortion correction, and our results indicate that this method increases the spectral energy across the entire range of frequencies equally and does not appropriately correct the CSAT3B data in the inertial subrange. It leads to an overestimation of ${\stackrel{\mathrm{\u203e}}{{w}^{\prime}{w}^{\prime}}}^{\mathrm{0.5}}$, and it does not correct for the winddirectiondependent error of $\stackrel{\mathrm{\u203e}}{u}$. Based on these results, we conclude that the probeinduced flow distortion issue of sonic anemometers warrants further investigation in the future to effectively correct general measurements of scalar fluxes.
Since any systematic effects in the measurement of ${\stackrel{\mathrm{\u203e}}{{w}^{\prime}{w}^{\prime}}}^{\mathrm{0.5}}$ usually directly translate into errors in eddycovariance flux measurements, the findings of this study are also relevant with respect to the energy balance closure problem (Stoy et al., 2013) and the accuracy of any trace gas flux measurement (Foken et al., 2011; Wilson et al., 2002). In this context, we can state that the very good agreement in the ${\stackrel{\mathrm{\u203e}}{{w}^{\prime}{w}^{\prime}}}^{\mathrm{0.5}}$ measurements of both instruments indicates that a probeinduced flow distortion error of the CSAT3B sonic anemometer contributes only very little to the observed systematic underestimation of scalar fluxes using the eddycovariance method.
In summary, the agreement of all variables tested in this comparison experiment is at least as good as or better than that between two adjacent sonic anemometers (Mauder and Zeeman, 2018). This indicates that both instruments are very precise devices for measuring turbulence statistics, particularly for vertical scalar fluxes. Considering the findings of the intercomparison experiment of Mauder and Zeeman (2018), we conclude that the other sonic anemometers tested in that study are also suitable for general flux measurements within the range of comparability and bias described in that study. However, our spectral analysis shows that the bistatic Doppler lidar developed by PTB is slightly more accurate, particularly for measurements of friction velocity and the momentum flux.
For completeness, we show the comparison for the standard deviations of u and v between the CSAT3B and the PTB lidar measurements in Fig. A1. The overall agreement is very good.
Tilt angles of the double rotation method as a function of wind direction can provide an indication about the potential misalignment of the instruments, which has been corrected for by this coordinate rotation as part of the postprocessing. As expected, tilt angles of the PTB lidar are generally smaller than those of the CSAT3B (Fig. A2).
Sonic anemometer and Doppler lidar data are available upon request to Matthias Mauder (matthias.mauder@kit.edu).
MM and IV operated the sonic anemometer, and ME, CG, SO, and PW operated the Doppler lidar and preprocessed its 10 Hz raw data. MM calculated turbulence statistics, including the relevant corrections based on the raw sonic anemometer and Doppler lidar data, and calculated the statistical metrics for the intercomparison. LW and MM conducted the (cospectral) analysis. MM wrote Sects. 1, 2.4, 2.5, 3, and 4; IB wrote Sect. 2.1.1; ME wrote Sect. 2.1.2; IV wrote Sect. 2.2; JT wrote Sect. 2.3; and LW wrote Sect. 2.6. All coauthors provided comments and suggestions on a previous version of this paper.
The authors declare that they have no conflict of interest.
We acknowledge Mathias Herbst of the DWD Zentrum für Agrarmeteorologische Forschung in Braunschweig for providing logistical support during the measurement campaign, and we thank the ThünenInstitut in Braunschweig for providing the field site for this experiment. The equipment for study has been financially supported in part by the Helmholtz initiative “Modular Observations Solutions for Earth Systems (MOSES)”. We thank Jamie Smidt (KIT) for checking the English grammar and spelling.
The article processing charges for this openaccess publication were covered by a Research Centre of the Helmholtz Association.
This paper was edited by Szymon Malinowski and reviewed by John Frank and one anonymous referee.
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