## 1. Introduction

The quality of remote sensing instruments is generally judged by performing an intercomparison experiment (e.g., Antoniou et al. 2004). Winds are measured at several heights by cup or sonic anemometers on a mast, together with measurements by a remote sensing instrument. What do the reported correlation values mean in terms of predicting differences in measured wind speed? This question arises since these differences between cup anemometers and remote sensing instruments are the essence of whether remote sensing gives “bankable” data. What quality of wind measurements can be expected from remote sensing in a typical installation? This is relevant since intercomparisons are often under test conditions unlike those typically encountered at normal sites.

Some previous intercomparison experiments have touched on the spatial and temporal separation issues treated in detail below. Mastrantonio and Fiocco (1982) describe transmission on three acoustic beams simultaneously, with emphasis on Doppler processing, rather than the effects of spatial separation of their beams. Contini et al. (2006) consider ultrasonic anemometers, with some interesting observations about spatial separation of sensors, but the scales and other considerations are different from those of sodars. Contini et al. (2007) show improvements from one sodar over another if all acoustic beams are transmitted simultaneously, but the comparison is between two very different sodar systems. Other works (e.g., Contini et al. 2004) consider poor sodar setup: in the work below it is assumed that such factors are negligible.

## 2. Correlation between mast and remote instruments

*U*is toward the +

*x*direction and variations in wind vector components are (

*u*,

*υ*,

*w*). A scatterplot is obtained from

*N*measurements of mast instrument wind speed

*U*

_{m,n}and the corresponding remote instrument wind speed

*U*

_{r,n}, where

*n*= 1, 2, … ,

*N*. Neither of the measurement pairs,

*U*

_{m,n}and

*U*

_{r,n}, necessarily is equal to the actual wind

*U*which includes the turbulent fluctuations, because all instruments exhibit measurement errors. However, it is conventional to consider the mast measurements as error free, whereas

_{n}*ɛ*is Gaussian distributed with standard deviation

*σ*.

_{ɛ}*U*

_{m,n}is made at the mast. A measure of the scatter around the best-fit line is the coefficient of determination

*R*

^{2}, where

*a*is very close to 1 and

*b*is very close to 0. It is convenient to write

*R*

^{2}as

*N*≫ 1), respectively. Quantity

*p*(

*U*)

*dU*, of observing a wind speed between

*U*and

*U*+

*dU*, written as

*σ*/

_{U}*U*depends only on the shape parameter

*k*via

The rms difference Δ*U*_{rms} can arise from a number of causes, including differences between scalar (cup type) and vector (remote type) measurements; remote sensing sampling over spatially distributed volumes; sampling spread over time; and spatial separation between the remote sensing volumes and the mast sensor. Except in the case of complex terrain, these differences are essentially random instead of systematic and are a result of turbulent fluctuations in wind speed being sensed differently by the mast sensors and the remote sensors. Systematic differences occur in complex terrain (Bingol et al. 2009; Behrens et al. 2012; Bradley et al. 2012; Bradley 2012). It is assumed that instrumentation is setup carefully so problems such as poor alignment are not an issue.

Clearly, *R*^{2} is not a property of the remote sensing instrument alone. It depends on moments *U* and *R*^{2} should be achieved by any good-quality sodar or lidar remote sensing instrument, since the inherent limitations of the instrument are being reached. This essentially explains why it is possible to get very high *R*^{2} values in some intercomparisons, while much lower values are obtained in others.

*U*

_{rms}/

*U*and

*R*

^{2}for a range of wind regimes. For

*σ*/

_{U}*U*= 0.52 (Weibull

*k*= 2), the fractional rms wind difference is 6% for an intercomparison producing

*R*

^{2}= 0.985, or 4% for an

*R*

^{2}= 0.995. These results are also checked by simulating a large number (

*N*= 1000) of random mast winds from a Weibull distribution using

*r*are random numbers uniformly distributed between 0 and 1. For each mast wind, a remote instrument wind is generated with an additional random normally distributed variation having standard deviation

_{n}*σ*. The results of this stochastic simulation are shown as crosses in Fig. 1 for

_{ɛ}*σ*/

_{U}*U*= 0.6 (

*k*= 1.72).

## 3. Cup-sodar differences due to turbulence

A cup anemometer measures the total wind run in a sampling period, whereas a remote sensing instrument averages the vector wind components measured during a sampling period. Kristensen (1999) has described the bias arising from these different methods of measuring wind, as follows.

*U*

_{m,n}= [(

*U*+

_{n}*u*)

_{n}^{2}+

*υ*

_{n}^{2}]

^{1/2}, whereas a remote sensing instrument measures the vector (

*U*+

_{n}*u*,

_{n}*υ*,

_{n}*w*), albeit via three or more separate measurements. The average wind speed measured by the mast-mounted cup anemometer is (to the second order)

_{n}The term in brackets is the transverse turbulent intensity (not to be confused with *σ _{U}*/

*U*), and the difference in measured wind speeds is typically 0%–2%.

*R*

^{2}. We can simulate this by generating winds

*U*from random Weibull deviates, and then generating random

*u*,

_{n}*υ*, and

_{n}*w*values for a succession of samples at this

_{n}*U*. These turbulent components are generated by filtering a white noise spectrum to obtain a von Kármán velocity spectrum using the method described by the company MathWorks, based on Careta et al. (1993) and Ewert (2008). Results are shown in Fig. 2 and compared with the prediction from (15). Weibull parameters

*k*= 2 and

*λ*= 8 m s

^{−1}are used. A height of 80 m is chosen for the generation of

*u*,

_{n}*υ*, and

_{n}*w*and both simulated mast winds and simulated sodar winds are averaged over 10 min. Fitting a straight line to the resulting scatterplot from 1000 stochastic 10-min winds gives

_{n}*R*

^{2}values for each chosen turbulence intensity value.

Depending on the site, a sodar can receive reflections from fixed nonatmospheric objects. Consequently, sodar–mast intercomparisons are inevitably conducted with the sodar placed 80 m or more from the mast. This introduces a further difference between the sodar-measured wind and the mast-sensed winds, since the same volume of air is not being sensed. Figure 2 shows the effect on *R*^{2} of a mast–sodar separation of *D* = 80 m. In this case the *u _{n}*,

*υ*, and

_{n}*w*are sampled for the random mast winds and delayed samples used for the sodar winds, with delay

_{n}*D*/

*U*= 11.4 s in this example. Since the time constant for autocorrelation of the turbulent velocity components is around 20 s, the mast samples and the sodar samples are quite different. A similar procedure is followed as for the scalar–vector averaging example, using the same parameters, giving the data points shown as triangles in Fig. 2 and the accompanying line of best fit. Note that this procedure also includes the scalar–vector averaging differences, which account for about half of the reduction in

*R*

^{2}.

A further cause of differences between winds recorded by mast instruments and sodars arises because three or more spatially separated volumes are successively sampled by the sodar, with time delays between each volume sampling (Bradley 2007). This effect can again be simulated using the random von Kármán method. The scalar–vector effect and the mast–sodar separation effect are included, as described above. The time between sampling at height *h* = 80 m on one beam, and sampling at that height on the next beam, is taken to be 2.8 s. A three-beam sodar is simulated, with beams 1 and 2 in orthogonal planes and tilted at zenith angles of 18°, whereas beam 3 is vertical. Results are shown by squares in Fig. 2, together with the line of best fit.

Moore and Bailey (2006) presented a plot of measured differences between mast and sodar winds (equivalent to Δ*U*_{rms}/*U*) at a range of turbulent intensities. These data can be scaled and (4) used to obtain an equivalent *R*^{2}. The results are shown as crosses in Fig. 2 and, except at the highest turbulent intensities, agree well with the random von Kármán simulations. Note that the mast–sodar separation distance for the data from Moore and Bailey (2006) is not known, but 80 m is typical for such intercomparisons.

*x*,

*y*, and

*z*by solving equations that relate the wind components to the Doppler shift along each radial beam direction. As an example, a three-beam system having two beams tilted at angle

*θ*off vertical and one vertical beam would solve equations such as

*m*represent radial wind components estimated from measured Doppler shifts, and

*σ*

_{w}^{2}= 0.5

*σ*

_{u}^{2}. Because

*θ*is small, the 1/tan

*θ*dominates the first term on the right. This shows that the scatter in the speed measurement is prone to vertical turbulence in particular for small angle-tilted beams (small

*θ*).

*R*

^{2}. This effect has also been explored experimentally by Behrens et al. (2010). Figure 3, adapted from this work, shows the

*R*

^{2}obtained by comparing winds estimated from opposing beams on a five-beam Metek sodar. The beams, called here beam 1 and beam 2, are aligned with the wind with one tilted upwind by

*θ*= 17.5° and the other downwind with the same zenith angle. The time between a sample from beam 1 and a sample from beam 2 at the same height is Δ

*t*

_{12}= 18 s. A very high

*R*

^{2}(effectively 1.00) is measured at the height where turbulence from one sampled volume has moved to the second sampled volume in the time Δ

*t*

_{12}. The high

*R*

^{2}is possible because the scalar–vector averaging difference is not present (both measurements are from the same sodar), and effectively the same turbulent volume is being sampled. Since sodars sample turbulent structure in a large volume, the different pointing angles of the two opposing beams is not a factor so that

*z*tan

*θ*/

*U*is the time the turbulence takes to advect from beam 1 to beam 2 at height

*z*and at wind speed

*U*, and

*τ*is the turbulence correlation time (see, e.g., Blanc-Benon et al. 1991; Chu et al. 1996). The first-order linearized approximation of the exponential will likely only be valid close to height giving maximum

*R*

^{2}. From (4),

*z*

_{max}and

*A*. From

*z*

_{max}and the known

*θ*and Δ

*t*

_{12}, an estimate of

*U*= 2.8 m s

^{−1}is obtained. The turbulent intensity and σ

_{U}are not known. But if values of

*σ*/

_{u}*U*= 0.2 and

*σ*/

_{U}*U*= 0.22 (or

*k*= 5.2) are assumed, then

*τ*= 29 ± 1 s is obtained, equal to

*z*

_{max}/

*U*= 29 s, which would be expected if the integral length scale is assumed to be equal to

*z*

_{max}. Such a narrow Weibull could occur because the experiment by Behrens et al. (2010) had a duration of only seven days. The explanation of Fig. 3 in terms of advection of turbulence therefore seems plausible.

## 4. Conclusions

Given the above discussed differences between mast and sodar, what is the best current estimate of the fundamental wind speed errors in a sodar? The commonly quoted *R*^{2} values for remote sensing instruments are *not* a property of the instrument, but are more closely associated with how the intercomparison experiment is carried out. Very high values of the coefficient of determination, *R*^{2}, are now reported for lidar comparisons with mast instruments (Mikkelsen and Bradley 2011), providing the data are filtered to remove turbulent fluctuation and other environmental effects. The difficulty with doing this for sodars is that the signals originate from turbulence. This means that filtering data from an intercomparison experiment to retain only low-turbulence data will necessarily mean a low signal-to-noise ratio for the sodar, and a corresponding reduction in *R*^{2} resulting from statistical errors in finding the Doppler spectrum's peak position.

There is real difficulty therefore in answering the question: How good is a sodar? Most field use, away from the idealized “laboratory” environment, seems to have an *R*^{2} value of 0.975–0.985. From Fig. 1, this corresponds to a range of relative difference, compared to a cup anemometer, of around 6%. However, from Fig. 2 it is seen that only about one-third of the *R*^{2} reduction is due to the inescapable difference between scalar and vector averaging, and the remaining two-thirds of *R*^{2} loss is attributable to measurement methodology, rather than errors. Both the mast instruments and the sodar are measuring winds accurately, but just not in the same place and at the same time. This suggests that sodars being used operationally can be expected to have real rms errors of around 2% (i.e., one-third of the 6% identified in Fig. 1).

## Acknowledgments

The author is grateful to Torben Mikkelsen for the useful discussions.

## REFERENCES

Antoniou, I., Jørgensen H. E. , von Hünerbein S. , Bradley S. G. , Kindler D. , Warmbier G. , and de Noord M. , 2004: The Profiler Intercomparison Experiment (PIE).

*Proc. European Wind Energy Conf. and Exhibition,*London, United Kingdom, EWEA, CD-ROM.Behrens, P., Bradley S. , and Wiens T. , 2010: A multisodar approach to wind profiling.

,*J. Atmos. Oceanic Technol.***27**, 1165–1174.Behrens, P., O'Sullivan J. , Archer R. , and Bradley S. , 2012: Underestimation of mono-static sodar measurements in complex terrain.

,*Bound.-Layer Meteor.***143**, 97–106.Bingol, F., Mann J. , and Foussekis D. , 2009: Conically scanning lidar error in complex terrain.

,*Meteor. Z.***18**, 189–195.Blanc-Benon, P., Juvé D. , and Comte-Bellot G. , 1991: Occurrence of caustics for high-frequency acoustic waves propagating through turbulent fields.

,*Theor. Comput. Fluid Dyn.***2**, 271–278.Bradley, S. G., 2007:

*Atmospheric Acoustic Remote Sensing.*CRC Press/Taylor and Francis Group, 328 pp.Bradley, S. G., 2012: A simple model for correcting sodar and lidar errors in complex terrain.

,*J. Atmos. Oceanic Technol.***29**, 1717–1722.Bradley, S. G., Perrott Y. , and Oldroyd A. , 2012: Corrections for wind-speed errors from sodar and lidar in complex terrain.

,*Bound.-Layer Meteor.***143**, 37–48.Careta, A., Sagues F. , and Sancho J. M. , 1993: Stochastic generation of homogeneous isotropic turbulence with well-defined spectra.

,*Phys. Rev.***48E**, 2279–2287.Chu, C. R., Parlange M. B. , Katul G. G. , and Albertson J. D. , 1996: Probability density functions of turbulent velocity and temperature in the atmospheric surface layer.

,*Water Resour. Res.***32**, 1681–1688.Contini, D., Mastrantonio G. , Viola A. , and Argentini S. , 2004: Mean vertical motions in the PBL measured by Doppler sodar: Accuracy, ambiguities, possible improvements.

,*J. Atmos. Oceanic Technol.***21**, 1532–1544.Contini, D., Donateo A. , and Belosi F. , 2006: Accuracy of measurements of turbulent phenomena in the surface layer with an ultrasonic anemometer.

,*J. Atmos. Oceanic Technol.***23**, 785–801.Contini, D., Grasso F. M. , Mastrantonio G. , Viola A. P. , and Martano P. , 2007: Performances of a modular PC-based multi-tone sodar system in measuring vertical wind velocity.

,*Meteor. Z.***16**, 357–365.Ewert, R., 2008: Broadband slat noise prediction based on CAA and stochastic: Sound sources from a fast random particle-mesh (RPM) method.

,*Comput. Fluids***37**, 369–387.Kristensen, K., 1999: The perennial cup anemometer.

,*Wind Energy***2**, 59–75.Mastrantonio, G., and Fiocco G. , 1982: Accuracy of wind velocity determinations with Doppler sodar.

,*J. Appl. Meteor.***21**, 820–830.Mikkelsen, T., and Bradley S. G. , 2011: Lidar remote sensing.

,*Int. Sustainable Energy Rev.***5**, 19–23.Moore, K. E., and Bailey B. H. , 2006: Maximizing the accuracy of sodar measurements for wind resource assessment. AWS Truewind Research Note 2, 10 pp. [Available online at http://www.awstruepower.com/2006/08/maximizing-the-accuracy-of-sodar-measurements-for-wind-resource-assessment/.]