TheHøvsøretallwindproﬁleexperiment–adescription...

The Høvsøre tall wind profile experiment – a description1

of wind profile observations in the atmospheric boundary2

layer3

Alfredo Pena ([email protected]), Rogier Floors and Sven-Erik Gryning4

DTU Wind Energy, Risø Campus, Technical University of Denmark,5

Frederiksborgvej 399, 4000 Roskilde, Denmark6

Abstract. We present an analysis of data from a nearly one-year measurement7

campaign performed at Høvsøre, Denmark. Høvsøre is a coastal farmland area, where8

the terrain is flat. Within the easterly sector upstream of the site, the terrain is9

nearly homogenous. This topography and conditions provide a good basis for the10

analysis of vertical wind speed profiles under a wide range of atmospheric stability,11

turbulence, and forcing conditions. One of the objectives of the campaign was to12

serve as a benchmark for flow over flat terrain models.13

The observations consist of combined wind lidar and sonic anemometer mea-14

surements at a meteorological mast. The sonic measurements cover the first 100 m15

and the wind lidar started measuring at 100 m every 50 m in the vertical. Results16

of the analysis of the observations of the horizontal wind speed components in the17

range 10–1200 m and surface turbulence fluxes are illustrated in detail combined18

with forcing conditions derived from mesoscale model simulations.19

Ten different cases are here presented. The observed wind profiles approach well20

the simulated gradient and geostrophic winds close to the simulated boundary-layer21

height during both barotropic and baroclinic conditions, respectively, except for a22

low-level jet case as expected. The simulated winds are also presented for complete-23

ness and show good agreement with the measurements, generally underpredicting24

the turning of the wind in both barotropic and baroclinic cases.25

Keywords: Atmospheric boundary layer, Baroclinity, Geostrophic wind, Sonic mea-26

surements, Turbulence fluxes, Wind lidar, Wind profile27

1. Introduction28

There are several type of models for the prediction of the wind and its29

related parameters in the atmospheric boundary layer (ABL) ranging30

from the mesoscale, e.g. the advanced Weather Research and Fore-31

casting (WRF) model (Skamarock et al., 2008), to the microscale,32

e.g. the Wind Atlas Analysis and Application Program (WAsP) model33

(Mortensen et al., 2007). Particularly, the microscale models have been34

developed to improve the wind predictions over complex, forested, and35

heterogeneous terrain. However, these assume near-neutral stability36

and barotropic conditions in most cases, which might be far from the37

actual conditions at many sites where wind predictions are important,38

c© 2013 Kluwer Academic Publishers. Printed in the Netherlands.

Hovsore_tall_wind_profile.tex; 30/07/2013; 13:18; p.1

2 Alfredo Pena, Rogier Floors and Sven-Erik Gryning

and thus they become highly uncertain, especially above the surface39

layer.40

Understanding of the change of the horizontal wind velocity compo-41

nents with height and, therefore, of the turning of the wind in the ABL42

under different surface, wind, and forcing conditions is essential for the43

parametrization of atmospheric processes and the improvement of both44

microscale and mesoscale models, e.g. via the planetary boundary layer45

(PBL) parametrizations. This understanding can be partly achieved46

through the analysis of wind measurements in the ABL.47

Observations of the wind velocity components in the ABL are how-48

ever scarce and in many cases controversial as the instrumentations49

used in the experiments were inaccurate (radiosondes and similar tech-50

niques in most cases), the assumptions for the analysis of the measure-51

ments were too simplistic, and the surface and forcing conditions were52

not always measured. To the authors’ knowledge, there are only three53

experiments particularly designed for the analysis of the ABL winds.54

The first is the Leipzig experiment, initially described by Mildner55

(1932), which has been used extensively as a benchmark for numerical56

and analytical flow models. The data commonly used are actually the57

results from the reanalysis performed by Lettau (1950), who assumed a58

neutral and barotropic atmosphere to reconstruct the vertical profiles59

of wind and turbulent exchange coefficient, although the conditions60

were probably stable and the upwind flow inhomogeneous (Riopelle and61

Stubley, 1988; Bergmann, 2006). There is also controversy on the sur-62

face roughness and the boundary-layer height values of the experiment63

(Lettau, 1962; Hess, 2004).64

The second is the O’Neill experiment, performed in Nebraska and65

designed by Lettau (Lettau and Davidson, 1957), who tried to avoid66

some of the problems inherent to the Leipzig experiment such as ter-67

rain heterogeneity and unknown atmospheric stability conditions. The68

ABL winds were however measured using both balloons, radiosondes,69

and airplanes, although Lettau already anticipated that ground-based70

methods were needed for accurate measurements (Lettau, 1990). This71

experiment is not popular among modellers.72

The last is the Wangara experiment, performed in Australia in 196773

(Clarke et al., 1971). Hourly double-theodolite observations of pilot bal-74

loons were performed at different stations over 40 days under different75

surface, stability, and forcing conditions. However, as pointed out by76

Clarke and Hess (1974), thermal winds were not accurately estimated,77

and the surface friction velocity and heat flux had to be indirectly78

estimated from a drag-coefficient method and wind and temperature79

profiles, respectively.80


The Høvsøre tall wind profile experiment 3

In wind energy, there is now an understanding of the importance of81

accurate wind-speed measurements and the observation of the vertical82

wind shear for wind and load predictions. However, there is very little83

knowledge about the influence of (and the mechanisms controlling)84

the turning of the wind, the boundary-layer height, and baroclinity85

on wind turbines and structures. The Tall Wind Profile experiment,86

performed at a nearly flat and homogenous area in Denmark, observed87

ABL winds quasi-continuously during one year combining a wind lidar88

and meteorological (met) mast measurements to attempt to respond to89

the late challenges brought by the wind community. Here, we present90

ten cases (out of a much larger dataset), where the ABL winds were ac-91

curately measured up to about 1000 m and the forcing and winds were92

simulated with a mesoscale model. We first provide some definitions93

(Section 2) useful for the interpretation of the observations. We then94

describe the site, the measurements, and the modelling in Section 3.95

The data analysis is explained in Section 4 and illustrates the cases96

explored in this study. Summary and conclusions are provided in the97

last section.98

2. Definitions99

The three wind speed components (u, v, w) are here placed on a left-100

handed coordinate system (see Fig. 1), being u and v the horizontal and101

w the vertical wind speed components (the latter aligned with the z-102

axis, i.e. the height above the ground). In this fashion, by aligning u at103

the surface (as shown in Section 3 this means at a 10-m height) with the104

horizontal wind speed vector (i.e. with the wind direction), v is zero105

at the surface increasing when the wind vector turns clockwise with106

height (veering wind) and decreasing when it turns counterclockwise107

with height (backing wind). The horizontal wind speed magnitude U108

at any height is then computed as109

U =(

u2 + v2)1/2

. (1)

The friction velocity u∗ is defined as110

u2∗=

(

u′w′2+ v′w′

2)1/2

, (2)

where the overbar indicates a time average and the primes fluctuations111

over the average. The Obukhov length L is defined as112

L =−Tv u3

∗

κ g w′T ′

v

, (3)



U

θ

...............................

................................................

u

v

EW

N

S

x

y

Figure 1. Coordinate system used for the wind profile and the estimation of thesurface geostrophic and thermal winds. U is the horizontal wind speed vector at thesurface and θ the wind direction from the geographical north

where Tv is the virtual temperature, κ the von Karman constant (here113

we use the value 0.4), g the gravitational acceleration, and w′T ′

v the114

virtual kinematic heat flux.115

In the surface layer and under neutral atmospheric conditions, the116

logarithmic wind profile117

U =u∗κ

ln

(

z

zo

)

, (4)

where zo is the roughness length, is commonly used to predict the wind118

speed over flat, non-forested, and homogenous terrain without taking119

into account its turning with height.120

We derive the surface geostrophic, the gradient, and the ‘total’121

geostrophic wind (the latter known here simply as geostrophic wind)122

from the WRF simulations (explained in Section 3). The gradient wind123

takes into account the curvature of the isobars in the surface geostrophic124

wind. For their derivation, we define their two components oriented125

with the x and y axis, eastwards and northwards, respectively (see126

Fig. 1).127

The two components of the surface geostrophic winds(

Gox , Goy

)

are128

then given as,129

Gox = −

1

ρ fc

∂Po

∂yand (5a)

Goy =1

ρ fc

∂Po

∂x, (5b)



where ρ is the air density, fc the Coriolis parameter, and Po the mean130

sea level pressure. The baroclinic effect on the two components of the131

geostrophic wind (Gx, Gy) is derived as (Holton and Hakim, 2004),132

GTx = −

1

fc

∂Φz − ∂Φo

∂yand (6a)

GTy =1

fc

∂Φz − ∂Φo

∂x, (6b)

where Φ is the geopotential, and the subindexes z and o refer to a given133

height and the surface, respectively. The magnitude of the gradient134

wind Gg is a function of the magnitude of the surface geostrophic wind135

Go =√

G2ox +G2

oy (Kristensen and Jensen, 1999)136

Go

fc R

(

Gg

Go

)2

+Gg

Go− 1 = 0, (7)

where R is the radius of the curvature of the isobars. The two compo-137

nents of the gradient wind(

Ggx , Ggy

)

are computed assuming that the138

angle between them is equal to that between Gox and Goy .139

The geostrophic wind components are thus computed as140

Gx = Ggx +∆z GTx and (8a)

Gy = Ggy +∆z GTy, (8b)

where ∆z is the difference between a given height and the surface (the141

z and o levels in Eqs. (6a) and (6b)). uG and vG hereafter refer to142

Gx and Gy when rotated into the u-v coordinate system (Fig. 1). The143

magnitude of the geostrophic wind is given as UG =√

u2G + v2G.144

3. Site, measurements, and WRF modelling145

3.1. Site146

The measurements were performed at the National Test Station for147

Wind Turbines located in a coastal area known as Høvsøre in west148

Jutland, Denmark (Fig. 2). Høvsøre is a flat farmland area, which is149

fairly homogeneous with disturbances on the flow from the presence of150

the North Sea and the Nissum Fjord, 1.7 km west and 950 m south,151

respectively, some scattered trees, houses, and crop patches east, the152

village of Bøvlingbjerg 3 km south-east, and five wind turbines north153

of a meteorological mast placed south of the station at a height of 2 m154

above mean sea level.155



120◦

Meteorological mast

1 km

Nissum

Fjord

North

Sea

30◦

Figure 2. National Test Station for Wind Turbines at Høvsøre, Denmark. The mete-orological mast location (circle) and the analyzed sector are also shown. The imagewas taken from Google Earth

Easterly winds (within the range 30◦–120◦) are therefore nearly ideal156

for the study of flow over flat and homogeneous terrain. Thus, we focus157

our analysis to these directions, although winds within this range are158

not the predominant ones at Høvsøre (Pena, 2009).159

3.2. Measurements160

The measurements come from two different types of instrumentation:161

sonic anemometers at the meteorological mast and a wind lidar system.162

3.2.1. Sonic measurements163

Metek USA-1 scientific sonic anemometers are placed at 10, 20, 40, 60,164

80, and 100 m on the booms facing north of the meteorological mast.165

Thus, for easterly winds the mast effect on the sonic measurements is166

negligible. Other details about the mast and its instrumentation can be167

found in Jørgensen et al. (2008). The recording frequency of the sonic168

time series is 20 Hz.169

3.2.2. Wind lidar170

AWLS70 WindCube, a pulsed wind lidar from the company Leosphere,171

was installed ≈10 m south-west from the meteorological mast during172

the period April 2010–March 2011. The WLS70 measures the radial173

velocity at four azimuthal positions separated 90◦ in the horizontal174

plane with an inclination of 15◦ from the zenith and derives the three175

wind-speed components assuming horizontal flow homogeneity. The176

wavelength, the pulse length, and pulse energy of the laser are 1.5 µm,177

400 ns, and 20 µJ, respectively. Under ‘good’ aerosol conditions, the178



system reports measurements up to 2 km. Recorded data depend on179

the instrument’s threshold for the carrier-to-noise ratio (CNR), which180

was left equal to the default value (−35 dB). The instrument measures181

from a height of 100 m every 50 m at a rate of ≈10 s for each azimuthal182

position. The measurement volume at each azimuthal position and183

height extends ≈60 m radially.184

−200

0

200

−2000

2000

100

200

300

400

500

600

x [m]y [m]

z[m

]

EN

W

S−200 −100 0 100 200

−200

−150

−100

−50

0

50

100

150

200

x [m]

y[m

]

N

W

S

E

←−ul

←−vl

Figure 3. WLS70 wind lidar scanning pattern from side (left) and top (right) views.The instrument is shown in the grey rectangle, the mast in the white square, andthe four azimuthal radial positions in black, cyan, blue, and red colours

As shown in Fig. 3, the wind lidar was positioned with an offset185

of 50◦ from the north in order to avoid influence of the mast on the186

eastern azimuthal position shown in blue. This is important as one wind187

lidar wind-speed component, vl, is related to the difference between the188

eastern and western radial velocities (in this case indicated by the red189

and blue points).190

3.3. WRF modelling191

We use outputs of simulations using the WRF model version 3.4 (Ska-192

marock et al., 2008). Two domains are used with horizontal resolutions193

of 18 and 6 km as shown in Fig. 4.194

The model was run in analysis mode every 10 days starting at195

0000 UTC during April 2010–April 2011 (see details in Gryning et al.,196

2013b). Initial and boundary conditions came from the National Center197

for Environmental Prediction (NCEP) final analysis data and real-time198

global sea-surface temperature analysis from NCEP. Model outputs199

from 24 to 264 h were used to generate continuous time series of 10-200

min resolution (24-h spin-up period). Timesteps were 120 and 40 s for201

the outermost and innermost domains, respectively. The first 11 vertical202



Figure 4. Model outermost (D1) and innermost domains (D2) used for the simu-lations (black lines). The red square shows the area used for the derivation of thepressure and geopotential gradients

model levels are approx. at 14, 43, 72, 101, 130, 191, 311, 485, 705, 966,203

and 1263 m. The first-order Yonsei University (YSU) PBL scheme is204

used (Hong et al., 2006). The boundary-layer height is estimated in the205

PBL parametrization as a function of the ratio of the critical to the206

bulk Richardson number at the boundary-layer top. The model was207

nudged in the outermost domain above the 11th model level towards208

the analysis data. Other details about the model setup can be found in209

Gryning et al. (2013a).210

4. Data analysis211

The WLS70 system provides two datasets: a ‘fast’ one with the ≈10 s212

measurements at each height and azimuthal position and a ‘slow’ one213

with outputs for the same parameters but based on 10-min averages.214

The sonic time series of the wind velocity and temperature were recorded215

on a third dataset. Here we provide a description of the filtering and216

selecting criteria we use on the three datasets to produce the final217

output with wind speed profiles from 10 m up to ≈1000 m and sonic218

turbulence measurements from 10 m up to 100 m averaged in 30-min219

periods.220

The first part of the analysis is performed on the ‘slow’ wind lidar221

dataset. We extract 10-min data from 100 up to 600 m to increase the222

number of vertical profiles for the study. We also apply a filter so that223

each 10-min profile shows measurements every 50 m with a minimum224

mean CNR of –22 dB, used by Floors et al. (2013) and Pena et al. (2013)225

for accurate wind speed measurements, and with 100% availability (this226



is a number relating the amount of ≈10 s records where CNR > −35 dB227

within the 10-min period).228

We then exclude non-easterly winds by looking at the wind direction229

at 100 m only. Since the final analysis is performed in 30-min averages,230

we extract data where three consecutive 10-min intervals are found.231

We also apply a stationarity check based on the criteria by Lange232

et al. (2004), where the ratio between any of the three consecutive233

10-min mean values of U and wind direction is limited to the interval234

[0.8,1.2]. This is mostly done to avoid large differences between the235

three consecutive 10-min periods. We extract the wind lidar ‘fast’ time236

series correspondent to those 30-min periods.237

Statistics of all wind lidar parameters are performed based on the238

30-min periods. Here we choose to present those related to the mean239

wind speed components only, since turbulence quantities are highly240

influenced by the wind lidar’s measurement volume and scanning pat-241

tern. The over/underprediction of the wind lidar turbulence (compared242

to that of a sonic measurement) depends on the turbulence spectral243

tensor, the observed height, and atmospheric stability, among others244

(Sathe et al., 2011; Sathe and Mann, 2012).245

We then extract the concurrent sonic time series; these are linearly246

detrended over the 30-min period. u∗ and L are estimated from the247

sonics; here we assume that the sonic temperature and kinematic heat248

flux are good estimates of Tv and w′T ′

v in Eq. (3). We use the crosswind249

corrections in Liu et al. (2001) for w′T ′

v. However, the sonics did not250

continuously operate during the wind lidar campaign. We therefore251

have to make a compromise between data amount and number of sonic252

levels; we leave out of the analysis the sonics at 20, 60, and 80 m. The253

total number of combined sonic/wind lidar 30-min observations is 371254

(we also check that on each wind lidar 30-min time series, there are at255

least 160 of the ideal ≈180 records). Table I shows a summary of the256

results of the filtering criteria.257

We extract the 10-min instantaneous outputs from the WRF simu-258

lations correspondent to the time stamps of the combined sonic/wind259

lidar observations and average them in 30 min means. The gradi-260

ents of geopotential and mean sea level pressure in Eqs. (5a)–(6b)261

are computed from these outputs over a 300 km square around the262

meteorological mast (see Fig. 4). Linear regression is applied to the263

northward and eastward gradient of the mean sea level pressure field.264

Similarly, the gradient of the geopotential difference in Eqs. (6a) and265

(6b) is computed. The gradient of the geopotential difference between266

any given model level and the first one is computed using linear regres-267

sion; this results in the baroclinic term at each level that is added to268

the gradient wind. We estimate the gradient wind in Eq. (7) assuming269



Table I. Results of the analysis of measurements after the filtering and selectingcriteria described in Sec. 4. The total number of potential 10-min measurementsfor the period of the wind lidar campaign at Høvsøre is 49968. The wind lidarreports 39173 10-min mean measurements at least at one height

Filtering criteria applied Number of samples/profiles left

Wind lidar measurements at each height 15772 10 min

in the range 100–600 m after availability

and CNR criteria

Easterly winds only (30◦–120◦) 2592 10 min

Three consecutive 10-min periods 1857 10 min

Stationarity criterion 1713 10 min (571 30 min)

Sonic availability (10, 40 and 100 m) 371 30 min

that in a given area the pressure field can be described by the surface270

(Kristensen and Jensen, 1999),271

P (x, y) = Pr + Pxx+ Pyy + 0.5(

Pxxx2 + Pxyxy + Pyyy

2)

, (9)

where Pr is a reference pressure and Px, Pxx, Pxy, Py, and Pyy are the272

first and second derivatives with respect to x and y, respectively. The273

curvature can then be estimated using this field as (Kristensen and274

Jensen, 1999)275

R =

(

P 2x + P 2

y

)3/2

PyyP 2x − 2PxyPxPy + PxxP 2

y

. (10)

The curvature of the isobars is estimated using the algorithm described276

in Shary (1995), which fits Eq. (9) to a 3 by 3 grid in a least-squares277

sense. A mean curvature is then computed for all grid points in the278

area of interest around the site.279

4.1. Wind lidar accuracy280

The wind lidar and the sonic measurements overlap at the 100-m height.281

Figure 5 illustrates a comparison of the total 371 30-min observations282

of U and direction from the 100-m sonic and wind lidar. As shown both283

wind speed and direction measurements show a very high correlation,284

although the techniques and the measurement volumes are different.285

The sonic shows a slightly higher wind speed compared to the wind286

lidar particularly within the high wind speed range. For the wind profile287

analysis, we scale/adjust the wind lidar wind speed measurements at288

all heights (of both components) so that the 100-m wind lidar matches289



the sonic observations at 100 m. The wind lidar shows an offset of290

4–10◦ with the sonic wind direction and as with the wind speed, we291

correct/adjust all wind lidar directions so that in the wind profile292

analysis both show the same wind direction at 100 m.293

0 3 6 9 12 15 180

3

6

9

12

15

18

sonic wind speed [m s−1]

lidarwindspeed[m

s−1]

y = 0.95xy = 0.93x + 0.21R2 = 0.98N = 371

40 60 80 100 120

40

60

80

100

120

sonic wind direction [deg.]lidarwinddirection[deg.]

y = 0.92xy = 0.95x − 2.0R2 = 0.99N = 371

Figure 5. Comparison of the sonic anemometer and the wind lidar 30-min measure-ments at 100 m of horizontal wind speed magnitude (left frame) and wind direction(right frame). The observations are shown with grey markers together with a 1:1 solidline in black. The results of a linear regression through origin, a linear regressionwith offset, the Pearson’s linear correlation coefficient (R2), and the number ofobservations (N) are also given

4.2. Boundary-layer wind profiles294

Here we describe and illustrate some cases (ensemble means) that can295

be studied and further modelled based on the combined sonic/wind296

lidar data and the simulations (hereafter all WRF simulations are297

referred to as simulations). They are selected because they show par-298

ticular wind speed and turning of the wind situations, and a variety of299

surface and simulated forcing conditions. Table II provides a summary300

of the ten selected cases.301

For each case the ensemble mean of 30-min averages of observed302

vertical profiles of u, v, and U from 10 up to 600 m is illustrated. As303

mentioned before, we aligned u at 10 m with U at 10 m (so v at 10 m304

is always 0 m s−1). The turning of the wind can therefore be estimated305

from the angle between v and u (see Fig. 1).306

The median of the simulated boundary-layer height (zi) is also shown.307

Because in some cases zi is between 600 and ≈1200 m, we also illustrate308

the ensemble mean of the concurrent 10-min wind lidar profiles from309

650 up to 1200 m. These are shown in a different colour because they310

might be more uncertain than the measurements below. This is due311



Table II. Summary of Tall wind profile cases. All cases represent observationsperformed in 2010

Case Observation period Description

(local time)

1 May 5, 0530–0700 Very stable surface conditions, high wind

veering, and low forcing

2 June 9, 0600–0730 Neutral to stable surface conditions and low

boundary-layer height

3 Apr. 25–26, 2200–0130 Low-level jet and highly baroclinic atmosphere

4 Sep. 8, 0150–0620 Stable surface conditions and high forcing

Sep. 8–9, 2040–0700

5 Sep. 7, 1330–1700 Neutral surface conditions and barotropic

Sep. 8, 1330–1650 atmosphere

Sep. 9, 1400–1830

6 May 7, 0830–1630 Slightly unstable surface conditions, high

forcing, and nearly barotropic atmosphere

7 Sep. 27, 1320–1920 Neutral surface conditions and baroclinic

atmosphere

8 Nov. 24, 0200–0330 Slightly stable surface conditions and highly

baroclinic atmosphere

9 Sep. 25, 0940–1420 Very unstable surface conditions and low forcing

10 Dec. 12, 0820–0950 Stable surface conditions and highly baroclinic

atmosphere

to their generally low CNR (it might be lower than −22 dB) and312

that the amount of “concurrent” measurements might decrease with313

height (neither the CNR nor the availability criteria are applied to314

these retrievals).315

Together with the observed profiles, the simulated gradient and316

geostrophic winds and simulated wind outputs are shown. They are317

also rotated using the observed wind direction at 10 m. The prediction318

of U from the log profile, Eq. (4), using the observed u∗ value at 10 m319

and zo = 0.015 m (Pena et al., 2010b; Pena et al., 2010a), up to zi is320

also illustrated. In Tables III–VIII, the ensemble mean of the measured321

and simulated parameters for all the discussed cases are given.322

4.2.1. Case 1323

Here, the wind lidar observed some of the largest values of turning324

of the wind: the wind veers 43◦ and 66◦ in the first 100 and 200 m,325

respectively, and backs slightly above this. This is due to a small baro-326



clinic component clearly observed in v (see Fig. 6, where both v and U327

decrease above 200 m both in the observations and the simulated wind328

and geostrophic components). The observed wind direction at 10 m is329

66◦, so this small baroclinic component decelerates the flow due to the330

colder air over land compared to the warmer air over sea.331

−1

0

1

2 U100

U10

G14G100

N

S

EW

10

20

40

100

200

400

800

1200

0 5 10 15 20

height[m

]

u [m s−1]

10

20

40

100

200

400

800

1200

−5 0 5 10 15

v [m s−1]

10

20

40

100

200

400

800

1200

0 5 10 15 20

U [m s−1]

Figure 6. Observed (black circles) and simulated (green lines) mean vertical profilesof u, v, and U for case 1. The error bars represent± one standard deviation. Observedprofiles from 650 up to 1200 m are shown in cyan circles. The simulated zi value isshown in the dashed grey line. The gradient and the geostrophic winds are shown inblue and red lines, respectively. The prediction using the log profile is also illustratedin the grey solid line. Observed horizontal wind speed vectors at 10 m and at a heightclose to the simulated zi, and simulated geostrophic winds at the first model level(≈14 m) and at a height close to the simulated zi are shown above the profiles

Close to the surface the observed conditions are very stable and the332

stability increases with height as shown by the z/L values in Table IV.333



The observed friction velocity at 10 m is very low (u∗ = 0.19 m s−1)334

and decreases linearly with height (the observed surface winds and335

simulated forcing are rather low). The simulated zi is 87 m; the closest336

model level to the zi value (i.e. 101 m) shows a geostrophic wind angle337

(i.e. that between the simulated geostrophic wind and the observed338

wind at 10 m) of 42◦, which is very close to the observed turning of339

the wind. Although the simulated winds agree reasonable well with the340

behaviour of both observed u and v (for such a complicated modelling341

scenario), the turning of the wind is highly underestimated; it is 20◦342

and 31◦ at 101 and 191 m, respectively.343

4.2.2. Case 2344

The simulated zi is relatively low (169 m), and the observed profiles of345

u, v, and U show a wind maximum at ≈350 m and rapidly decrease346

upwards (Fig. 7). This might be due to wind deceleration due to baro-347

clinity (particularly for u). The observed wind direction at 10 m is 69◦,348

so u decelerates due to the warmer air south compared to that north of349

Høvsøre. Both observed u and U approach the simulated geostrophic350

wind high above zi. The observed wind veers 8◦ and 26◦ in the first 100351

and 250 m, respectively, agreeing with the simulated geostrophic wind352

angle (28◦ at 191 m). Thus zi is probably between the simulated value353

and 250 m.354

Near the surface the observed atmospheric conditions are close to355

neutral and become slightly stable with height (Table IV). The observed356

friction velocity at 10 m is 0.37 m s−1 and is relatively constant with357

height. The wind speeds are not as low as in Case 1 and the log profile358

predictions are much closer to the observations. The simulated winds359

are in very good agrement with the observed v component, but highly360

underpredict the observations of u, and thus, U .361

4.2.3. Case 3362

Here, a low-level jet (LLJ) is clearly observed (Fig. 8). The observed363

U at 10 m is not very high (6.33 m s−1), but it reaches 20.11 m s−1 at364

400 m, which is the wind speed maximum, and that height is similar365

to the simulated zi (351 m). The simulated geostrophic wind angle is366

58◦ at 485 m and the observed wind veers 11◦ and 61◦ at 100 and367

500 m, respectively. The wind is highly ageostrophic (the observed368

wind around the wind maximum is much higher than the geostrophic369

wind), as expected. Baroclinity has a high impact on the observa-370

tions and simulated geostrophic wind (on the latter already below371

the simulated zi). The observed 10-m wind direction is 107◦ so that372

the high baroclinity component in u is due to positive temperature373



U250

G191

G14

U10

W E

S

N

10

20

40

100

200

400

800

1200

0 5 10 15 20

height[m

]

u [m s−1]

10

20

40

100

200

400

800

1200

−5 0 5 10 15

v [m s−1]

10

20

40

100

200

400

800

1200

0 5 10 15 20

U [m s−1]

Figure 7. Similar to Fig. 6 but for case 2

difference northwards. At about 1200 m the observed winds approach374

the simulated geostrophic values.375

The observed atmospheric conditions close to the surface are stable376

and L is nearly the same at all sonic levels. The observed friction veloc-377

ity at 10 m is 0.38 m s−1 and decreases slightly with height (Table IV).378

Although the simulated winds behave similarly compared to the ob-379

served u and v components, the strength of the LLJ is underestimated380

as found by Floors et al. (2013).381

4.2.4. Case 4382

The conditions as seen by the simulations are nearly barotropic and383

the strength of the forcing is very high (UG = 21.42 m s−1 at the first384

model level). The simulated zi (763 m) corresponds well with the height385

where the observed U shows its maximum (Fig. 9). The observed U is386

high at all levels: 11.75 and 21.18 m s−1 at 100 and 600 m, respectively.387

Close to the simulated zi (at 705 m), the simulated geostrophic wind388



W E

S

NU10

G485

U500

G14

10

20

40

100

200

400

800

1200

0 5 10 15 20

height[m

]

u [m s−1]

10

20

40

100

200

400

800

1200

−5 0 5 10 15

v [m s−1]

10

20

40

100

200

400

800

1200

0 5 10 15 20

U [m s−1]


angle is 45◦; the observed veering is 7◦ and 45◦ in the first 100 and389

700 m, respectively. The observed u and v components approach well390

the simulated geostrophic wind close to zi.391

Near the surface the observed atmospheric conditions are stable392

and remain nearly constant in the first 100 m. The observed friction393

velocity at 10 m is 0.45 m s−1 (the observed wind direction at 10 m394

is 84◦) and also remains constant within the sonics’ range (Table IV).395

The simulated winds are in good agreement with the observed U (v396



N

E

S

WU10

G705

G14U700

10

20

40

100

200

400

800

1200

0 5 10 15 20

height[m

]

u [m s−1]

10

20

40

100

200

400

800

1200

−5 0 5 10 15

v [m s−1]

10

20

40

100

200

400

800

1200

0 5 10 15 20

U [m s−1]


is slightly overpredicted) and the highest differences are found close to397

and above zi.398

4.2.5. Case 5399

Very high wind speeds are observed close to the surface (U = 10.04400

and 13.26 m s−1 at 10 and 100 m, respectively) and the conditions,401

as seen by the simulations, are nearly barotropic with high forcing402

(UG = 19.45 m s−1 at the first model level). The simulated zi (1120 m)403

matches well the height of the observed wind speed maximum (Fig. 10).404

The simulated geostrophic wind angle is 28◦ at 1263 m and the observed405

wind veers 4◦ and 25◦ at 100 and 1200 m, respectively (the observed406


18Alfre

doPena,RogierFloors

andSven-E

rikGryning

Table III. Observed mean horizontal wind-speed components (u, v) at different heights z. The simulated geostrophic wind com-ponents (uG, vG) are also given at different levels (between 10 and 950 m the closest model level output to an observed height isused). The results are provided for Cases 1–4, where the number in parenthesis indicates the number of 30-min averages used forthe ensemble mean and zi indicates the boundary-layer height from the model simulations

Case 1 (3): zi ≈ 87 m Case 2 (3): zi ≈ 169 m Case 3 (7): zi ≈ 351 m Case 4 (23): zi ≈ 763 m

z u v uG vG u v uG vG u v uG vG u v uG vG

[m] [m s−1] [m s−1] [m s−1] [m s−1]

10 3.01 0.00 4.96 4.76 5.10 0.00 9.39 5.07 6.33 0.00 9.42 11.16 7.31 0.00 15.05 15.09

40 4.33 1.30 5.00 4.73 6.06 0.26 9.36 5.07 8.00 0.52 9.35 11.15 8.96 0.33 15.05 15.06

100 4.92 4.67 5.11 4.67 7.82 1.08 9.24 5.06 10.59 2.14 9.12 11.10 11.65 1.51 15.03 14.99

150 3.69 6.56 5.14 4.65 9.20 2.48 9.20 5.03 13.20 4.16 8.97 11.07 13.75 3.07 15.01 14.96

200 2.88 6.34 5.15 4.63 9.87 3.97 9.14 4.94 14.51 5.67 8.57 10.90 15.03 4.15 14.98 14.92

250 2.51 5.37 - - 9.98 4.91 - - 15.29 7.70 - - 15.86 5.47 - -

300 3.02 5.58 5.09 4.52 10.79 5.30 8.99 4.84 15.81 10.27 7.66 10.54 16.66 6.72 14.89 14.86

350 2.96 5.29 - - 11.51 5.67 - - 14.86 13.21 - - 17.02 7.99 - -

400 2.77 4.96 - - 11.66 5.46 - - 12.70 15.52 - - 17.12 9.19 - -

450 2.69 4.66 - - 11.53 4.97 - - 10.41 16.31 - - 17.10 10.37 - -

500 2.80 4.40 5.00 4.27 11.27 4.45 8.52 4.75 8.76 16.10 6.37 10.12 17.03 11.49 14.78 14.85

550 3.05 4.19 - - 10.96 4.09 - - 7.44 15.78 - - 16.78 12.60 - -

600 3.32 4.05 - - 10.49 4.01 - - 6.29 15.42 - - 16.20 13.66 - -

650 3.63 4.02 - - 10.30 4.15 - - 5.03 15.00 - - 15.61 14.66 - -

700 3.78 4.15 4.96 3.88 10.04 4.12 7.49 4.73 3.97 14.57 4.92 9.64 15.17 15.32 14.70 14.92

750 3.85 4.29 - - 9.69 3.87 - - 3.23 14.20 - - 14.73 15.81 - -

800 3.91 4.43 - - 9.23 3.56 - - 2.67 13.83 - - 14.33 16.15 - -

850 3.95 4.60 - - 8.78 3.20 - - 2.21 13.32 - - 14.04 16.37 - -

900 3.99 4.83 - - 8.44 2.90 - - 1.80 12.67 - - 13.89 16.50 - -

950 4.06 4.98 4.94 3.50 8.16 2.74 5.81 4.89 1.54 12.03 3.34 9.20 13.78 16.56 14.63 15.04

1000 4.07 5.14 - - 7.82 2.66 - - 1.45 11.48 - - 13.68 16.53 - -

1050 4.12 5.26 - - 7.28 2.56 - - 1.47 11.03 - - 13.65 16.48 - -

1100 4.25 5.44 - - 6.74 2.35 - - 1.58 10.67 - - 13.37 16.20 - -

1150 4.27 5.53 - - 6.12 2.19 - - 1.78 10.28 - - 12.91 15.80 - -

1200 4.25 5.54 - - 5.40 2.10 - - 1.91 9.91 - - 12.69 15.61 - -

1263 - - 4.78 3.18 - - 3.74 5.14 - - 1.68 8.90 - - 14.47 15.07

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Table IV. Observed mean friction velocity u∗ and dimensionless stability param-eter z/L at different heights z. The results are provided for Cases 1–4

Case 1 Case 2 Case 3 Case 4

z u∗ z/L u∗ z/L u∗ z/L u∗ z/L

[m] [m s−1] [-] [m s−1] [-] [m s−1] [-] [m s−1] [-]

10 0.19 0.035 0.37 -0.003 0.38 0.077 0.45 0.045

40 0.15 0.946 0.34 0.058 0.37 0.216 0.45 0.184

100 0.08 1.870 0.40 0.263 0.35 0.673 0.50 0.386

wind direction at 10 m is 100◦). The observed wind-speed components407

approach well the simulated geostrophic values.408

The rather large error bars in Fig. 10 show the high wind variability409

observed during the afternoon hours of these three consecutive days.410

Within the first 100 m, the observed stability conditions are highly411

neutral (z/L values close to zero). The observed friction velocity at412

10 m is rather high (0.70 m s−1) and increases to 0.85 m s−1 at 100 m413

(Table VI). The observed U is well predicted by the log profile within414

the entire ABL. The simulated winds are in good agreement with the415

observed v component, slightly underestimating u.416

4.2.6. Case 6417

The simulations show an atmosphere that is nearly barotropic with very418

high forcing conditions (UG = 20.47 m s−1 at the first model level). The419

simulated zi is very high (1290 m) and although it is beyond the highest420

observed level, the observations of both u and v seem to approach the421

simulated geostrophic wind at zi (Fig. 11). The observed wind speeds422

are also very high: U is 14.94 and 16.54 m s−1 at 100 and 600 m,423

respectively. The observed wind direction at 10 m is 57◦. The observed424

wind veers very little: 4◦ and 14◦ at 100 and 1200 m, respectively, while425

the simulated geostrophic wind angle at 1263 m is 15◦.426

Close to the surface the observed conditions are slightly unstable427

becoming more neutral with height. The observed friction velocity at428

10 m is 0.62 m s−1 and increases with height (Table VI). The log profile429

predicts well the observed U values within the ABL. The simulated430

winds are in very good agreement with the observations, particularly431

for u. The simulated wind veering is however rather low in the first432

hundred of metres; it veers less than 1◦ at 101 m.433



N

E

S

W

U10

G14

G1263

U1200

10

20

40

100

200

400

800

1200

0 5 10 15 20

height[m

]

u [m s−1]

10

20

40

100

200

400

800

1200

−5 0 5 10 15

v [m s−1]

10

20

40

100

200

400

800

1200

0 5 10 15 20

U [m s−1]


4.2.7. Case 7434

The simulations indicate that the atmosphere is baroclinic and in both435

observed u and v components, the flow decelerates approaching zi,436

which is simulated at 746 m (Fig. 12). The observed wind direction437

at 10 m is 41◦ and so the baroclinic components on u and v are mainly438

due to the warmer air south of Høvsøre (compared to that north of it).439

Both observed wind speed components, particularly u and therefore440

U , show a higher deceleration compared to the simulated geostrophic441

wind but approach well its value at zi. The observed wind speeds are442

high: U is 11.17 and 14.37 m s−1 at 100 and 600 m, respectively. The443



G14

U1200

G1263

U10

W E

S

N

10

20

40

100

200

400

800

1200

0 5 10 15 20

height[m

]

u [m s−1]

10

20

40

100

200

400

800

1200

−5 0 5 10 15

v [m s−1]

10

20

40

100

200

400

800

1200

0 5 10 15 20

U [m s−1]


observed wind veers 5◦ and 26◦ at 100 and 700 m, respectively, and the444

simulated geostrophic wind angle is 22◦ at 705 m.445

Within the first 100 m the observations indicate that the atmo-446

sphere is near-neutral with a nearly constant friction velocity of u∗ =447

0.56 m s−1 at 10 m (Table VI). The log profile agrees reasonable448

well with the observed U values, although it overpredicts slightly the449

wind speed. During summer and early autumn, the fields and crops at450

Høvsøre are close to be harvested and a zo value of 0.015 m is perhaps451

low (see also the slight overprediction of the log profile on cases 2 and452

5). The simulated winds agree well with the behaviour of the observed453

u component, but the wind veering is overpredicted in the first 250 m.454

4.2.8. Case 8455

The observed wind veers 4◦ within the first 150 m and then backs456

upwards; 17◦ at 900 m relative to the 10-m wind (Fig. 13). This is due457



EW

U10G14

G705

U700

N

S

10

20

40

100

200

400

800

1200

0 5 10 15 20

height[m

]

u [m s−1]

10

20

40

100

200

400

800

1200

−5 0 5 10 15

v [m s−1]

10

20

40

100

200

400

800

1200

0 5 10 15 20

U [m s−1]


to a high thermal-wind component, particularly observed in v, which458

notoriously decelerates and becomes negative at 350 m. The observed459

wind direction at 10 m is 41◦, thus such a high thermal wind comes460

from a colder north-east air over land compared to the warmer south-461

west air over sea. Close to the simulated zi (897 m), the observed u and462

v components approach the simulated geostrophic wind. The simulated463

geostrophic wind angle at 966 m is –20◦ (agreeing with the observed464

backing above).465

The observed conditions within the first 100 m are slightly stable466

and the observed friction velocity is rather constant u∗ = 0.38 m s−1467

(Table VI). The simulated wind-speed components show similar be-468

haviour compared to the observations, although u is underpredicted469

above 400 m, v is overpredicted (in magnitude) the first 400 m, and the470



U10

G14

U900

G966

W E

N

S

10

20

40

100

200

400

800

1200

0 5 10 15 20

height[m

]

u [m s−1]

10

20

40

100

200

400

800

1200

−5 0 5 10 15

v [m s−1]

10

20

40

100

200

400

800

1200

0 5 10 15 20

U [m s−1]


simulated backing is underpredicted (the wind angle from the simulated471

winds is –7◦ at 966 m).472

4.2.9. Case 9473

The observed wind speeds do not change much with height within the474

first 1200 m: U is 4.91 and 4.98 m s−1 at 100 and 600 m, respectively475

(Fig. 14). There is a slight baroclinic component decelerating u close476

to zi, which is estimated at 1064 m by the simulations. The observed477

wind direction at 10 m is 91◦ and so this simulated eastward thermal478

wind is due to the higher air temperature southwards from Høvsøre.479

The simulated forcing is low: UG = 5.54 m s−1 at the first model level.480

The observed wind veers 2◦ and 14◦ at 100 and 600 m, respectively,481


24Alfre

doPena,RogierFloors

andSven-E

rikGryning

Table V. Similar to Table III but for Cases 5–8

Case 5 (19): zi ≈ 1120 m Case 6 (30): zi ≈ 1290 m Case 7 (12): zi ≈ 746 m Case 8 (3): zi ≈ 897 m

z u v uG vG u v uG vG u v uG vG u v uG vG

[m] [m s−1] [m s−1] [m s−1] [m s−1]

10 10.04 0.00 16.92 9.32 11.13 0.00 19.82 4.74 7.95 0.00 13.46 6.37 5.87 0.00 13.85 -1.16

40 11.57 0.24 16.93 9.33 12.41 0.34 19.79 4.77 9.65 0.24 13.46 6.33 7.17 0.09 13.91 -1.26

100 13.23 0.98 16.96 9.36 13.90 1.04 19.69 4.84 11.13 1.01 13.45 6.23 8.57 0.65 14.06 -1.53

150 15.14 2.21 16.97 9.37 14.55 1.61 19.65 4.88 11.85 1.18 13.44 6.17 9.16 0.73 14.13 -1.67

200 15.72 2.36 17.01 9.38 14.93 1.69 19.55 4.95 12.34 1.47 13.42 6.06 9.68 0.72 14.27 -1.96

250 15.92 2.62 - - 15.13 1.71 - - 12.73 1.80 - - 10.00 0.56 - -

300 16.48 2.76 17.09 9.41 15.35 1.78 19.37 5.10 13.06 2.15 13.36 5.83 10.49 0.31 14.53 -2.54

350 16.74 2.95 - - 15.60 1.90 - - 13.29 2.59 - - 10.78 -0.09 - -

400 16.92 3.08 - - 15.78 1.94 - - 13.42 3.10 - - 11.11 -0.63 - -

450 17.11 3.25 - - 15.98 1.99 - - 13.48 3.71 - - 11.54 -1.11 - -

500 17.28 3.50 17.23 9.43 16.11 2.08 19.17 5.29 13.42 4.32 13.25 5.51 11.98 -1.54 14.87 -3.36

550 17.42 3.75 - - 16.25 2.13 - - 13.38 4.84 - - 12.37 -2.00 - -

600 17.50 3.97 - - 16.37 2.24 - - 13.29 5.47 - - 12.69 -2.35 - -

650 17.56 4.18 - - 16.50 2.36 - - 12.89 6.18 - - 12.95 -2.65 - -

700 17.69 4.45 17.41 9.42 16.68 2.52 19.19 5.45 13.40 6.54 12.86 5.08 13.16 -2.89 15.23 -4.37

750 17.82 4.76 - - 16.89 2.70 - - 12.50 6.36 - - 13.32 -3.11 - -

800 17.97 5.13 - - 17.02 2.89 - - 10.13 4.24 - - 13.58 -3.39 - -

850 18.13 5.47 - - 17.21 3.12 - - 11.25 5.56 - - 13.52 -3.50 - -

900 18.28 5.87 - - 17.40 3.40 - - 10.43 5.49 - - 13.53 -3.69 - -

950 18.40 6.29 17.51 9.35 17.57 3.65 19.50 5.42 10.00 4.98 11.92 4.33 13.26 -4.04 15.45 -5.59

1000 18.50 6.67 - - 17.75 3.80 - - 9.57 5.04 - - 13.38 -5.19 - -

1050 18.32 7.13 - - 17.91 3.89 - - 9.20 5.06 - - 13.47 -4.87 - -

1100 17.91 7.27 - - 18.02 4.01 - - 8.60 4.27 - - 13.30 -5.74 - -

1150 17.81 7.83 - - 18.11 4.21 - - 8.37 2.72 - - 15.47 -3.79 - -

1200 17.81 8.20 - - 18.13 4.38 - - 6.97 1.69 - - 12.29 -5.96 - -

1263 - - 17.27 9.17 - - 19.64 5.13 - - 10.97 3.30 - - 15.20 -6.91

Hovsore_tall_wind_profile.tex;

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p.24


Table VI. Similar to Table IV but for Cases 5–8

Case 5 Case 6 Case 7 Case 8

z u∗ z/L u∗ z/L u∗ z/L u∗ z/L

[m] [m s−1] [-] [m s−1] [-] [m s−1] [-] [m s−1] [-]

10 0.70 -0.005 0.62 -0.021 0.56 0.008 0.38 0.027

40 0.67 -0.012 0.77 -0.020 0.54 0.030 0.40 -0.012

100 0.85 0.006 0.79 -0.032 0.58 0.009 0.38 0.170

only. The observed u and U approach the simulated geostrophic value482

at zi. At 966 m the simulated geostrophic wind angle is –5◦, whereas483

the observations show an angle of 12◦ at 950 m (there is nearly no484

geostrophic wind turning from the simulations).485

G966

U10

U950

G14S

N

EW

10

20

40

100

200

400

800

1200

0 5 10 15 20

height[m

]

u [m s−1]

10

20

40

100

200

400

800

1200

−5 0 5 10 15

v [m s−1]

10

20

40

100

200

400

800

1200

0 5 10 15 20

U [m s−1]


Close to the surface the observed conditions are very unstable and486

as the atmosphere is nearly barotropic, the observed wind turning487



is small, as expected. The observed friction velocity at 10 m is low488

(u∗ = 0.26 m s−1) and increases with height (Table VIII). The sim-489

ulated winds are in good agreement with the observations and it is490

noticed that they clearly depart from the simulated geostrophic wind,491

as the observations, for the v component and, thus, they also show wind492

veering.493

4.2.10. Case 10494

The highly baroclinic atmosphere clearly influences both u and v (Fig. 15).495

Both observed u and v approach well the simulated geostrophic wind496

close to zi, which is simulated at 971 m. At about 250 m the observed497

U value clearly overtakes the simulated gradient wind and continues498

accelerating with the simulated geostrophic wind. The observed wind499

only veers in the first 100 m and then slowly backs upwards. The500

observed wind angle is –8◦ at 1000 m and the simulated geostrophic501

wind angle is –7◦ at 966 m. The observed wind direction at 10 m is 37◦;502

thus the thermal wind is due to the a large positive air temperature503

gradient eastwards.504

Within the first 100 m, the observed stability conditions are stable.505

The observed friction velocity is rather low and constant with height506

(u∗ = 0.26 m s−1 at 10 m) (Table VIII). The behaviour of the simulated507

winds agree reasonable well with the observations, underestimating u508

after the baroclinic component accelerates the flow. v is simulated to509

point south-easterly all the way the ABL, whereas the observed v points510

north-westerly up to 600 m where it turns south-westerly upwards.511

5. Summary and conclusions512

Accurate observations of the two horizontal wind speed components513

in the entire ABL were performed over flat terrain with nearly ho-514

mogeneous upstream flow under different surface stability and forcing515

conditions. The measurements were carried out combining a long-range516

wind lidar with sonic anemometers. Simulations using the WRF model517

are used to infer the forcing conditions (surface geostrophic, gradient,518

and thermal winds) and to help categorizing the observations. Wind519

outputs from the simulations are also compared to the measurements.520

In the ten different cases here shown, the observed surface winds be-521

have in correspondence to the surface atmospheric stability conditions;522

the vertical wind shear and turning of the wind are higher in stable com-523

pared to unstable conditions. Close to the simulated boundary-layer524

height, the observed wind components approach well the simulated525

geostrophic winds under both barotropic and baroclinic conditions,526



Table VII. Similar to Table III but for Cases 9 and 10

Case 9 (6): zi ≈ 1064 m Case 10 (3): zi ≈ 971 m

z u v uG vG u v uG vG

[m] [m s−1] [m s−1]

10 4.36 0.00 5.40 -0.30 5.19 0.00 11.72 1.44

40 4.63 0.00 5.36 -0.30 6.74 0.10 11.84 1.36

100 4.91 0.14 5.23 -0.30 8.48 0.75 12.18 1.15

150 5.12 0.46 5.16 -0.30 9.46 0.49 12.34 1.05

200 5.16 0.66 5.00 -0.30 10.45 0.43 12.67 0.84

250 5.05 0.80 - - 10.76 0.72 - -

300 5.15 0.92 4.71 -0.30 12.04 0.43 13.30 0.43

350 5.14 0.98 - - 12.48 0.32 - -

400 5.09 1.03 - - 12.84 0.34 - -

450 5.00 1.17 - - 13.15 0.30 - -

500 5.00 1.23 4.30 -0.30 13.44 0.26 14.18 -0.17

550 4.92 1.23 - - 13.70 0.26 - -

600 4.82 1.24 - - 13.95 0.15 - -

650 4.85 1.29 - - 14.33 -0.53 - -

700 4.88 1.30 4.06 -0.33 14.49 -0.74 15.18 -0.97

750 4.92 1.26 - - 14.54 -1.02 - -

800 4.95 1.19 - - 14.67 -1.36 - -

850 4.97 1.12 - - 14.74 -1.59 - -

900 5.01 1.05 - - 14.75 -1.88 - -

950 5.06 1.07 4.23 -0.38 14.75 -2.04 16.01 -2.07

1000 5.03 1.17 - - 14.63 -1.96 - -

1050 4.94 1.32 - - 14.68 -2.15 - -

1100 4.91 1.41 - - 14.65 -2.18 - -

1150 4.79 1.30 - - 14.63 -2.15 - -

1200 4.90 1.41 - - 14.59 -2.00 - -

1263 - - 4.58 -0.16 - - 16.60 -3.39

Table VIII. Similar to Table IV but forCases 9 and 10

Case 9 Case 10

z u∗ z/L u∗ z/L

[m] [m s−1] [-] [m s−1] [-]

10 0.26 -0.401 0.26 0.141

40 0.24 -1.148 0.29 0.155

100 0.37 -0.802 0.28 0.491



G966

G14

U1000

U10

W

N

S

E

10

20

40

100

200

400

800

1200

0 5 10 15 20

height[m

]

u [m s−1]

10

20

40

100

200

400

800

1200

−5 0 5 10 15

v [m s−1]

10

20

40

100

200

400

800

1200

0 5 10 15 20

U [m s−1]


except for a LLJ case, where the wind speed maximum is located527

close to the boundary-layer height (the approach to geostrophic speeds528

occurs much higher in this particular case). Forcing conditions from529

the simulations are therefore useful to understand the wind behaviour530

high above the surface layer.531

Also for all cases and at a height close to the simulated boundary-532

layer height, both simulated geostrophic wind and observed wind angles533

are in very good agreement under both backing and veering conditions.534

This improves the confidence in the simulated forcing and boundary-535

layer height. The simulated horizontal wind speed magnitude agrees536



reasonable well with the observations, although the simulations gen-537

erally underpredict the turning of the wind, and in the LLJ case, the538

strength of the wind.539

Acknowledgements540

Funding from the Danish Council for Strategic Research Project Num-541

ber 2104-08-0025 “Tall Wind” project is acknowledged. We would also542

like to thank the Test and Measurements section of DTU Wind Energy543

for the maintenance of the Høvsøre database.544

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