The Høvsøre tall wind profile experiment a description of wind profile observations in the atmospheric boundary layer

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1 1 2 3 The Høvsøre tall wind profile experiment a description of wind profile observations in the atmospheric boundary layer Alfredo Peña (aldi@dtu.dk), Rogier Floors and Sven-Erik Gryning DTU Wind Energy, Risø Campus, Technical University of Denmark, Frederiksborgvej 399, 00 Roskilde, Denmark Abstract. We present an analysis of data from a nearly one-year measurement campaign performed at Høvsøre, Denmark. Høvsøre is a coastal farmland area, where the terrain is flat. Within the easterly sector upstream of the site, the terrain is nearly homogenous. This topography and conditions provide a good basis for the analysis of vertical wind speed profiles under a wide range of atmospheric stability, turbulence, and forcing conditions. One of the objectives of the campaign was to serve as a benchmark for flow over flat terrain models. The observations consist of combined wind lidar and sonic anemometer measurements at a meteorological mast. The sonic measurements cover the first m and the wind lidar started measuring at m every 50 m in the vertical. Results of the analysis of the observations of the horizontal wind speed components in the range 1 m and surface turbulence fluxes are illustrated in detail combined with forcing conditions derived from mesoscale model simulations. Ten different cases are here presented. The observed wind profiles approach well the simulated gradient and geostrophic winds close to the simulated boundary-layer height during both barotropic and baroclinic conditions, respectively, except for a low-level jet case as expected. The simulated winds are also presented for completeness and show good agreement with the measurements, generally underpredicting the turning of the wind in both barotropic and baroclinic cases. Keywords: Atmospheric boundary layer, Baroclinity, Geostrophic wind, Sonic measurements, Turbulence fluxes, Wind lidar, Wind profile Introduction There are several type of models for the prediction of the wind and its related parameters in the atmospheric boundary layer (ABL) ranging from the mesoscale, e.g. the advanced Weather Research and Forecasting (WRF) model (Skamarock et al., 8), to the microscale, e.g. the Wind Atlas Analysis and Application Program (WAsP) model (Mortensen et al., 7). Particularly, the microscale models have been developed to improve the wind predictions over complex, forested, and heterogeneous terrain. However, these assume near-neutral stability and barotropic conditions in most cases, which might be far from the actual conditions at many sites where wind predictions are important, c 13 Kluwer Academic Publishers. Printed in the Netherlands. Hovsore_tall_wind_profile.tex; 30/07/13; 13:18; p.1

2 2 Alfredo Peña, Rogier Floors and Sven-Erik Gryning and thus they become highly uncertain, especially above the surface layer. Understanding of the change of the horizontal wind velocity components with height and, therefore, of the turning of the wind in the ABL under different surface, wind, and forcing conditions is essential for the parametrization of atmospheric processes and the improvement of both microscale and mesoscale models, e.g. via the planetary boundary layer (PBL) parametrizations. This understanding can be partly achieved through the analysis of wind measurements in the ABL. Observations of the wind velocity components in the ABL are however scarce and in many cases controversial as the instrumentations used in the experiments were inaccurate (radiosondes and similar techniques in most cases), the assumptions for the analysis of the measurements were too simplistic, and the surface and forcing conditions were not always measured. To the authors knowledge, there are only three experiments particularly designed for the analysis of the ABL winds. The first is the Leipzig experiment, initially described by Mildner (1932), which has been used extensively as a benchmark for numerical and analytical flow models. The data commonly used are actually the results from the reanalysis performed by Lettau (1950), who assumed a neutral and barotropic atmosphere to reconstruct the vertical profiles of wind and turbulent exchange coefficient, although the conditions were probably stable and the upwind flow inhomogeneous(riopelle and Stubley, 1988; Bergmann, 6). There is also controversy on the surface roughness and the boundary-layer height values of the experiment (Lettau, 1962; Hess, 4). The second is the O Neill experiment, performed in Nebraska and designed by Lettau (Lettau and Davidson, 1957), who tried to avoid some of the problems inherent to the Leipzig experiment such as terrain heterogeneity and unknown atmospheric stability conditions. The ABL winds were however measured using both balloons, radiosondes, and airplanes, although Lettau already anticipated that ground-based methods were needed for accurate measurements (Lettau, 1990). This experiment is not popular among modellers. The last is the Wangara experiment, performed in Australia in 1967 (Clarke et al., 1971). Hourly double-theodolite observations of pilot balloons were performed at different stations over days under different surface, stability, and forcing conditions. However, as pointed out by Clarke and Hess (1974), thermal winds were not accurately estimated, and the surface friction velocity and heat flux had to be indirectly estimated from a drag-coefficient method and wind and temperature profiles, respectively. Hovsore_tall_wind_profile.tex; 30/07/13; 13:18; p.2

3 The Høvsøre tall wind profile experiment In wind energy, there is now an understanding of the importance of accurate wind-speed measurements and the observation of the vertical wind shear for wind and load predictions. However, there is very little knowledge about the influence of (and the mechanisms controlling) the turning of the wind, the boundary-layer height, and baroclinity on wind turbines and structures. The Tall Wind Profile experiment, performed at a nearly flat and homogenous area in Denmark, observed ABL winds quasi-continuously during one year combining a wind lidar and meteorological (met) mast measurements to attempt to respond to the late challenges brought by the wind community. Here, we present ten cases (out of a much larger dataset), where the ABL winds were accurately measured up to about 0 m and the forcing and winds were simulated with a mesoscale model. We first provide some definitions (Section 2) useful for the interpretation of the observations. We then describe the site, the measurements, and the modelling in Section 3. The data analysis is explained in Section 4 and illustrates the cases explored in this study. Summary and conclusions are provided in the last section Definitions The three wind speed components (u,v,w) are here placed on a lefthandedcoordinatesystem (see Fig. 1), beinguandv thehorizontal and w the vertical wind speed components (the latter aligned with the z- axis, i.e. the height above the ground). In this fashion, by aligning u at thesurface(as showninsection 3this meansat a-m height) withthe horizontal wind speed vector (i.e. with the wind direction), v is zero at the surface increasing when the wind vector turns clockwise with height (veering wind) and decreasing when it turns counterclockwise with height (backing wind). The horizontal wind speed magnitude U at any height is then computed as U = ( u 2 +v 2) 1/2. (1) 1 The friction velocity u is defined as u 2 = (u w 2 +v w 2) 1/2, (2) where the overbar indicates a time average and the primes fluctuations over the average. The Obukhov length L is defined as L = T v u 3, (3) κ g w T v Hovsore_tall_wind_profile.tex; 30/07/13; 13:18; p.3

4 4 Alfredo Peña, Rogier Floors and Sven-Erik Gryning v y N. θ U W x E u Figure 1. Coordinate system used for the wind profile and the estimation of the surface geostrophic and thermal winds. U is the horizontal wind speed vector at the surface and θ the wind direction from the geographical north S where T v is the virtual temperature, κ the von Kármán constant (here we use the value 0.4), g the gravitational acceleration, and w T v the virtual kinematic heat flux. In the surface layer and under neutral atmospheric conditions, the logarithmic wind profile U = u ( ) z κ ln, (4) z o 118 where z o is the roughness length, is commonly used to predict the wind 119 speed over flat, non-forested, and homogenous terrain without taking 1 into account its turning with height. 121 We derive the surface geostrophic, the gradient, and the total 122 geostrophic wind (the latter known here simply as geostrophic wind) 123 from the WRF simulations (explained in Section 3). The gradient wind 124 takes into account the curvature of the isobars in the surface geostrophic 125 wind. For their derivation, we define their two components oriented 126 with the x and y axis, eastwards and northwards, respectively (see 127 Fig. 1). Thetwo components of thesurface geostrophic winds ( ) 128 G ox,g oy are 129 then given as, G ox = 1 P o ρ f c y G oy = 1 P o ρ f c x, and (5a) (5b) Hovsore_tall_wind_profile.tex; 30/07/13; 13:18; p.4

5 The Høvsøre tall wind profile experiment where ρ is the air density, f c the Coriolis parameter, and P o the mean sea level pressure. The baroclinic effect on the two components of the geostrophic wind (G x,g y ) is derived as (Holton and Hakim, 4), G Tx = 1 f c Φ z Φ o y and (6a) G Ty = 1 Φ z Φ o, (6b) f c x where Φ is the geopotential, and the subindexes z and o refer to a given height and the surface, respectively. The magnitude of the gradient wind G g is a function of the magnitude of the surface geostrophic wind G o = G 2 o x +G 2 o y (Kristensen and Jensen, 1999) G o f c R ( Gg G o ) 2 + G g G o 1 = 0, (7) 137 where R is the radius of the curvature of the isobars. The two components of the gradient wind ( ) 138 G gx,g gy are computed assuming that the 139 angle between them is equal to that between G ox and G oy. 1 The geostrophic wind components are thus computed as G x = G gx + z G Tx and G y = G gy + z G Ty, (8a) (8b) where z is the difference between a given height and the surface (the z and o levels in Eqs. (6a) and (6b)). u G and v G hereafter refer to G x and G y when rotated into the u-v coordinate system (Fig. 1). The magnitude of the geostrophic wind is given as U G = u 2 G +v2 G Site, measurements, and WRF modelling 3.1. Site The measurements were performed at the National Test Station for Wind Turbines located in a coastal area known as Høvsøre in west Jutland, Denmark (Fig. 2). Høvsøre is a flat farmland area, which is fairly homogeneous with disturbances on the flow from the presence of the North Sea and the Nissum Fjord, 1.7 km west and 950 m south, respectively, some scattered trees, houses, and crop patches east, the village of Bøvlingbjerg 3 km south-east, and five wind turbines north of a meteorological mast placed south of the station at a height of 2 m above mean sea level. Hovsore_tall_wind_profile.tex; 30/07/13; 13:18; p.5

6 6 Alfredo Peña, Rogier Floors and Sven-Erik Gryning North Sea 30 1 km Meteorological mast Nissum Fjord 1 Figure 2. National Test Station for Wind Turbines at Høvsøre, Denmark. The meteorological mast location (circle) and the analyzed sector are also shown. The image was taken from Google Earth Easterlywinds(withintherange30 1 )arethereforenearlyideal for the study of flow over flat and homogeneous terrain. Thus, we focus our analysis to these directions, although winds within this range are not the predominant ones at Høvsøre (Peña, 9) Measurements The measurements come from two different types of instrumentation: sonic anemometers at the meteorological mast and a wind lidar system Sonic measurements Metek USA-1 scientific sonic anemometers are placed at,,, 60, 80, and m on the booms facing north of the meteorological mast. Thus, for easterly winds the mast effect on the sonic measurements is negligible. Other details about the mast and its instrumentation can be found in Jørgensen et al. (8). The recording frequency of the sonic time series is Hz Wind lidar A WLS70 WindCube, a pulsed wind lidar from the company Leosphere, was installed m south-west from the meteorological mast during the period April March 11. The WLS70 measures the radial velocity at four azimuthal positions separated 90 in the horizontal plane with an inclination of 15 from the zenith and derives the three wind-speed components assuming horizontal flow homogeneity. The wavelength, the pulse length, and pulse energy of the laser are 1.5 µm, 0 ns, and µj, respectively. Under good aerosol conditions, the Hovsore_tall_wind_profile.tex; 30/07/13; 13:18; p.6

7 The Høvsøre tall wind profile experiment system reports measurements up to 2 km. Recorded data depend on the instrument s threshold for the carrier-to-noise ratio (CNR), which was left equal to the default value ( 35 db). The instrument measures from a height of m every 50 m at a rate of s for each azimuthal position. The measurement volume at each azimuthal position and height extends 60 m radially N 0 50 vl z [m] 300 y [m] 0 50 W ul E 0 N y [m] 0 W E S 0 x [m] 150 S 0 x [m] 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, and the four azimuthal radial positions in black, cyan, blue, and red colours As shown in Fig. 3, the wind lidar was positioned with an offset of 50 from the north in order to avoid influence of the mast on the eastern azimuthal position shown in blue. This is important as one wind lidar wind-speed component, v l, is related to the difference between the eastern and western radial velocities (in this case indicated by the red and blue points) WRF modelling We use outputs of simulations using the WRF model version 3.4 (Skamarock et al., 8). Two domains are used with horizontal resolutions of 18 and 6 km as shown in Fig. 4. The model was run in analysis mode every days starting at 0000 UTC during April April 11 (see details in Gryning et al., 13b). Initial and boundary conditions came from the National Center for Environmental Prediction (NCEP) final analysis data and real-time global sea-surface temperature analysis from NCEP. Model outputs from 24 to 264 h were used to generate continuous time series of - min resolution (24-h spin-up period). Timesteps were 1 and s for the outermost and innermost domains, respectively. The first 11 vertical Hovsore_tall_wind_profile.tex; 30/07/13; 13:18; p.7

8 8 Alfredo Peña, Rogier Floors and Sven-Erik Gryning Figure 4. Model outermost (D1) and innermost domains (D2) used for the simulations (black lines). The red square shows the area used for the derivation of the pressure and geopotential gradients model levels are approx. at 14, 43, 72, 1, 130, 191, 311, 485, 705, 966, and 1263 m. The first-order Yonsei University (YSU) PBL scheme is used (Hong et al., 6). The boundary-layer height is estimated in the PBL parametrization as a function of the ratio of the critical to the bulk Richardson number at the boundary-layer top. The model was nudged in the outermost domain above the 11th model level towards the analysis data. Other details about the model setup can be found in Gryning et al. (13a) Data analysis The WLS70 system provides two datasets: a fast one with the s measurements at each height and azimuthal position and a slow one with outputs for the same parameters but based on -min averages. The sonic time series of the wind velocity and temperature were recorded on a third dataset. Here we provide a description of the filtering and selecting criteria we use on the three datasets to produce the final output with wind speed profiles from m up to 0 m and sonic turbulence measurements from m up to m averaged in 30-min periods. The first part of the analysis is performed on the slow wind lidar dataset. We extract -min data from up to 600 m to increase the number of vertical profiles for the study. We also apply a filter so that each -min profile shows measurements every 50 m with a minimum meancnrof 22dB,usedbyFloorsetal.(13) andpeñaetal.(13) for accurate wind speed measurements, and with % availability (this Hovsore_tall_wind_profile.tex; 30/07/13; 13:18; p.8

9 The Høvsøre tall wind profile experiment isanumberrelatingtheamount of srecordswherecnr > 35dB within the -min period). We then exclude non-easterly winds by looking at the wind direction at m only. Since the final analysis is performed in 30-min averages, we extract data where three consecutive -min intervals are found. We also apply a stationarity check based on the criteria by Lange et al. (4), where the ratio between any of the three consecutive -min mean values of U and wind direction is limited to the interval [0.8,1.2]. This is mostly done to avoid large differences between the three consecutive -min periods. We extract the wind lidar fast time series correspondent to those 30-min periods. Statistics of all wind lidar parameters are performed based on the 30-min periods. Here we choose to present those related to the mean wind speed components only, since turbulence quantities are highly influenced by the wind lidar s measurement volume and scanning pattern. The over/underprediction of the wind lidar turbulence (compared to that of a sonic measurement) depends on the turbulence spectral tensor, the observed height, and atmospheric stability, among others (Sathe et al., 11; Sathe and Mann, 12). We then extract the concurrent sonic time series; these are linearly detrended over the 30-min period. u and L are estimated from the sonics; here we assume that the sonic temperature and kinematic heat flux are good estimates of T v and w T v in Eq. (3). We use the crosswind corrections in Liu et al. (1) for w T v. However, the sonics did not continuously operate during the wind lidar campaign. We therefore have to make a compromise between data amount and number of sonic levels; we leave out of the analysis the sonics at, 60, and 80 m. The total number of combined sonic/wind lidar 30-min observations is 371 (we also check that on each wind lidar 30-min time series, there are at least 160 of the ideal 180 records). Table I shows a summary of the results of the filtering criteria. We extract the -min instantaneous outputs from the WRF simulations correspondent to the time stamps of the combined sonic/wind lidar observations and average them in 30 min means. The gradients of geopotential and mean sea level pressure in Eqs. (5a) (6b) are computed from these outputs over a 300 km square around the meteorological mast (see Fig. 4). Linear regression is applied to the northward and eastward gradient of the mean sea level pressure field. Similarly, the gradient of the geopotential difference in Eqs. (6a) and (6b) is computed. The gradient of the geopotential difference between any given model level and the first one is computed using linear regression; this results in the baroclinic term at each level that is added to the gradient wind. We estimate the gradient wind in Eq. (7) assuming Hovsore_tall_wind_profile.tex; 30/07/13; 13:18; p.9

10 Alfredo Peña, Rogier Floors and Sven-Erik Gryning Table I. Results of the analysis of measurements after the filtering and selecting criteria described in Sec. 4. The total number of potential -min measurements for the period of the wind lidar campaign at Høvsøre is The wind lidar reports min mean measurements at least at one height Filtering criteria applied Number of samples/profiles left Wind lidar measurements at each height min in the range 600 m after availability and CNR criteria Easterly winds only (30 1 ) 2592 min Three consecutive -min periods 1857 min Stationarity criterion 1713 min ( min) Sonic availability (, and m) min that in a given area the pressure field can be described by the surface (Kristensen and Jensen, 1999), P(x,y) = P r +P x x+p y y +0.5 ( P xx x 2 +P xy xy +P yy y 2), (9) where P r is a reference pressure and P x, P xx, P xy, P y, and P yy are the first and second derivatives with respect to x and y, respectively. The curvature can then be estimated using this field as (Kristensen and Jensen, 1999) ( P 2 x +P 2 3/2 y) R = P yy Px 2 2P xyp x P y +P xx Py 2. () The curvature of the isobars is estimated using the algorithm described in Shary (1995), which fits Eq. (9) to a 3 by 3 grid in a least-squares sense. A mean curvature is then computed for all grid points in the area of interest around the site Wind lidar accuracy The wind lidar and the sonic measurements overlap at the -m height. Figure 5 illustrates a comparison of the total min observations of U and direction from the -m sonic and wind lidar. As shown both wind speed and direction measurements show a very high correlation, although the techniques and the measurement volumes are different. The sonic shows a slightly higher wind speed compared to the wind lidar particularly within the high wind speed range. For the wind profile analysis, we scale/adjust the wind lidar wind speed measurements at all heights (of both components) so that the -m wind lidar matches Hovsore_tall_wind_profile.tex; 30/07/13; 13:18; p.

11 The Høvsøre tall wind profile experiment the sonic observations at m. The wind lidar shows an offset of 4 with the sonic wind direction and as with the wind speed, we correct/adjust all wind lidar directions so that in the wind profile analysis both show the same wind direction at m lidar wind speed [m s 1 ] y = 0.95x y = 0.93x R 2 = 0.98 N = 371 lidar wind direction [deg.] y = 0.92x y = 0.95x 2.0 R 2 = 0.99 N = sonic wind speed [m s 1 ] sonic wind direction [deg.] Figure 5. Comparison of the sonic anemometer and the wind lidar 30-min measurements at 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 solid line in black. The results of a linear regression through origin, a linear regression with offset, the Pearson s linear correlation coefficient (R 2 ), and the number of observations (N) are also given Boundary-layer wind profiles Here we describe and illustrate some cases (ensemble means) that can be studied and further modelled based on the combined sonic/wind lidar data and the simulations (hereafter all WRF simulations are referred to as simulations). They are selected because they show particular wind speed and turning of the wind situations, and a variety of surface and simulated forcing conditions. Table II provides a summary of the ten selected cases. For each case the ensemble mean of 30-min averages of observed vertical profiles of u, v, and U from up to 600 m is illustrated. As mentioned before, we aligned u at m with U at m (so v at m is always 0 m s 1 ). The turning of the wind can therefore be estimated from the angle between v and u (see Fig. 1). Themedianofthesimulatedboundary-layerheight(z i )isalsoshown. Because in some cases z i is between 600 and 1 m, we also illustrate the ensemble mean of the concurrent -min wind lidar profiles from 650 up to 1 m. These are shown in a different colour because they might be more uncertain than the measurements below. This is due Hovsore_tall_wind_profile.tex; 30/07/13; 13:18; p.11

12 12 Alfredo Peña, Rogier Floors and Sven-Erik Gryning Table II. Summary of Tall wind profile cases. All cases represent observations performed in Case Observation period (local time) Description 1 May 5, Very stable surface conditions, high wind veering, and low forcing 2 June 9, Neutral to stable surface conditions and low boundary-layer height 3 Apr , Low-level jet and highly baroclinic atmosphere 4 Sep. 8, Stable surface conditions and high forcing Sep. 8 9, Sep. 7, Neutral surface conditions and barotropic Sep. 8, atmosphere Sep. 9, May 7, Slightly unstable surface conditions, high forcing, and nearly barotropic atmosphere 7 Sep. 27, Neutral surface conditions and baroclinic atmosphere 8 Nov. 24, Slightly stable surface conditions and highly baroclinic atmosphere 9 Sep. 25, Very unstable surface conditions and low forcing Dec. 12, Stable surface conditions and highly baroclinic atmosphere to their generally low CNR (it might be lower than 22 db) and that the amount of concurrent measurements might decrease with height (neither the CNR nor the availability criteria are applied to these retrievals). Together with the observed profiles, the simulated gradient and geostrophic winds and simulated wind outputs are shown. They are also rotated using the observed wind direction at m. The prediction of U from the log profile, Eq. (4), using the observed u value at m and z o = m (Peña et al., b; Peña et al., a), up to z i is also illustrated. In Tables III VIII, the ensemble mean of the measured and simulated parameters for all the discussed cases are given Case 1 Here, the wind lidar observed some of the largest values of turning of the wind: the wind veers 43 and 66 in the first and m, respectively, and backs slightly above this. This is due to a small baro- Hovsore_tall_wind_profile.tex; 30/07/13; 13:18; p.12

13 The Høvsøre tall wind profile experiment clinic component clearly observed in v (see Fig. 6, where both v and U decrease above m both in the observations and the simulated wind and geostrophic components). The observed wind direction at m is 66, so this small baroclinic component decelerates the flow due to the colder air over land compared to the warmer air over sea. U G G 14 N W E U S height [m] u [m s 1 ] v [m s 1 ] U [m s 1 ] Figure 6. Observed (black circles) and simulated (green lines) mean vertical profiles ofu,v,andu forcase1.theerrorbarsrepresent±onestandarddeviation.observed profiles from 650 up to 1 m are shown in cyan circles. The simulated z i value is shown in the dashed grey line. The gradient and the geostrophic winds are shown in blue and red lines, respectively. The prediction using the log profile is also illustrated in thegrey solid line. Observedhorizontal wind speed vectors at m andat aheight close to the simulated z i, and simulated geostrophic winds at the first model level ( 14 m) and at a height close to the simulated z i are shown above the profiles Close to the surface the observed conditions are very stable and the stability increases with height as shown by the z/l values in Table IV. Hovsore_tall_wind_profile.tex; 30/07/13; 13:18; p.13

14 14 Alfredo Peña, Rogier Floors and Sven-Erik Gryning The observed friction velocity at m is very low (u = 0.19 m s 1 ) and decreases linearly with height (the observed surface winds and simulated forcing are rather low). The simulated z i is 87 m; the closest model level to the z i value (i.e. 1 m) shows a geostrophic wind angle (i.e. that between the simulated geostrophic wind and the observed wind at m) of 42, which is very close to the observed turning of the wind. Although the simulated winds agree reasonable well with the behaviour of both observed u and v (for such a complicated modelling scenario), the turning of the wind is highly underestimated; it is and 31 at 1 and 191 m, respectively Case 2 The simulated z i is relatively low (169 m), and the observed profiles of u, v, and U show a wind maximum at 350 m and rapidly decrease upwards (Fig. 7). This might be due to wind deceleration due to baroclinity (particularly for u). The observed wind direction at m is 69, so u decelerates due to the warmer air south compared to that north of Høvsøre. Both observed u and U approach the simulated geostrophic wind high above z i. The observed wind veers 8 and 26 in the first and 250 m, respectively, agreeing with the simulated geostrophic wind angle (28 at 191 m). Thus z i is probably between the simulated value and 250 m. Near the surface the observed atmospheric conditions are close to neutral and become slightly stable with height(table IV). The observed friction velocity at m is 0.37 m s 1 and is relatively constant with height. The wind speeds are not as low as in Case 1 and the log profile predictions are much closer to the observations. The simulated winds are in very good agrement with the observed v component, but highly underpredict the observations of u, and thus, U Case 3 Here, a low-level jet (LLJ) is clearly observed (Fig. 8). The observed U at m is not very high (6.33 m s 1 ), but it reaches.11 m s 1 at 0 m, which is the wind speed maximum, and that height is similar to the simulated z i (351 m). The simulated geostrophic wind angle is 58 at 485 m and the observed wind veers 11 and 61 at and 500 m, respectively. The wind is highly ageostrophic (the observed wind around the wind maximum is much higher than the geostrophic wind), as expected. Baroclinity has a high impact on the observations and simulated geostrophic wind (on the latter already below the simulated z i ). The observed -m wind direction is 7 so that the high baroclinity component in u is due to positive temperature Hovsore_tall_wind_profile.tex; 30/07/13; 13:18; p.14

15 The Høvsøre tall wind profile experiment 15 G 14 U 250 G 191 U W N S E height [m] u [m s 1 ] Figure 7. Similar to Fig. 6 but for case v [m s 1 ] U [m s 1 ] difference northwards. At about 1 m the observed winds approach the simulated geostrophic values. The observed atmospheric conditions close to the surface are stable and L is nearly the same at all sonic levels. The observed friction velocity at m is 0.38 m s 1 and decreases slightly with height (Table IV). Although the simulated winds behave similarly compared to the observed u and v components, the strength of the LLJ is underestimated as found by Floors et al. (13) Case 4 The conditions as seen by the simulations are nearly barotropic and the strength of the forcing is very high (U G = m s 1 at the first modellevel). Thesimulated z i (763 m) correspondswell withtheheight where the observed U shows its maximum (Fig. 9). The observed U is high at all levels: and m s 1 at and 600 m, respectively. Close to the simulated z i (at 705 m), the simulated geostrophic wind Hovsore_tall_wind_profile.tex; 30/07/13; 13:18; p.15

16 16 Alfredo Peña, Rogier Floors and Sven-Erik Gryning U 500 G 485 G 14 U N W E S height [m] u [m s 1 ] Figure 8. Similar to Fig. 6 but for case v [m s 1 ] U [m s 1 ] angle is 45 ; the observed veering is 7 and 45 in the first and 700 m, respectively. The observed u and v components approach well the simulated geostrophic wind close to z i. Near the surface the observed atmospheric conditions are stable and remain nearly constant in the first m. The observed friction velocity at m is 0.45 m s 1 (the observed wind direction at m is 84 ) and also remains constant within the sonics range (Table IV). The simulated winds are in good agreement with the observed U (v Hovsore_tall_wind_profile.tex; 30/07/13; 13:18; p.16

17 The Høvsøre tall wind profile experiment 17 G 705 G 14 U 700 N U W E S height [m] u [m s 1 ] Figure 9. Similar to Fig. 6 but for case v [m s 1 ] U [m s 1 ] is slightly overpredicted) and the highest differences are found close to and above z i Case 5 Very high wind speeds are observed close to the surface (U =.04 and m s 1 at and m, respectively) and the conditions, as seen by the simulations, are nearly barotropic with high forcing (U G = m s 1 at the first model level). The simulated z i (11 m) matches well the height of the observed wind speed maximum (Fig. ). Thesimulatedgeostrophicwindangleis28 at 1263mandtheobserved wind veers 4 and 25 at and 1 m, respectively (the observed Hovsore_tall_wind_profile.tex; 30/07/13; 13:18; p.17

18 Hovsore_tall_wind_profile.tex; 30/07/13; 13:18; p.18 Table III. Observed mean horizontal wind-speed components (u, v) at different heights z. The simulated geostrophic wind components (u G,v G) are also given at different levels (between and 950 m the closest model level output to an observed height is used). The results are provided for Cases 1 4, where the number in parenthesis indicates the number of 30-min averages used for the ensemble mean and z i indicates the boundary-layer height from the model simulations Case 1 (3): z i 87 m Case 2 (3): z i 169 m Case 3 (7): z i 351 m Case 4 (23): z i 763 m z u v u G v G u v u G v G u v u G v G u v u G v G [m] [m s 1 ] [m s 1 ] [m s 1 ] [m s 1 ] Alfredo Peña, Rogier Floors and Sven-Erik Gryning

19 The Høvsøre tall wind profile experiment 19 Table IV. Observed mean friction velocity u and dimensionless stability parameter 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 ] [-] wind direction at m is ). The observed wind-speed components approach well the simulated geostrophic values. The rather large error bars in Fig. show the high wind variability observed during the afternoon hours of these three consecutive days. Within the first m, the observed stability conditions are highly neutral (z/l values close to zero). The observed friction velocity at m is rather high (0.70 m s 1 ) and increases to 0.85 m s 1 at m (Table VI). The observed U is well predicted by the log profile within the entire ABL. The simulated winds are in good agreement with the observed v component, slightly underestimating u Case 6 The simulations show an atmosphere that is nearly barotropic with very highforcing conditions (U G =.47 m s 1 at the firstmodel level). The simulated z i is very high (1290 m) andalthough it is beyondthehighest observed level, the observations of both u and v seem to approach the simulated geostrophic wind at z i (Fig. 11). The observed wind speeds are also very high: U is and m s 1 at and 600 m, respectively. The observed wind direction at m is 57. The observed wind veers very little: 4 and 14 at and 1 m, respectively, while the simulated geostrophic wind angle at 1263 m is 15. Close to the surface the observed conditions are slightly unstable becoming more neutral with height. The observed friction velocity at m is 0.62 m s 1 and increases with height (Table VI). The log profile predicts well the observed U values within the ABL. The simulated winds are in very good agreement with the observations, particularly for u. The simulated wind veering is however rather low in the first hundred of metres; it veers less than 1 at 1 m. Hovsore_tall_wind_profile.tex; 30/07/13; 13:18; p.19

20 Alfredo Peña, Rogier Floors and Sven-Erik Gryning G 14 G 1263 U 1 U N W S E height [m] u [m s 1 ] Figure. Similar to Fig. 6 but for case v [m s 1 ] U [m s 1 ] Case 7 The simulations indicate that the atmosphere is baroclinic and in both observed u and v components, the flow decelerates approaching z i, which is simulated at 746 m (Fig. 12). The observed wind direction at m is 41 and so the baroclinic components on u and v are mainly due to the warmer air south of Høvsøre (compared to that north of it). Both observed wind speed components, particularly u and therefore U, show a higher deceleration compared to the simulated geostrophic wind but approach well its value at z i. The observed wind speeds are high: U is and m s 1 at and 600 m, respectively. The Hovsore_tall_wind_profile.tex; 30/07/13; 13:18; p.

21 The Høvsøre tall wind profile experiment 21 N W E S G 1263 U 1 U G height [m] u [m s 1 ] Figure 11. Similar to Fig. 6 but for case v [m s 1 ] U [m s 1 ] observed wind veers 5 and 26 at and 700 m, respectively, and the simulated geostrophic wind angle is 22 at 705 m. Within the first m the observations indicate that the atmosphere is near-neutral with a nearly constant friction velocity of u = 0.56 m s 1 at m (Table VI). The log profile agrees reasonable well with the observed U values, although it overpredicts slightly the wind speed. During summer and early autumn, the fields and crops at Høvsøre are close to be harvested and a z o value of m is perhaps low (see also the slight overprediction of the log profile on cases 2 and 5). The simulated winds agree well with the behaviour of the observed u component, but the wind veering is overpredicted in the first 250 m Case 8 The observed wind veers 4 within the first 150 m and then backs upwards; 17 at 900 m relative to the -m wind (Fig. 13). This is due Hovsore_tall_wind_profile.tex; 30/07/13; 13:18; p.21

22 22 Alfredo Peña, Rogier Floors and Sven-Erik Gryning N W S E U700 G14 G705 U height [m] u [m s 1 ] Figure 12. Similar to Fig. 6 but for case v [m s 1 ] U [m s 1 ] to a high thermal-wind component, particularly observed in v, which notoriously decelerates and becomes negative at 350 m. The observed wind direction at m is 41, thus such a high thermal wind comes from a colder north-east air over land compared to the warmer southwest air over sea. Close to the simulated z i (897 m), the observed u and v components approach the simulated geostrophic wind. The simulated geostrophic wind angle at 966 m is (agreeing with the observed backing above). The observed conditions within the first m are slightly stable and the observed friction velocity is rather constant u = 0.38 m s 1 (Table VI). The simulated wind-speed components show similar behaviour compared to the observations, although u is underpredicted above 0 m, v is overpredicted (in magnitude) the first 0 m, and the Hovsore_tall_wind_profile.tex; 30/07/13; 13:18; p.22

23 The Høvsøre tall wind profile experiment 23 W N S E U G14 U900 G height [m] u [m s 1 ] Figure 13. Similar to Fig. 6 but for case v [m s 1 ] U [m s 1 ] simulated backing is underpredicted(the wind angle from the simulated winds is 7 at 966 m) Case 9 The observed wind speeds do not change much with height within the first 1 m: U is 4.91 and 4.98 m s 1 at and 600 m, respectively (Fig. 14). There is a slight baroclinic component decelerating u close to z i, which is estimated at 64 m by the simulations. The observed wind direction at m is 91 and so this simulated eastward thermal wind is due to the higher air temperature southwards from Høvsøre. The simulated forcing is low: U G = 5.54 m s 1 at the first model level. The observed wind veers 2 and 14 at and 600 m, respectively, Hovsore_tall_wind_profile.tex; 30/07/13; 13:18; p.23

24 Hovsore_tall_wind_profile.tex; 30/07/13; 13:18; p.24 Table V. Similar to Table III but for Cases 5 8 Case 5 (19): z i 11 m Case 6 (30): z i 1290 m Case 7 (12): z i 746 m Case 8 (3): z i 897 m z u v u G v G u v u G v G u v u G v G u v u G v G [m] [m s 1 ] [m s 1 ] [m s 1 ] [m s 1 ] Alfredo Peña, Rogier Floors and Sven-Erik Gryning

25 The Høvsøre tall wind profile experiment 25 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 ] [-] only. The observed u and U approach the simulated geostrophic value at z i. At 966 m the simulated geostrophic wind angle is 5, whereas the observations show an angle of 12 at 950 m (there is nearly no geostrophic wind turning from the simulations). U 950 G 14 U G 966 N W E S height [m] u [m s 1 ] Figure 14. Similar to Fig. 6 but for case v [m s 1 ] U [m s 1 ] Close to the surface the observed conditions are very unstable and as the atmosphere is nearly barotropic, the observed wind turning Hovsore_tall_wind_profile.tex; 30/07/13; 13:18; p.25

26 26 Alfredo Peña, Rogier Floors and Sven-Erik Gryning is small, as expected. The observed friction velocity at m is low (u = 0.26 m s 1 ) and increases with height (Table VIII). The simulated winds are in good agreement with the observations and it is noticed that they clearly depart from the simulated geostrophic wind, astheobservations, forthev component and, thus,they also showwind veering Case The highly baroclinic atmosphere clearly influences both u and v (Fig. 15). Both observed u and v approach well the simulated geostrophic wind close to z i, which is simulated at 971 m. At about 250 m the observed U value clearly overtakes the simulated gradient wind and continues accelerating with the simulated geostrophic wind. The observed wind only veers in the first m and then slowly backs upwards. The observed wind angle is 8 at 0 m and the simulated geostrophic wind angle is 7 at 966 m. The observed wind direction at m is 37 ; thus the thermal wind is due to the a large positive air temperature gradient eastwards. Within the first m, the observed stability conditions are stable. The observed friction velocity is rather low and constant with height (u = 0.26 m s 1 at m) (Table VIII).Thebehaviourof thesimulated winds agree reasonable well with the observations, underestimating u after the baroclinic component accelerates the flow. v is simulated to point south-easterly all the way the ABL, whereas the observed v points north-westerly up to 600 m where it turns south-westerly upwards Summary and conclusions Accurate observations of the two horizontal wind speed components in the entire ABL were performed over flat terrain with nearly homogeneous upstream flow under different surface stability and forcing conditions. The measurements were carried out combining a long-range wind lidar with sonic anemometers. Simulations using the WRF model are used to infer the forcing conditions (surface geostrophic, gradient, and thermal winds) and to help categorizing the observations. Wind outputs from the simulations are also compared to the measurements. In the ten different cases here shown, the observed surface winds behave in correspondence to the surface atmospheric stability conditions; theverticalwindshearandturningofthewindarehigherinstablecompared to unstable conditions. Close to the simulated boundary-layer height, the observed wind components approach well the simulated geostrophic winds under both barotropic and baroclinic conditions, Hovsore_tall_wind_profile.tex; 30/07/13; 13:18; p.26

27 The Høvsøre tall wind profile experiment 27 Table VII. Similar to Table III but for Cases 9 and Case 9 (6): z i 64 m Case (3): z i 971 m z u v u G v G u v u G v G [m] [m s 1 ] [m s 1 ] Table VIII. Similar to Table IV but for Cases 9 and Case 9 Case z u z/l u z/l [m] [m s 1 ] [-] [m s 1 ] [-] Hovsore_tall_wind_profile.tex; 30/07/13; 13:18; p.27

28 28 Alfredo Peña, Rogier Floors and Sven-Erik Gryning N W S E U G 14 U 0 G height [m] u [m s 1 ] Figure 15. Similar to Fig. 6 but for case v [m s 1 ] U [m s 1 ] except for a LLJ case, where the wind speed maximum is located close to the boundary-layer height (the approach to geostrophic speeds occurs much higher in this particular case). Forcing conditions from the simulations are therefore useful to understand the wind behaviour high above the surface layer. Also for all cases and at a height close to the simulated boundarylayer height, both simulated geostrophic wind and observed wind angles are in very good agreement under both backing and veering conditions. This improves the confidence in the simulated forcing and boundarylayer height. The simulated horizontal wind speed magnitude agrees Hovsore_tall_wind_profile.tex; 30/07/13; 13:18; p.28