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z [m] 300 250 200 160 130 100 80 60 40 30 20 10 15 20 25 30 35 40 45 50 U/u∗o [-] Figure 157: Wind profiles observed for different stability classes at Høvsøre, Denmark. The markers indicate the combined lidar/cup anemometer observations, the solid lines the predictions using Eqs. (261) and (262) with d = 5/4, and the dashed lines the predictions from Eq. (250). Legend as in Fig. 153. Once η and zi are estimated, the wind speed observations can be compared to the models. Figure 157 illustrates the comparison of the models in Eqs. (261) and (262) with d = 5/4, the surface-layer wind profile, Eq. (250), and the wind speed observations for the number of stability classes also used in Figures 153 and 154. As with the neutral observations, surfacelayer scaling fits well the observations within the surface layer only. The wind profile model, which limits the value of the length scale, corrects for the departures of the observations beyond the surface layer. Similar results were obtained in Peña et al. (2010a) using Eqs. (261) and (262) with d = 1 and the wind profile models in Gryning et al. (2007). 11.4 Summary • Theuseofground-basedremotesensinginstrumentshasbeenusefulforthestudyanddescription of the wind profile within and beyond the surface layer and for the improvement of the models that are traditionally used in wind power and boundary-layer meteorology. • Over flat land and homogenous terrain and over the sea, the surface-layer wind profile fits well the observations for a wide range of atmospheric stability conditions within the surface layer only. For the analysis of wind profiles over water, however, a new scaling should be added in order to account for the variable roughness length. • Wind speed observations from combined lidar/cup anemometer measurements up to 160 m AMSL at the Horns Rev offshore wind farm are well predicted by wind profile models that limit the value of the length scale, as suggested by Gryning et al. (2007), where the BLH becomes an important parameter, particularly for stable conditions. • Near-neutral wind speed observations from combined lidar/cup anemometer measurements up to 300 m AGL at Høvsøre, Denmark, departure from the logarithmic wind profile beyond the surface layer. Simple analytical models, which limit the value of the length scale, predict such departure and fit well the observations. • Wind profile models, extended for diabatic conditions, are compared to wind speed observations from combined lidar/cup anemometer measurements up to 300 m AGL at Høvsøre, Denmark, for a number of stability conditions. The models, which also limit 228 <strong>DTU</strong> Wind Energy-E-Report-0029(EN) vs s ns n nu u vu
the growth of the length scale, agree better with the observations compared to the surface-layer wind profile, which under- and over-predicts the wind speed beyond the surface layer. The models also depend on the BLH, which is estimated under neutral and stable conditions using surface-layer turbulence measurements and under unstable conditions using ceilometer observations of the aerosol backscatter profile. Notation a parameter for the convective dimensionless wind shear A integration constant for a given stability from the resistant laws ABL atmospheric boundary layer AGL above ground level AMSL above mean sea level b parameter for the stable dimensionless wind shear B integration constant for a given stability from the resistant laws BLH boundary-layer height cw continuous wave C proportionality constant for the boundary-layer height d parameter for the control of the length scale D proportionality parameter for the limiting length scale fc Coriolis parameter g gravitational acceleration l local mixing length lMBL middle boundary-layer length scale lSL surface-layer mixing length L Obukhov length LST local standard time MOST Monin-Obukhov similarity theory NTWT National Test Station for Large Wind Turbines p parameter for the convective dimensionless wind shear Ro surface Rossby number SNR signal-to-noise ratio To mean surface-layer temperature u∗ local friction velocity u∗o surface-layer friction velocity u∗o average surface-layer friction velocity U horizontal mean wind speed w ′ Θv ′ o surface-layer kinematic virtual heat flux z height above the ground or above mean sea level zi boundary-layer height zo surface roughness length zo mean surface roughness length αc Charnock’s parameter β aerosol backscatter coefficient ∆u∗o fluctuating surface-layer friction velocity η limiting value for the length scale κ von Kármán constant φm dimensionless wind shear ψm diabatic correction of the wind profile ∂ partial derivative References Antoniou I., Jørgensen H. E., Mikkelsen T., Frandsen S., Barthelmie R., Perstrup C., and Hurtig M. (2006) Offshore wind profile measurements from remote sensing instruments. Proc. of the European Wind Energy Conf., Athens Blackadar A. K. (1962) The vertical distribution of wind and turbulent exchange in a neutral atmosphere. J. Geophys. Res. 67:3095–3102 Blackadar A. K. (1965) A Single-layer theory of the vertical distribution of wind in a baroclinic neutral atmospheric boundary layer. In: Flux of heat and momentum in the planetary boundary layer of the atmosphere. AFCRL-65-531, The Pennsylvania State University, 1–22 <strong>DTU</strong> Wind Energy-E-Report-0029(EN) 229
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Remote Sensing for Wind Energy DTU
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Author: Alfredo Peña, Charlotte B.
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4 Introduction to continuous-wave D
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8 Nacelle-based lidar systems 157 8
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12 Complex terrain and lidars 231 1
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1 Remote sensing of wind Torben Mik
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Figure 2: Calibration, laboratory w
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Figure 3: Example of scatter plots
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1.2.3 Summary of sodars Most of tod
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1.3.3 Wind lidars Measuring wind wi
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Figure 6: CW wind lidars (ZephIRs)
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Further developments Furthermore, n
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2 The atmospheric boundary layer S
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Figure 9: Large spatial scale varia
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Du3 Dt Du1 Dt Du2 Dt The three mome
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Figure 13: Consensus relations betw
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ψ z L ∼ − 5 L . For unstable c
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Figure 15: Behavior of the turbulen
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Figure 17: Newly developed models t
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The value of q0 at the surface is d
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the spray is the source of icing on
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u∗2 u∗1 u1(h) = u∗1 k ln h
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Figure 26:Land-seabreeze system,whe
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Figure28:Three dimensionalpicture o
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sufficient information. Finally, we
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Mann, J. (1998) Wind field simulati
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(2010) and comparison under differe
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To get the velocity field from the
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τ(k) [Arbitrary units] 10 3 10 2 1
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(Koopmans, 1974; Bendat and Piersol
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anemometer was installed at each en
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and fSw(f) u 2 ∗ = 1.05n 1+5.3n 5
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and n = 0.468. This spectrum implie
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to be calculated. We do that on a m
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Notation A Charnock constant neutra
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Maxey M. R. (1982) Distortion of tu
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4.2 Basic principles of lidar opera
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4.2.5 Wind profiling in conical sca
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4.3.1 Behaviour of scattering parti
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the beam radius at the output lens.
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from which the value of VLOS is der
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the atmosphere. The SNR 4 for a win
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individual line-of-sight wind speed
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A general approach to mitigating th
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from ±VH sinδ (if the tilt is tow
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as a down draught (of the same abso
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Table 7: Combined results from 28 Z
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in Eastern Jutland between January
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Figure 56: Normalized power curves
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the concept. Developments include i
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References x horizontal position in
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Wagner R., Mikkelsen T., and Courtn
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5.2 End-to-end description of pulse
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Scanner Coherent lidar measure the
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Figure 62: Radial wind velocity ret
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transform in order to use data obta
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This wavelength is also the most fa
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Eq.(132)isadaptedforcollimatedsyste
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5.3.6 Existing systems and actual p
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(Gottshall et al., 2010; Albers et
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References Albers A., Janssen A. W.
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derived from fluctuations of the wi
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Acoustic received echo (ARE) method
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Figure 71: Sample time-height cross
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A gradient minimum is characterized
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Figure73:Bragg-relatedacoustic(belo
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stability (inversion strength) can
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Figure 76: Combined soundingwith a
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Figure 79: Favorite regions (shaded
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Direct detection of MLH from acoust
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Engelbart D.A.M.and Bange J. (2002)
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7 What can remote sensing contribut
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uyms 9.0 8.5 8.0 7.5 7.0 120 140 16
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Bottom of rotor Φ rotation r w u
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Height Height Hub 1.6 1.4 1.2 1.0 0
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PP rated PP rated 1.0 0.8 0.6 0.4 0
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KEprofileKEhub 1.2 1.1 1.0 0.9 0.8
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PP rated WS Lidarms 1.0 0.8 0.6 0.4
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8 Nacelle-based lidar systems Andre
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• Flexibletrajectories.Dependingo
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Figure 104: Sketch of simultaneous
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The normal wind direction vector nw
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Figure 109: Test site at DTU Wind E
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Figure 112: Power curve met mast an
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Notation C number of sent photons C
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9 Lidars and wind turbine control -
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for three unknowns, it is impossibl
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model of the blade pitch actuator,
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Page 179 and 180: PSD(Ωg) [(rpm) 2 /Hz] PSD(Ωg) [
Page 181 and 182: 0.04 0.03 ˆk [ rad m ] 0.02 0.63 0
Page 183 and 184: [%] 10 0 −10 −20 −30 MyT Moop
Page 185 and 186: PSD(θ1) [rad 2 /Hz] PSD(Moop1) [Nm
Page 187 and 188: Pel/Pel,max [-] 1 0.98 0.96 0.94 0.
Page 189 and 190: fL weighting function GRL transfer
Page 191 and 192: E. Hau, Windkraftanlagen, 4th ed. S
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Page 195 and 196: vertical (m) 150 100 50 R d 0
Page 197 and 198: With feedback only, on the other ha
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Page 201 and 202: Normalized C r = C r P Q 2 W (r) b
Page 203 and 204: Normalized C r = C r P Q 2 W (r) b
Page 205 and 206: Coherence 1 0.8 0.6 0.4 0.2 0 10
Page 207 and 208: Coherence 1 0.8 0.6 0.4 0.2 0 10
Page 209 and 210: Magnitude Squared 10 8 10 7 10 6 10
Page 211 and 212: Figure 148: During simulation, FAST
Page 213 and 214: Figure 149: Collective flap respons
Page 215 and 216: Magnitude (abs) blade pitch gen spe
Page 217 and 218: • Measurement coherence, which ca
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Page 221 and 222: 11 Lidars and wind profiles Alfredo
Page 223 and 224: z [m] 160 100 80 60 40 20 10 15 20
Page 225 and 226: z [−] zo 1 κ ln 40 38 36 34 32 3
Page 227: z [m] z [m] 1000 900 800 700 600 50
Page 231 and 232: 12 Complex terrain and lidars Ferha
Page 233 and 234: Figure 158: The ZephIR models which
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Page 237 and 238: Figure 162: Lavrio: The scatter plo
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Page 241 and 242: References Albers A. and Janssen A.
Page 243 and 244: Wexler (1968), where the limitation
Page 245 and 246: 13.2.1 Systematic turbulence errors
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Page 249 and 250: The theoretical systematic errors a
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Page 253 and 254: Height (m) Height (m) 160 140 120 1
Page 255 and 256: RMSPE (%) 70 60 50 40 30 20 10 0 vu
Page 257 and 258: involves interaction of all compone
Page 259 and 260: Lindelöw P. (2007) Fibre Based Coh
Page 261 and 262: plications, including meteorologica
Page 263 and 264: Figure 173: Rayleigh-Jeans’ appro
Page 265 and 266: Figure 176: Brightness temperature
Page 267 and 268: with rain are shown in Fig. 179. A
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Page 275 and 276: References s point in space Sν(s)
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Christiansen et al., 2006). The res
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educe speckle noise, a random noise
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Figure 188: Envisat ASAR wind field
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Figure 190: Envisat ASAR wind field
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Satellites in sun-synchronous polar
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500 overlapping scenes for wind res
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(with the fewest samples). The unce
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Badger M., Hasager C. B., Thompson
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Ren Y. Z., Lehner S., Brusch S., Li
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tracking, climate studies, air-sea
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The modified logarithmic wind profi
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sea ice mask was applied and σ0 me
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QuikSCAT 25 20 15 10 5 Horns Rev Fi
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Figure 203:Example of an ASCAT coas
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References Bourassa M.A., Legler D.
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DTU Wind Energy Technical Universit
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