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Figure 72: Sample time-height cross-section from a potential temperature sounding with RASS. However, problems with this method arise e.g. in case of multiple (well defined) aerosol layers, which renders the selection of the correct mixing layer top ambiguous. Furthermore, in spring and summer the detection of the MLH for deep (convective) boundary layers often fails. This is mostly due to the high variability of the aerosol backscatter signal with height which limits the range for MLH estimation in those conditions (de Haij et al., 2006). Variance method At the top of the CBL we have entrainment of clear air masses from the free troposphere into the ABL. The entrainment process is temporarily variable and leads locally to considerable fluctuations in the aerosol concentration. Therefore the maximum in the vertical profile of the variance of the optical backscatter intensity can be an indicator for an entrainment layer on top of a CBL (Hooper and Eloranta, 1986; Piironen and Eloranta, 1995). The method is called variance centroid method in Menut et al. (1999). The variance method for the CBL height is also described in Lammert and Bösenberg (2006). Due to the assumptions made this method is suitable for daytime convective boundary layers only. An elucidating comparison between the gradient method and the variance method can be found in Martucci et al. (2004) although they used a Nd:YAG LIDAR at 532 nm instead of a ceilometer and thus suffered from a high lowest range gate in the order of 300 m. 6.2.3 RASS The acoustic and optical methods for MLH determination, which have been described in the sections above, are all indirect methods that try to infer the mixing-layer height from other variables which usually adapt to the vertical structure of the ABL. The only direct and key variable for the analysis of the presence of a mixing layer is the vertical profile of virtual temperature. Temperature profiles can directly be measured with a radio-acoustic sounding system (RASS). Fig. 72 shows an example. We start here with a short description of the available RASS methods. Instrumentation A radio-acoustic sounding system (RASS) operates acoustic and electromagnetic sounding simultaneously (Marshall et al., 1972). This instrument is able to detect acousticshockfronts oftheacousticpulsesandtodetermine theirpropagationspeedfrom the Doppler shift of the backscattered electro-magnetic waves. This propagation speed is equal to the speed of sound which in turn is a known function of air temperature and humidity. To different types of RASS have been realised (Engelbart and Bange, 2002): a Bragg-RASS and 130 <strong>DTU</strong> Wind Energy-E-Report-0029(EN)
Figure73:Bragg-relatedacoustic(below)andelectro-magnetic(above)frequenciesforRASS. a Doppler-RASS. Bragg-(windprofiler) RASS A Bragg-RASS (or windprofiler-RASS) is basically a windprofiler with an additional acoustic emitter. When the Bragg condition is fulfilled (Fig. 73), i.e. the wavelength of the sound waves λa is half the one of the electro-magnetic waves λe, then there is optimal backscatter of the electro-magnetic waves from the acoustic waves (Fig. 74). The electro-magnetic signal is emitted at a fixed frequency, but the emitted sound signal is a chirp signal with varying frequency fa. From the sound wave length λa,B at which optimal backscatter occurs the propagation speed of the sound signal can be determined via the following dispersion relation: ca = λa,Bfa/2. (151) For a VHF windprofiler operating at 50 MHz a sound frequency of about 100 Hz is used, for a UHF windprofiler operating at 1 GHz a sound frequency around 2 kHz is most suitable to fulfil the Bragg condition. Because the attenuation of sound waves in the atmosphere is strongly frequency dependent, a UHF RASS can detect temperature profiles up to about 1.5 kmheightwhereasaVHFRASScanobservetemperatureprofilesthroughoutthetroposphere. Doppler-(sodar) RASS A Doppler-RASS (or sodar-RASS) is a sodar with an additional electro-magnetic emitter and receiver (Fig. 75) operating at a frequency fe,0. From the Doppler shift ∆fe of the electro-magnetic radiation which is backscattered at the density fluctuations caused by the sound waves the propagation speed ca of the sound waves is determined: ca = −c∆fe/(2fe,0) (152) where c denotes the speed of light. A Doppler-RASS like a Bragg-RASS also emits a chirp sound signal in order to assure that the Bragg condition is optimally met due to the varying temperature over the entire height range. The so determined propagation speed ca is a sum of the speed of sound cs and of the vertical movement of the air w within which the sound waves propagate: ca = cs +w. (153) <strong>DTU</strong> Wind Energy-E-Report-0029(EN) 131
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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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Page 81 and 82: from which the value of VLOS is der
Page 83 and 84: the atmosphere. The SNR 4 for a win
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Page 87 and 88: A general approach to mitigating th
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Page 91 and 92: as a down draught (of the same abso
Page 93 and 94: Table 7: Combined results from 28 Z
Page 95 and 96: in Eastern Jutland between January
Page 97 and 98: Figure 56: Normalized power curves
Page 99 and 100: the concept. Developments include i
Page 101 and 102: References x horizontal position in
Page 103 and 104: Wagner R., Mikkelsen T., and Courtn
Page 105 and 106: 5.2 End-to-end description of pulse
Page 107 and 108: Scanner Coherent lidar measure the
Page 109 and 110: Figure 62: Radial wind velocity ret
Page 111 and 112: transform in order to use data obta
Page 113 and 114: This wavelength is also the most fa
Page 115 and 116: Eq.(132)isadaptedforcollimatedsyste
Page 117 and 118: 5.3.6 Existing systems and actual p
Page 119 and 120: (Gottshall et al., 2010; Albers et
Page 121 and 122: References Albers A., Janssen A. W.
Page 123 and 124: derived from fluctuations of the wi
Page 125 and 126: Acoustic received echo (ARE) method
Page 127 and 128: Figure 71: Sample time-height cross
Page 129: A gradient minimum is characterized
Page 133 and 134: stability (inversion strength) can
Page 135 and 136: Figure 76: Combined soundingwith a
Page 137 and 138: Figure 79: Favorite regions (shaded
Page 139 and 140: Direct detection of MLH from acoust
Page 141 and 142: Engelbart D.A.M.and Bange J. (2002)
Page 143 and 144: 7 What can remote sensing contribut
Page 145 and 146: uyms 9.0 8.5 8.0 7.5 7.0 120 140 16
Page 147 and 148: Bottom of rotor Φ rotation r w u
Page 149 and 150: Height Height Hub 1.6 1.4 1.2 1.0 0
Page 151 and 152: PP rated PP rated 1.0 0.8 0.6 0.4 0
Page 153 and 154: KEprofileKEhub 1.2 1.1 1.0 0.9 0.8
Page 155 and 156: PP rated WS Lidarms 1.0 0.8 0.6 0.4
Page 157 and 158: 8 Nacelle-based lidar systems Andre
Page 159 and 160: • Flexibletrajectories.Dependingo
Page 161 and 162: Figure 104: Sketch of simultaneous
Page 163 and 164: The normal wind direction vector nw
Page 165 and 166: Figure 109: Test site at DTU Wind E
Page 167 and 168: Figure 112: Power curve met mast an
Page 169 and 170: Notation C number of sent photons C
Page 171 and 172: 9 Lidars and wind turbine control -
Page 173 and 174: for three unknowns, it is impossibl
Page 175 and 176: model of the blade pitch actuator,
Page 177 and 178: |GRL| [-] 1 0.8 0.6 0.4 0.2 10 k [r
Page 179 and 180: PSD(Ωg) [(rpm) 2 /Hz] PSD(Ωg) [
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0.04 0.03 ˆk [ rad m ] 0.02 0.63 0
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[%] 10 0 −10 −20 −30 MyT Moop
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PSD(θ1) [rad 2 /Hz] PSD(Moop1) [Nm
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Pel/Pel,max [-] 1 0.98 0.96 0.94 0.
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fL weighting function GRL transfer
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E. Hau, Windkraftanlagen, 4th ed. S
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¨¦¦§©¡§ ¥§¨¦¦§£ ¡¥
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vertical (m) 150 100 50 R d 0
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With feedback only, on the other ha
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Figure 136: Estimated preview requi
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Normalized C r = C r P Q 2 W (r) b
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Normalized C r = C r P Q 2 W (r) b
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Coherence 1 0.8 0.6 0.4 0.2 0 10
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Coherence 1 0.8 0.6 0.4 0.2 0 10
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Magnitude Squared 10 8 10 7 10 6 10
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Figure 148: During simulation, FAST
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Figure 149: Collective flap respons
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Magnitude (abs) blade pitch gen spe
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• Measurement coherence, which ca
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Jonkman, B. (2009) TurbSim user’s
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11 Lidars and wind profiles Alfredo
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z [m] 160 100 80 60 40 20 10 15 20
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z [−] zo 1 κ ln 40 38 36 34 32 3
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z [m] z [m] 1000 900 800 700 600 50
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the growth of the length scale, agr
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12 Complex terrain and lidars Ferha
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Figure 158: The ZephIR models which
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Uconst wΑx l h Φ h.tanΦ Figure 1
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Figure 162: Lavrio: The scatter plo
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Figure 163: Panahaiko: The scatter
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References Albers A. and Janssen A.
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Wexler (1968), where the limitation
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13.2.1 Systematic turbulence errors
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where x is the center of the scanni
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The theoretical systematic errors a
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Height (m) 160 140 120 100 80 60 40
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Height (m) Height (m) 160 140 120 1
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RMSPE (%) 70 60 50 40 30 20 10 0 vu
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involves interaction of all compone
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Lindelöw P. (2007) Fibre Based Coh
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plications, including meteorologica
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Figure 173: Rayleigh-Jeans’ appro
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Figure 176: Brightness temperature
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with rain are shown in Fig. 179. A
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This method gives a good accuracy (
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Figure 183: A recent maintenance in
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Figure 185: Temperature profiles in
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References s point in space Sν(s)
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Also the Doppler Centroid anomaly c
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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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