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Table 14: Overview of methods using ground-based remote sensing for the derivation of the mixing-layer height mentioned in this chapter (see right most column to find its placement in section 6.2). method short description which section? acoustic ARE analysis of acoustic 6.2.1 backscatter intensity acoustic HWS analysis of wind 6.2.1 speed profiles acoustic VWV analysis of vertical 6.2.1 wind variance profiles acoustic EARE analysis of backscatter and 6.2.1 vertical wind variance profiles optical threshold detection of a given 6.2.2 backscatter intensity threshold optical gradient analysis of backscatter 6.2.2 intensity profiles optical idealized backscatter analysis of backscatter 6.2.2 intensity profiles optical wavelet analysis of backscatter 6.2.2 intensity profiles optical variance analysis of backscatter 6.2.2 intensity profiles acoustic/electro-magnetic RASS 6.2.3 acoustic/electro-magnetic sodar-RASS and 6.2.3 windprofiler-RASS acoustic/electro-magnetic/in situ sodar-RASS plus 6.2.3 surface heat flux data acoustic/electro-magnetic sodar plus windprofiler 6.2.4 acoustic/optical sodar plus ceilometer 6.2.4 Figure 70: Sample time-height cross-sectionfrom acoustic soundingwith a sodar. Left: acoustic backscatter intensity, right: horizontal wind direction. Thin black lines demark inversions. valleyair overasnow-coveredvalley floor.The multiplelayering originatedfrom an interlacing of down-valley (wind direction around 190 ◦ ) and down-slope (wind direction around 230 ◦ ) flows. The layers are separated by temperature inversions and each higher layer is potentially warmer than the next lower layer. They persisted nearly the whole day because no vertical mixing took place in the stably stratified valley atmosphere. 124 DTU Wind Energy-E-Report-0029(EN)
Acoustic received echo (ARE) method The ARE method is the most basic method of determining MLH from acousticremote sensing.Most ofthe methods listed in Beyrich (1997) belong to this method. The method does not require an analysis of the Doppler shift of the backscattered signals. The method makes use of the assumption that turbulence is larger in the mixing layer than in the atmosphere above, and that this turbulence is depicted in the intensity of the acoustic backscatter. MLH is analyzed either from the maximum negative slopeorfromthechangingcurvatureoftheverticalprofileoftheacousticbackscatterintensity or it is analyzed from the height where the backscatter intensity decreases below a certain pre-specified threshold value. Horizontal wind speed (HWS) method The HWS method requires the analysis of the Doppler shift of the backscattered acoustic signals. The algorithm is based on the analysis of the shape of hourly-averaged vertical wind speed profiles using the assumption that wind speed and wind direction are almost constant within the mixing layer but approach gradually towards the geostrophic values above the mixing layer. Beyrich (1997) listed this method in his Tab. 2 but did not discuss it further. The applicability of the method is probably limited to the well-developed convective boundary layers (CBL) due to the underlying assumptions. Such CBLs are often higher than the maximum range of a sodar. Even if the CBL height is within the range of the sodar the algorithm for the analysis of the Doppler shift often fails above the inversion topping of the CBL due to too low signal-to-noise ratios. Today, small Dopplerwind lidars are available to derive wind speed and direction profiles through the whole depth of the boundary layer. This facilitates the application of the HWS method. Vertical wind variance (VWV) method The VWV method is also working only for CBLs. It is based on the vertical profile of the variance of the vertical velocity component σw. In a CBL σw reaches a maximum in a height azi. Typical values for a are between 0.35 and 0.40. Thus, in principle, this is an extrapolation method. It has been tried for sodar measurements because it permits a detection of MLH up to heights which are 2.5 times above the limited maximum range (usually between 500 and 1000 m) of the sodar. Beyrich (1997) classified this method as not reliable. A related method, which is based on power spectra of the vertical velocity component, is integrated in the commercial evaluation software of certain sodars (Contini et al., 2009). The application of the VWV method is now also been facilitated by the easy availability of small Doppler wind lidars. Enhanced acoustic received echo (EARE) method The EARE algorithm has been proposed by Emeis and Türk (2004) and Emeis et al. (2007). The method is an enhancement of the ARE method in twoways.Firstly, it includesfurther variables into the MLH algorithmthat are available from Doppler-sodars. The benefits of the additional usage of the variance of the vertical velocity component have been demonstrated by Emeis and Türk (2004). Secondly, it determines not only MLH from sodar measurements but also the heights of additional lifted inversions. Especially in orographically complex terrain, the vertical structure of the ABL can be very complicated. Emeis et al. (2007) have shown that several persistent inversions one above the other which form in deep Alpine valleys can be detected from sodar measurements (Fig. 70). EARE determines three different types of heights based on acoustic backscatter intensity and the variance of the vertical velocity component. Because the horizontal wind information above the inversion is not regularly available from sodar measurements, horizontal wind data have not been included into this scheme. In the following a letter “H” and an attached number will denote certain derived heights which are related to inversions and the MLH; while the variable z is used to denote the normal vertical coordinate. The EARE algorithm detects: • the height(H1) ofaturbulentlayercharacterisedbyhighacousticbackscatterintensities R(z) due to thermal fluctuations(therefore having a highvariance ofthe vertical velocity component σw), DTU Wind Energy-E-Report-0029(EN) 125
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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
Page 73 and 74: 4.2 Basic principles of lidar opera
Page 75 and 76: 4.2.5 Wind profiling in conical sca
Page 77 and 78: 4.3.1 Behaviour of scattering parti
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Page 81 and 82: from which the value of VLOS is der
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Page 87 and 88: A general approach to mitigating th
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Page 93 and 94: Table 7: Combined results from 28 Z
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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: derived from fluctuations of the wi
Page 127 and 128: Figure 71: Sample time-height cross
Page 129 and 130: A gradient minimum is characterized
Page 131 and 132: Figure73:Bragg-relatedacoustic(belo
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
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model of the blade pitch actuator,
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|GRL| [-] 1 0.8 0.6 0.4 0.2 10 k [r
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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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