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Businger J. A.,WyngaardJ.C.,IzumiY.,andBradley E. F.(1971) Flux-profile relationships inthe atmospheric surface layer. J. Atmos. Sci. 28:181–189 Caughey S. J. and Palmer S. G. (1979) Some aspects of turbulence structure through the depth of the convective boundary layer. Quart. J. Roy. Meteor. Soc. 105:811–827 Charnock H. (1955) Wind stress over a water surface. Quart. J. Roy. Meteorol. Soc. 81:639–640 Floors R., Vincent C. L., Gryning S.-E., Peña A., and Batchvarova E. (2013) The Wind profile in the coastal boundary layer: wind lidar measurements and numerical modelling Bound.-Layer Meteorol. in press Gryning S.-E., Batchvarova E., Brümmer B., Jørgensen H., and Larsen S. (2007) On the extension of the wind profile over homogeneous terrain beyond the surface layer. Bound.-Layer Meteorol. 124:251–268 Högström U. (1988) Non-dimensional wind and temperature profiles in the atmospheric surface layer: a reevaluation. Bound.-Layer Meteorol. 42:55–78 IEC (2005) IEC 61400-12-1 Wind turbines - Design requirements. Int. Electrotechnical Commission Kindler D., Oldroyd A., MacAskill A., and Finch D. (2007) An eight month test campaign of the QinetiQ ZephIR system: Preliminary results. Meteorol. Z. 16:479–489 Lettau H. H. (1962) Theoretical wind spirals in the boundary layer of a barotropic atmosphere. Beitr. Phys. Atmos. 35:195–212 Monin A. S. and Obukhov A. M. (1954) Osnovnye zakonomernosti turbulentnogo peremeshivanija v prizemnom sloe atmosfery (Basic laws of turbulent mixing in the atmosphere near the ground). Trudy Geofiz. Inst. AN SSSR 24(151):163–187 Panofsky H. A. (1973) Tower Micrometeorogy. Haugen D. A. (Ed.) Workshop on Micrometeorolgy. American Meteorology Society, 151–176 Peña A. and Gryning S.-E. (2008) Charnock’s roughness length model and non-dimensional wind profiles over the sea. Bound.-Layer Meteorol. 128:191–203 Peña A., Gryning S.-E., and Hasager C. B. (2008) Measurements and modelling of the wind speed profile in the marine atmospheric boundary layer. Bound.-Layer Meteorol. 129:479–495 Peña A., Gryning S.-E., and Hasager C. B. (2010a) Comparing mixing-length models of the diabatic wind profile over homogeneous terrain. Theor. Appl. Climatol. 100:325–335 Peña A., Gryning S.-E., Mann J., and Hasager C. B. (2010b) Length scales of the neutral wind profile over homogeneous terrain. J. Appl. Meteorol. Climatol. 49:792–806 Peña A., Hasager C. B., Gryning S.-E., Courtney M., Antoniou I., and Mikkelsen T. (2009) Offshore wind profiling using light detection and ranging measurements. Wind Energy 12:105–124 Peña A., Mikkelsen T., Gryning S.-E., Hasager C. B., Hahmann A. N., Badger M., Karagali I., and Courtney M. (2012) Offshore vertical wind shear. Final report on NORSEWInD’s work task 3.1, DTU Wind Energy- E-Report-005(EN), DTU Wind Energy, Risø Campus, Roskilde Peña A., Gryning S.-E., and Hahmann A. N. (2013) Observations of the atmospheric boundary layer height under marine upstream flow conditions at a coastal site. J. Geophys. Res.: Atmosp. 118:1924–1940 Prandtl L. (1932) Meteorologische Anwendung der Strömungslehre (Meteorological application of fluid mechanics). Beitr. Phys. Atmos 19:188–202 Rossby C. G. and Montgomery R. B. (1935) The layers of frictional influence in wind and ocean currents. Pap. Phys. Oceanogr. Meteorol. 3(3):101 pp Smith D. A., Harris M., Coffey A. S., Mikkelsen T., Jørgensen H. E., Mann J., and Danielian R. (2006) Wind lidar evaluation at the Danish wind test site in Høvsøre. Wind Energy 9:87–93 Steyn D. G., Baldi M., and Hoff R. M. (1999) The detection of mixed layer depth and entrainment zone thickness from lidar backscatter profiles. J. Atmos. Ocean. Technol. 16:953–959 Stull R. B. (1988) An introduction to boundary layer meteorology, Kluwer Academic Publishers, 666 pp 230 DTU Wind Energy-E-Report-0029(EN)
12 Complex terrain and lidars Ferhat Bingöl DTU Wind Energy, Risø Campus, Roskilde, Denmark 12.1 Introduction The term “complex terrain” can be simply defined as any site where the wind is under effect of the terrain. This general definition includes landscapes with either vegetation or sudden elevation changes. In recent years, the interest of the European wind energy industry for such sites has increased. Formerly, they were considered as suboptimal for investments. This is not a coincidence and there are many reasons for such interest; most importantly the following two. Firstly, most of the suitable flat terrains have already been used. One example to this case is Northern Europe where the installed capacity is reaching its limit on flat terrain and the investors became more interested in complex sites. Secondly, the market is also growing in regions where wind resources are not fully utilized, like Mediterranean countries, where the land surface is dominated by rough terrain in the form of hills, mountains and forests. In both cases, the terrain poses a challenge for flow modelling because the assumptions of classical boundary-layer theory are violated which which has a great impact on the site assessment. If one has to identify the wind conditions in complex terrain, knowledge beyond the classical site assessment methods would be needed. Hence, procedures are needed for the verification of the power curve for wind turbines erected in complex terrain because the power curve variation is 6−8%, higher compared to that measured over flat terrain (Pedersen et al., 2002). Therefore, current site assessment techniques are not generally reliable in such conditions, which may lead to reduced turbine/wind park life-time and loss of investment. In addition to land cover and elevation complexity challenges in the terrain, the wind industry faces another equally important challenge related to the size of the wind turbines. In the last decade, the turbine hub heights have doubled, reaching a minimum of 100 m with 100 m of rotor diameter. The top and bottom edges of the blade of such turbines are typically at 150 and 50 m above ground level (a.g.l.), respectively. This multitudeoffactors has created the need foranew generationof measurement devices with certain capabilities. The instruments; 1. should be able to measure up to 200 m to cover the whole rotor swept area, 2. must be able to perform in profile measurement standards (e.g. IEC (2005)), 3. and be easy to install/operate in complex terrains. The above requirements cannot easily be fulfilled with conventional meteorological masts; installation of a meteorological mast and its maintenance, is a big logistical problem. Furthermore minor adjustments on the position of the meteorological mast entails almost the same amount of work as installing it. A category of instruments which can meet these goals is the wind energy Light Detection and Ranging instruments; mostly known as wind lidars or just lidars in wind energy. In this chapter, we will discuss on using the lidars in complex terrain. 12.2 Lidars Thelidarshavebecomeapartofwindenergymeteorologyafter1997(Mayoretal.,1997).The capabilities of the instrument were well-known but the necessary investment was too high for many applications and the operating heights were not relevant to wind energy related studies. Therefore, the usage of them is recent and it has started after the “wind energy lidars” are developed (Jørgensen et al., 2004). DTU Wind Energy-E-Report-0029(EN) 231
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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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|GRL| [-] 1 0.8 0.6 0.4 0.2 10 k [r
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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
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Page 199 and 200: Figure 136: Estimated preview requi
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
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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 and 228: z [m] z [m] 1000 900 800 700 600 50
Page 229: the growth of the length scale, agr
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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
Page 251 and 252: Height (m) 160 140 120 100 80 60 40
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
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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 271 and 272: Figure 183: A recent maintenance in
Page 273 and 274: Figure 185: Temperature profiles in
Page 275 and 276: References s point in space Sν(s)
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Page 279 and 280: 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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